Advancing the Epistemology of Intelligence Analysis: a Polanyian Perspective

Advancing the epistemology of intelligence analysis: A Polanyian perspective

Owen Ormerod, BA (Hons), BCrim

Submitted in fulfilment of the requirements for the degree of

Master of Arts

Deakin University

April 2018

ACKNOWLEDGEMENTS First and foremost I would like to thank my wife Jessica. Her love and support made this journey possible. She helped me stoke the fire and kept my passion burning for this project. It is a blessing to have Jessica in my life, as we navigate together through the hurdles we encounter. It is a privilege to share this achievement with her. This could not have been possible without her.

I would like to thank Dr Chad Whelan, my principal supervisor. His brutal honesty and practical advice kept me from going too far down the rabbit hole.

The assistance provided by Dr Struan Jacobs, particularly around understanding the ideas of Michael Polanyi, and being so generous with his time with me, was greatly appreciated. I would also like to thank Dr Darren Palmer for his practical advice on the thesis.

Thank you to Dr Floriana Badalotti, from Arte Lingua, for her assistance with the professional editing of this thesis.

My family have been of great assistance and moral support throughout this project. They have helped me stay focused and achieve this important goal. Their love and care helped sustain me.

Table of Contents

ACKNOWLEDGEMENTS ...... i

LIST OF FIGURES ...... iii

LIST OF APPENDICES ...... iii

ABBREVIATIONS ...... iv

ABSTRACT ...... v

Introduction ...... 1 Method and approach ...... 8

Chapter 1: The ‘Art’ and ‘Science’ of intelligence analysis ...... 14 Introduction ...... 14 Understanding the intelligence field...... 14 The intelligence cycle ...... 27 Intelligence analysis as ‘Art’ ...... 29 Intelligence analysis as ‘Science’ ...... 31 Education and training ...... 34 Conclusion ...... 38

Chapter 2: Exposition of Polanyi ...... 40 Introduction ...... 40 Discovery as the aim of scientific research ...... 42 No overarching ‘scientific method’ ...... 47 Tacit knowing ...... 50 Practical knowledge and skilful practice ...... 54 Tacit knowing and learning ...... 59 Personal knowledge ...... 63 Conclusion ...... 67

Chapter 3: Tacit knowing ...... 69 Introduction ...... 69 Tacit knowing and the use of tools ...... 70 SATs: an outline ...... 75 Tools, objectivity and bias ...... 79

Skilful performance ...... 82 Destructive analysis ...... 88 Conclusion ...... 94

Chapter 4: Personal knowledge ...... 97 Introduction ...... 97 The School of Athens – Ways of Knowing ...... 98 Genuine knowledge and belief ...... 107 Tradecraft: training and education ...... 114 Conclusion ...... 123

Conclusion ...... 125

Reference list ...... 133

LIST OF FIGURES Figure 1: The Intelligence Cycle ...... 27 Figure 2: The School of Athens ...... 100

LIST OF APPENDICES Appendix 1: Email from Glenn Carle……………………………………….167

Appendix 2: Email from Philip Mudd…………………………………….....170

iii

ABBREVIATIONS ACH Analysis of Competing Hypotheses

CIA Central Intelligence Agency

FBI Federal Bureau of Investigation

IC Intelligence Communities

KM Knowledge Management

SATs Structured Analytic Techniques

NSA National Security Agency

ODNI Office of the Director of National Intelligence

ONA Office of National Assessments

UK United Kingdom

UNODC United Nations Office on Drugs and Crime

WMD Weapons of Mass Destruction

ABSTRACT Epistemological considerations are at the heart of both understanding and developing a deeper perception of the tradecraft of intelligence analysis as a field of study and its practice. Ranging from the intelligence fields of national security to law enforcement, intelligence analysis as a practice and area of study engages a series of fundamental questions, covering what we know, how we know such things and the reliability of knowledge claims. This thesis situates itself within the sociology of knowledge with respect to the discourse on the discipline of intelligence analysis.

The primary research question guiding this thesis is: How can Michael Polanyi’s views of science contribute towards understanding the ‘process’ and ‘product’ of intelligence analysis? As revealed by intelligence literature, a clear and persistent aim for the transformation of intelligence analysis as a discipline has been to align the field with scientific principles and practices. Within intelligence literature, there are noticeable efforts within some quarters to align intelligence analysis, most observably within the US Intelligence Community, with generally accepted principles of science by requiring analysts to engage in more rigorous standards and systematic processes. Such efforts aim towards generating more accurate and reliable intelligence assessments, or for better ‘connecting the dots’. Michael Polanyi’s arguments regarding the activities of scientists are transferable to the field of intelligence analysis, providing a nuanced perspective for perceiving epistemological challenges and issues facing analysts. Specifically, Polanyi’s concepts of ‘tacit knowing’ and ‘personal knowledge’ contributes towards developing a more epistemologically robust and complete understanding of some aspects of the process and the product of intelligence analysis.

Keywords:

Intelligence analysis, science, Michael Polanyi, tacit, knowledge, epistemology

Introduction

Discovery consists of seeing what everyone else has seen and thinking what nobody has thought. Albert Szent-Györgyi, 1937 Nobel Prize scientist (cited in Lathrop 2008)

The primary research question guiding this thesis is the following: How can Michael Polanyi’s views of science contribute towards understanding the ‘process’ and ‘product’ of intelligence analysis? This question emerged from observations regarding the discernible trend, both within the national security and law enforcement literature, of attempts to align intelligence analysis to ‘scientific’ principles and practices (Cooper 2005; Gill 2009; Innes, Fielding & Cope 2005; Marrin 2011b; Marrin & Clemente 2005, 2006; Spielmann 2014; Wirtz 2014a). With respect to the goal in the intelligence field to become more ‘scientific’, this thesis argues Polanyi’s view of science can shed important light on the matter; specifically, his understanding of how problems are solved within science, involving the practitioner’s ‘tacit knowing’ and practical ‘craft skills’. The thesis argues that these insights of Polanyi are transferrable to the intelligence field. Similarly, understanding what it means to ‘know’ something as an intelligence product can be enhanced by Polanyi’s concept of the knower as using his/her ‘personal knowledge’. Polanyi’s understanding of the nature of science, covering the ‘tacit’ processes involved in solving problems and what it means to ‘know’ something by acknowledging the ‘personal knowledge’ of the practitioner, provides us with a more nuanced way to understand intelligence analysis. Polanyi’s views offer a perspective for better understanding fundamental epistemological issues facing intelligence analysts, specifically considerations regarding the tacit processes involved in solving problems and understanding the personal dimension of knowledge claims. The aim of this thesis is to utilise Polanyi’s depictions of ‘personal knowledge’, ‘tacit knowing’ and craft skill within the discourse of intelligence analysis in order to contribute towards a more epistemologically robust account of the analytical processes and products of intelligence analysis.

Closely related to the primary research question are the questions, ‘What is the nature of science?’ and ‘What is the nature of intelligence analysis?’ We need to understand what constitutes a science, and this is no simple matter since science has been defined in many different ways (Mehlberg 1955; Moore 2007; Sokal 2001). One of the more influential definitions in recent times is that of Karl Popper in terms of scientific theories being falsifiable, and the method of science (the method of assessing scientific theories) featuring criticism (Popper 1959). The approach taken in this thesis to the question ‘What is the nature of science?’ will rely on the writings of Polanyi. Besides exercising considerable influence on philosophers and sociologists of science, Polanyi has the merit of having known science with great intimacy. He was an accomplished scientific researcher, working alongside Einstein and others in Berlin in the 1920s and the 1930s.

Moving beyond ways of understanding science, the next task to explore is the nature of intelligence analysis. Polanyi’s perspective of science, including his views around how discoveries are made, contributes toward understanding certain aspects of intelligence analysis. In particular, Polanyi’s concepts of tacit and personal knowledge have a strong bearing on perceiving the practice of intelligence analysis. Given there has been a rekindling of interest in a search for a ‘theory of intelligence’ (Hunter & MacDonald 2017; Johnson 2017; Marrin 2007b, pp. 821-846; Phythian 2013, p. 54; Treverton & Gabbard 2008; Warner 2017; Zohar 2013) one can expect epistemological issues to come increasingly to the fore around intelligence analysis (Lillbacka 2013, p. 304). Key epistemological considerations will be explored in this thesis from a standpoint grounded in Polanyi’s writings, including the question, ‘What exactly is ‘knowledge?’ This is discussed with reference to intelligence analysis. The study of Polanyi’s views regarding epistemological issues in relation to science will help us better understand related issues in intelligence analysis. A number of scholars have agreed with Polanyi that tacit knowledge is an essential part of science (Beckett 2000; Gilbert 2000; Piccini & Kershaw 2004; Schön 1995), while others regard the whole idea of tacit knowledge as

being irrational mysticism (Popper 1959, p. 8). Polanyi explored the subject of tacit knowledge in great depth.

The dynamics of the contemporary information environment has an impact on the activities of analysts. The problems associated with this environment is most clearly visible with respect to the ‘information overload’ facing modern intelligence analysts – a phenomenon which has emerged as a result of the expansion of ‘open source’ material (unclassified and freely available information in the public domain) (Flood 2004, p. 108; Friedman, RS 1998, p. 161; Gaudet, J 2013, p. 169; Zegart, AB 2009).

It is important to acknowledge that there are some limitations with Polanyi’s experience and understanding of science. His perspective of science itself is not universally agreed on by scientists or scholars in this area. For example, Polanyi disagreed with the Positivist perspective of science and the idea of a ‘scientific method’. There are some who have challenged these positions for characterising science in that way (Kelleher 2008/2009, p. 8; Creswell & Poth 2017; Johnson, J 2006a, p. 224; Mackenzie, JIM 2011, pp. 543-46; Manjikian, M 2013, pp. 563-67; Ryan, P 2015, pp. 417-31). It is beyond the scope of this thesis to examine these criticisms in-depth, but it is nonetheless important to acknowledge that Polanyi’s perspective of science is not necessarily the definitive ‘truth’ for understanding this field and knowledge more generally.

This thesis shows that Polanyi’s account of tacit knowledge and related issues has direct relevance to understanding the activities of analysts, including the nature of their knowledge claims, whether those claims are objective, and tradecraft in the processes of intelligence analysis.

Agreeing with commentators who argue that at its core, intelligence analysis is a discipline concerned with thinking and knowledge claims (Bruce & George 2008; Lillbacka 2013; Marrin 2017; Mole 2012; Waltz 2014, p. 1; Wheaton & Richey 2015), this thesis is largely concerned with matters epistemological. Specifically, it concentrates on ‘intelligence

analysis’ in terms of better understanding the processes involved in solving problems in this discipline and comprehending the knowledge claims or final intelligence products (i.e. the knowledge claims that analysts produce at the end of their analysis). Polanyi’s contribution to this area is his insights into the practice of science. For example, his discussions regarding the art/science aspects of craftsmanship involved in making discoveries. Other scholars have examined these matters, including Harry Collins and Richard Evan’s treatment around expertise (Collins and Evans, 2008), Collins’ work on tacit and explicit knowledge (Collins, 2010), and Richard Sennet’s examination of ‘craft’ (Sennett, 2008). Polanyi’s engagement in these topics is arguably more detailed and offers sufficient insights.

Intelligence analysis is a knowledge-building activity, and to improve analysis requires an understanding of epistemology or the theory of the origin and nature of relevant knowledge (Bruce 2008, p. 171). This thesis provides a deeper understanding of the epistemological nature of intelligence analysis by examining and analysing salient features of Polanyi’s view of science. Using Polanyi to sharpen our understanding of the epistemology of intelligence analysis will generate a new way of envisaging the discipline of intelligence analysis.

A number of scholars have observed from a psychological point of view the importance of cognitive awareness in helping people avoid errors in processes of reasoning (Bar-Joseph 2010; Bruce 2008; Heuer 1999). However, comparatively little research has been conducted into the theoretical nature of analysis from a more philosophical, or specifically epistemological, standpoint (Vrist Rønn 2014). Scholars such as Vrist Rønn and Høffding (2012) rightly complain that epistemological contributions to intelligence theory are ‘still alarmingly low in number and thoroughness’, stating:

Even though fundamental questions from the domain of epistemology are often posed in the field of intelligence (e.g. ‘how do we arrive at new knowledge?’ and how robust are our conclusions related to the evidence at hand?), there are, to our knowledge, very few attempts to

qualify these questions from the perspective of professional epistemology (Vrist Rønn & Høffding 2012, pp. 4‒5). The Macquarie Dictionary defines epistemology as a branch of philosophy which investigates the origin, nature and methods and limits of human knowledge (The Concise Macquarie Dictionary 1982, p. 410). Knowledge and information are ‘inextricably linked’ to the definition of intelligence, so it is clear that epistemology matters to intelligence (Brown 2007, p. 337; Vandepeer 2011a, p. 106). Epistemology is seen to bear on intelligence analysis when we ask questions about when and how knowledge can be ascribed to an agent in terms of warrant, methodology, and justification of knowledge claims (Vrist Rønn & Høffding 2012, p. 697).

Examining the literature concerning the epistemological/cognitive nature of intelligence analysis, particularly around the issue of whether it is an ‘art’ or a ‘science’, will reveal a variety of positions as to how this epistemology can be understood (Cooper 2005; Gill 2009; Hall & Citrenbaum 2010; Marrin 2011b; Wirtz 2012). Beyond clarifying these different views, Polanyi’s theoretical analysis will help us better understand the art/science distinction as it applies to the discipline of intelligence analysis. Polanyi’s thought will be seen in this thesis to provide an alternative way of thinking about the epistemology of intelligence analysis, concerning both the process of intelligence analysis and the product; more specifically, the ways in which to recognize the epistemological processes by which analysts skilfully solve problems, and a clearer understanding of what it means to ‘know’ the knowledge as a product.

An important feature of Polanyi’s epistemology of science consists in his view that there is no set method of science. This contrasts the view of Karl Popper who believed science has a single defining method, the method of scientists striving to falsify their hypotheses. Polanyi does not doubt there are methods of science, but he says they change over time and they vary from discipline to discipline, being examples of local knowledge. There is, for Polanyi, no criterion of sound data or evidence for the purposes of judging hypotheses as worthy of adoption or rejection. Each scientist has to use his/her trained judgment in order to surmise and

make decisions. There is no fixed set of rules for scientists to follow. It is the same with the standards for judging what constitutes ‘good science’, according to Polanyi. These considerations are discipline-specific and subject to change. These and other insights of Polanyi into the nature of scientific research and knowledge have obvious relevance to the way intelligence analysis is undertaken. This thesis will study his account of processes of science as involving scientists’ use of their ‘practical skills’ and their ‘tacit’ and ‘personal’ knowledge in solving the problems that their theoretical assumptions and expectations have thrown up.

The justification for choosing Michael Polanyi as our source of guidance about the nature of scientific research and the nature of scientific knowledge is, as suggested earlier, that he himself was a distinguished scientific researcher. After training in, and practicing, medicine for a short period of time, he completed his Ph.D. in physical chemistry and devoted the next 20 years to research in this field (Jacobs 2009, p. 20). His understanding of scientific research has been widely approved of. For example, Thomas Kuhn in his highly influential The Structure of Scientific Revolutions (Kuhn 1962, p. 47) writes approvingly of Polanyi’s work in the context of presenting his own concept of paradigms as being the objects of scientists’ unformulated knowledge (Jacobs 2009, p. 20). As the scholar Zammito (Zammito 2004, p. 57) noted, Kuhn ‘insists on the essential role of the implicit or, in the terminology of Michael Polanyi, the ‘“tacit”… It is here, perhaps more than anywhere else, that Kuhn’s work has affected a long-term shift of inquiry in the philosophy of science: from logic to practice’. Despite fleeting references to some aspects of Polanyi’s ideas regarding ‘tacit knowing’, especially in relation to discussions around ‘know how’, there has not been a sustained engagement of his epistemology and its relevance to intelligence analysis (Alschuler 2007; Dennis 2013; Margolis et al. 2012, p. 2; Vogel & Dennis 2012; Waltz 2003).

Throughout intelligence literature there has not been any sustained attention given to describing Polanyi’s views of science in relation to intelligence analysis. This was an important reason for deciding to use

Polanyi as the main theorist to unpack and examine epistemological issues facing intelligence analysts. The researcher fully agrees with Maxwell (2005, p. 40) that at times the most productive conceptual frameworks are located outside of the traditional field of study (Maxwell 2005, p. 40), and believes Michael Polanyi’s ideas regarding the nature of science can generate valuable insights into the nature of intelligence analysis.

Some intelligence scholars have characterized the discipline as being ‘under-theorised’ with respect to epistemological considerations (Agrell 2012, p. 130; Betts 2007; Jervis 2010; Johnson 2017; Kreuzer 2016; Moore 2007; Posner 2006). Given intelligence analysis is a ‘knowledge-building’ activity (Bruce & George 2008), this lack of theoretical reflection and analysis regarding the epistemology of intelligence analysis is both striking and telling. This may be partly due to limited human and financial resources being made available for this sort of research, and it may also owe something to the fact that theoretical reflection is located at the end of the ‘intelligence cycle’ – at the end of the assembly line (Agrell 2012, p. 129). Others have argued that the neglect is largely due to the general acceptance of the underlying logic of what has been referred to as the ‘industrialised’ form of knowledge (Herman 1996), which assumes ‘reality’ will more or less reveal itself if only more ‘facts’ are collected. In this light, reflection and analysis regarding epistemological issues have been neglected in intelligence literature (Agrell 2012, p. 129; Evans 2009; Hatch 2013). Part of this neglect around the epistemology of intelligence analysis can be attributed to the dominance of the discipline of psychology in shaping the discourse within intelligence analysis (as a practice and field of study), which posits that the latter can only be improved marginally (Betts 2009, p. 87) by focusing on the cognitive limitations of intelligence analysis: specifically, the cognitive limitations of analysts’ efforts to ‘link the dots’ within the process of analysis, or making sense of the collected information and data (Bar-Joseph 2010, p. 24; Quarmby & Young 2010; Vrist Rønn 2014, p. 352). Although these views of managing cognitive aspects of intelligence analysis have some validity,

the intelligence discipline could benefit from examining the process and product of intelligence beyond the lens of psychology, and expand the discussions to include a greater sensitivity and awareness of epistemological considerations.

Method and approach This thesis has been sensitive to the view that different methodological approaches are based on certain theoretical and philosophical standpoints (Ormston et al. 2014) and researchers should maintain consistency between their philosophical starting position and the methods that they employ (Ritchie et al. 2013, p. 2). A range of methods and approaches were considered for this thesis to permit for high quality research (Li & Seale 2007; Patton 2005; Seale 1999). This author was aware of some of the limitations of this theoretical approach. The absence of empirical observations and data limited the capacity of this thesis to ‘test’ the accuracy or reliability of Polanyi’s understanding of science, as applied to the intelligence field. Yet ‘testing’ with empirical studies was beyond the scope of this thesis, which supported the decision to focus on a more theoretical exploration within this thesis. In support of this decision regarding the methodology, this author was further mindful of the importance of maintaining consistency of the research methodology throughout this project to generate more ‘valid’ research findings (Meadows & Morse 2001).

There are difficulties with clearly defining qualitative research (Silverman 2011). Qualitative research does not contain a universal theory or paradigm that is distinctively its own, nor does it possess a core set of methods or practices that are entirely or characteristically ‘qualitative’ (Denzin & Lincoln 2011, p. 6). Qualitative research can be understood as covering a broad church, including a wide range of approaches and methods contained within a variety of research disciplines. Despite such diversity, some scholars have attempted to outline the defining aspects of qualitative research (Barbour 2013; Carter & Little 2007; Denzin & Lincoln 2011; Nicholls 2009; Silverman 2011). In a broad sense, qualitative

research is often described as possessing an interpretive approach, focused on examining issues and taking in different views of research participants (Flick 2014). Denzin and Lincoln argue that qualitative research can be described as a set of interpretive research practices that take into account the perspectives of research participants, and attempt to represent those findings by making sense of it all (Denzin & Lincoln 2011, p. 3). Qualitative research is also often described as involving words or images rather than numbers (Ritchie et al. 2013, p. 3).

Whilst some scholars have stressed that qualitative and quantitative research methods should be understood as being complementary strategies, (Gilbert 2008; Silverman 2011), this thesis has adopted qualitative research largely because intelligence studies research is traditionally qualitative (Judge, Colbert & Ilies 2004; Ratcliffe 2014; Waltz 2014), and there would be limitations to exploring this field through the ‘lens’ of quantitative methods (Hawley 2008).

This research approach adopted in this thesis considered the function or role of ‘theory’ in relation to qualitative research, and the issues around whether the research should be conducted under the banner of a specific ‘school’ or theoretical tradition. The interpretivist approach was adopted for this thesis, since it was arguably the most pragmatic approach to best fit the specific research question (Creswell & Poth 2017). This approach allowed for a more flexible treatment of attention towards the ideas and concepts examined across a broad range of fields (Carson et al. 2001). This is unlike positivism, which adopts a more structured approach to research, emphasizing that there is a single objective reality independent of the researcher’s standpoint (Creswell & Poth 2017).

The interpretivist position holds that researchers generate meaning as they interpret their world (Williamson 2002; Williamson et al. 2002) and it is an approach that has enabled the author to meaningfully engage with Polanyi’s view of science, especially regarding his ideas regarding the way scientists make discoveries and skilfully solve problems. This appreciation would have been hindered, indeed prevented, had a positivist approach been pursued, particularly since Polanyi’s argument that scientists

routinely make discoveries and solve problems using thought-processes which are ‘tacit’ and in some sense pre-logical, or extra-logical, is an anathema to positivism. The interpretivist approach easily accommodates Polanyi’s ideas, whereas the positivist position adopts the restrictive view that scientists, social scientists and typically most people are animated by rational and logical arguments (Carson et al. 2001; Willis & Jost 2007). Adopting the positivist position would have prevented the inquiry of this thesis by excluding Polanyi’s pre- or extra-logical arguments as being outside of science. As Latour has observed, the methods of scientists powerfully affect how problems are identified and characterized (Latour 1990).

The goal of the design was to construct a logical sequence of steps to conduct the research. The literature review was the principal means for the collection of information for the thesis. The review explored a range of multi-disciplinary issues, including intelligence studies, theories of intelligence and viewpoints on science. The core tasks of this stage were to:

 Review the available literature relating to intelligence studies, intelligence analysis developments across law enforcement and national security fields  Develop a thematic review of the literature in order to identify the core issues and drivers for improving intelligence analysis  Outline pertinent insights of Polanyi regarding his understanding of the nature of science.

The literature review chapter outlines some of the views around the nature of intelligence analysis and ends with situating Polanyi as a scientist whose outlook in epistemology can contribute to the discourse within intelligence analysis. The literature around intelligence analysis outlines definitions of key terms, common challenges facing analysts and epistemological arguments and developments in the field. Key authors on the subject of intelligence analysis expanded over time as required. Articles from the 20th century to the present day were included since this is

the period in history in which developments can be observed in some areas of intelligence analysis, particularly after the Second World War. A reading of the abstract of each article established its pertinence as a resource for inclusion. Articles that examined issues around intelligence analysis, especially from an epistemological basis, were included along with works that were illustrative of pertinent aspects of Polanyi’s epistemology.

An outline of Polanyi’s epistemology and his understanding of science specifically a) sketches the contributions of Michael Polanyi in the field of knowledge, setting out his main perception of science, and b) clarifies how these insights into knowledge and the nature of science can contribute to more completely understanding the discipline of intelligence analysis. These findings then allowed for the development of a more nuanced epistemological understanding of both the process of intelligence analysis, along with a clearer perception of the knowledge generated, or the product.

The structure of the thesis is as follows. Chapter 1 presents a detailed literature review relating to the core debates around the nature of intelligence analysis. ‘Intelligence’ and ‘intelligence analysis’ are distinguished from each other and considered in relation to some key epistemological considerations. The main focus of this thesis is around discussions as to the nature of intelligence analysis and whether Polanyi’s insights into science can shed light on this discipline. This will involve outlining different perspectives around the activities of intelligence analysts. This will help to contextualise the ongoing debate over whether intelligence analysis is best understood as an ‘art’ or a ‘science’, or some combination of the two. How these features are defined has implications for the study and practice of the tradecraft of intelligence analysis.

Chapter 2 describes a number of important themes of Polanyi’s understanding of the nature of science and the activities of scientists engaged in solving problems and producing knowledge. The logic of discovery, or the processes from which scientists make discoveries, is a key feature for how Polanyi understands the nature of science. A central

argument of Polanyi is there is no single ‘method’ of science, or a single set of rules for how science should be conducted. Developing this view, Polanyi posits that the nature of science is not based on falsificationism. He presents an alternative way for understanding science, broadly applicable to other fields involving knowledge claims and research. According to Polanyi’s understanding, a ‘skilful performance’ necessarily involves the practitioner’s ‘practical knowledge’, which further engages their ‘tacit knowing’ and ‘personal knowledge’. Polanyi explains how the ‘apprentice’ submits to the ‘master’ within the tradition observing their ‘practical’ and ‘tacit’ ways of solving problems. These insights are transferrable to the field of intelligence analysis, both in terms of better understanding the process of solving problems skilfully and more clearly recognising what it means to ‘know’ truth-claims as a product.

Chapter 3 examines in greater depth Polanyi’s arguments regarding tacit knowing applied to intelligence analysis, specifically by providing a more enriched way of understanding ‘know how’ and by detailing epistemologically how we know. The aim is to clarify his characterisation of the process of skilfully solving problems, which is transferrable to the field of intelligence analysis. To this end, the chapter draws on some aspects of Polanyi’s concepts of ‘tacit knowing’, ‘structure of skills’ and a ‘skilful performance’. This chapter examines Structured Analytical Techniques (SATs) with respect to this discussion of Polanyi’s understanding of the skilful use of tools, including his concept of ‘indwelling’. Polanyi’s discussions regarding tacit knowing, especially in regard to the skilful use of tools, contributes to a deeper appreciation of the process of skilfully solving problems for the intelligence analysis profession.

Chapter 4 examines Polanyi’s concept of personal knowledge as a way to better understand what it means to ‘know’ something in intelligence analysis as a knowledge product. Polanyi’s theme of learning a tradecraft from skilled masters is arguably applicable to intelligence analysts as it is to scientists, leading to the development of the practitioner’s personal knowledge. Some key epistemological distinctions and ways of knowing will be outlined and used to show how Polanyi’s standpoint on how we

know things provides a way for considering knowledge claims within intelligence analysis.

The Conclusion summarises the main findings of this thesis in light of Polanyi’s understanding of science and the epistemological bearing his views have to the profession of intelligence analysis. Polanyi’s concept of tacit knowing, with respect to a ‘skilful performance’, illustrates that one of the key insights regarding the process of solving problems involves the practitioner’s relationship between their tacit and explicit knowledge. Polanyi provides a more technical and nuanced accounting of how tacit knowing operates within solving problems as a process, involving a ‘from- to’ orientation by the practitioner. The analyst, like the scientist, learns these skills by practice, education and close observation of masters in the tradition in their skilful performance of tasks. The analyst ‘dwells’ in the problem they are attempting to solve, using tools as an extension of themselves, in an effort to tacitly integrate all the pertinent information and theories coherently together. According to Polanyi’s outlook, the analyst generates knowledge as a product, which is characteristically personal. For there to be knowledge there must be a ‘knower’, and such a ‘personal coefficient’ within knowledge claims is not an imperfection but an important epistemological tenet of knowledge. Taken together, Polanyi’s perspective regarding the process of skilfully solving problems, the learning of a tradecraft, and the personal dimension to knowledge, contributes to the epistemological discourse within the intelligence analysis field.

Chapter 1: The ‘Art’ and ‘Science’ of intelligence analysis

Introduction There have been perennial attempts to better understand and refine intelligence analysis, both in relation to the broad aims of improving the practice and the study within this field. Intelligence analysis as a profession and a field of study has long wrestled with debates regarding defining and universally agreeing on the core nature of the activities of analysts. A discernible trend from available literature is the sustained interest for aligning intelligence analysis along ‘scientific’ lines with an emphasis on the value of explicit knowledge (Cooper 2005; Gill 2009; Innes, Fielding & Cope 2005; Marrin 2011b; Marrin & Clemente 2005, 2006; Spielmann 2014; Wirtz 2014a). Yet comparatively there has been less attention towards sufficiently understanding the ‘intuitive’ and ‘tacit’ processes and products of knowledge claims within intelligence literature (Dennis 2013; Kamarck 2005, p. 12; Margolis et al. 2012, p. 2; Ouagrham- Gormley 2014; Tang 2017; Vogel 2013a, 2013b). Exploring the arguments regarding whether the intelligence analysis profession is an ‘art’ or a ‘science’, or some combination of both, will reveal that epistemological considerations are fundamental within this discourse. Importantly, this question has a bearing on how ‘expertise’ is conceptualised, along with considerations regarding how the tradecraft is best learned and taught, as field of study and as a practice. Within this aspect, the intelligence field has identified the medical profession as a model to follow in relation to the learning and practice of their tradecraft. A preliminary outline of the defining qualities and aims of intelligence analysis will situate the broader discussions regarding whether the field is characteristically an ‘art’ or a ‘science’, or some combination of both.

Understanding the intelligence field For at least half a century, intelligence literature reveals a preoccupation with the development of an overarching theory of intelligence (Kahn 2009).

Efforts for developing a universal theory of intelligence (Dahl 2012; Marrin & Clemente 2006, pp. 642-665) have been motivated out of a sense of better understanding and improving the practice of analysis, along with the scholarly study of this tradecraft (George 2010; Herbert 2013; Kerbel 2008; Tan 2014; Treverton 2009; Walsh 2014). There is a variety of positions as to how intelligence as a profession can be understood (Cooper 2005; Gill 2009; Hall & Citrenbaum 2010; Marrin 2011b; Wirtz 2012), and the development of a universal theory of intelligence remains an open debate (Davis 2002a, pp. 2-4; Gill 2009; Lowenthal 2013, pp. 32- 35; Treverton & Gabbard 2008; Warner 2009, p. 17). A broad understanding of the term ‘intelligence’, outlined below, will be adopted for the purposes of this thesis, covering the common qualities or characteristics contained across the national security and law enforcement domains of intelligence.

Taken from the literature, a discernible way of perceiving the field of ‘intelligence’ can be distinguished in two fundamental ways (Bang 2017): a) the way information is obtained; b) how information can enable decision-makers, based on insight from collected and analysed information (Mudd 2015; Pherson 2013; Shaw 2007). The school of thought that defines intelligence with an emphasis on the former category generally includes references to the secret aspect of intelligence. This view is captured in the following quotes:

Intelligence is the official, secret collection and processing of information on foreign countries to aid in formulating and implementing foreign policy, and the conduct of covert activities abroad to facilitate the implementation of foreign policy (Random 1958, p. 76). Secret intelligence is intelligence that others are seeking to prevent you from knowing, often with formidable security barriers and violent sanctions against those who cooperate with intelligence officers (Omand 2014, p. 22). Advocates of category a) for conceptualizing the nature of intelligence place greater emphasis on the involvement of covert collection activities (Johnson 2009) and the aspect of secrecy as a defining feature (Gill 2009; Hall & Citrenbaum 2013; Lowenthal 2014; Phythian 2017; Warner 2009, p. 18). Lowenthal bluntly states this

characterisation of intelligence: ‘Secrets are the essence of intelligence’ (Lowenthal 2011, p. 61). In a publication from the Office of National Assessments (ONA) in 2006, the agency endorsed this view that intelligence is fundamentally secret in nature, stating ‘Intelligence is covertly obtained information. That is, it is obtained without the authority of the government or group that ‘owns’ the information’ (Office of National Assessments 2006, p. 3).

In contrast, ‘intelligence’ can be characterised in terms of category b), with an emphasis on assisting decision-makers generate an impact on the security environment. Specifically, according to this position, ‘intelligence’ can be perceived as assisting decision-makers in optimising their resources for responding to or shaping the security environment. The following formulations captures this view:

Probably the simplest definition of intelligence is that it is useful information (Gyngell 2011). Intelligence is information gathered for policy makers in government which illuminates the choices open to them and enables them to exercise good judgment (Rockefeller Commission 1975). The alternative argument is that intelligence can be ‘anything from any source’ which can assist the customer (Treverton & Gabbard 2008; Warner 2009, pp. 16-17). This position, which places primacy of intelligence on information from all sources (covert and overt), can be observed from the Flood Report into Australian intelligence. The Report stated:

While intelligence information is important, and often vital, to assessment, it is normally not the main source of information used by intelligence assessment agencies. Open sources ... provide the greater part of the information available to the Australian Government (Flood 2004, p. 10). Interestingly, even though the ONA claimed in 2006 that ‘Intelligence is covertly obtained information’ (Office of National Assessments 2006, p. 3), the Flood Report had already revealed two years earlier that the ‘ONA product draws heavily on published or open source material – it is the single largest source of material for ONA reporting’ (Flood 2004, p. 108). The argument that ‘intelligence’ is limited

to or can even be characterised as drawing significantly on covertly obtained information appears to be questionable.

Moving beyond general ways of characterising the intelligence field from a national security perspective, the law enforcement profession offers its own position and understanding of intelligence. At the outset, it is worth acknowledging that one of the most significant developments in the theory and practice of policing relates to changing perceptions of ‘intelligence’ within law enforcement (Brown 2007; Parliamentary Joint Committee on Law Enforcement 2013; Gill 1998; Sheptycki 2009); specifically, the appreciation for the importance of intelligence, and the growing recognition of the police as ‘knowledge workers’ (Brodeur & Dupont 2006, p. 1). Ericson and Haggerty coined the term ‘knowledge workers’ to refer to professionals engaging in the collection and analysis of data and information, leading to informed action (Ericson & Haggerty 1997, pp. 83- 84). In this sense, police have become increasingly ‘knowledge workers’ with respect to the institutional acknowledgement, over the last 35 years, of the core value of collecting and analysing pertinent information and data for shaping policing methodologies (Ratcliffe 2014). It would be amiss though to consider policing as having only recently become interested in ‘intelligence’, since information collection and analysis has been a fundamental aspect of policing in the US, UK, Australia, and Canada well before ‘intelligence-led’ policing methodologies emerged (Kelling & Coles 1997; Peak & Glensor 1999; Walsh 2001, pp. 347-350).

The idea of police officers operating as ‘knowledge workers’ within the so-called ‘intelligence-led’ policing paradigm (beginning in the 1990s) does not necessarily re-imagine the role of police, but rather refines policing in terms of the allocation of resources and the exercising of their duties (Eck, Clarke & Petrossian 2013; Peters & Cohen 2017; Scott 2017; Shearing & Wood 2007, p. 55; Townsley 2017). Intelligence-led policing has a more objective basis for deciding priorities and resource allocation (Ratcliffe 2016, p. 3). This is achieved by design, by applying analytical techniques and statistical predictions for identifying likely targets or vulnerable areas for police to anticipate and intervene (James 2017;

Knutsson 2017; Lewandowski, Carter & Campbell 2017; Perry 2013, p. xiii; Townsley, Mann & Garrett 2011).

This ‘intelligence-led’ paradigm shift within policing can be further recognised with respect to perspectives on ‘criminal intelligence’. Findings from the Parliamentary Joint Committee on Law Enforcement (2013, p. 6) observed that despite ‘criminal intelligence’ escaping a universally agreed definition, the Committee concluded the concept is constituted by the following key elements:

 information that has been validated and value-added;  information that has been analysed; and  not evidence.

The inquiry further elaborated as to the nature of intelligence by citing the United Nations Office on Drugs and Crime, which outlines intelligence as constituted by the following equation: ‘information + evaluation = intelligence (United Nations Office on Drugs and Crime 2011, p. 1).

It is clear that ‘intelligence’ in terms of criminal investigations is less preoccupied with whether the information is obtained covertly or overtly, but rather with a greater interest in the end product – making an impact on the security environment. As previously discussed, this is consistent with one of the main ways of characterising intelligence as understood by the national security field; namely, information and data collected (covert or overt) to ‘…protect your interests or to maintain a valuable advantage in advancing your interests over those posing threats to them’ (Cornall & Black 2011, p. 6). What matters for intelligence for both law enforcement and national security agencies has less to do with whether the information was obtained secretly or with publicly available sources, and more with the conferring of insight to promote decision-advantage (Cornall & Black 2011, p. 6).

The epistemological foundations of intelligence studies, it can be argued, is largely drawn from the ‘national security’ paradigm (O'Malley

2016; Sheptycki 2009, pp. 166-168). This paradigm has been shaped by the discourse on international relations, with a focus on conflict and the use of state power (Lawson 2003, pp. 78-82). In contrast, there is an increasingly large body of research from law enforcement intelligence, with its own paradigm based on the theory of human security (Coyne 2014; Black 2016; Bradford et al. 2016; James et al. 2017; Picciotto 2017; Rubenstein 2017; Weber, Fishwick & Marmo 2016). The theory of human security posits that intelligence considerations go beyond the use of state power (Lawson 2003, pp. 89-91), partly because of the rise of the ‘pluralisation’ of policing, or the provision of policing services from non-law enforcement agencies (Bradley & Sedgwick 2009; Crawford 2005; de Maillard & Zagrodzki 2017; Newburn 2003; O’Neill & Fyfe 2017; Terpstra 2017; van Steden 2017). The paradigm of human security within law enforcement holds that ‘intelligence’ is aimed towards proactively seeking to better understand the causes of insecurity, with less of a focus of reacting to insecurities (Broll 2016; Bullock 2013; Dupont 2017; Phythian 2012; Sheptycki 2009, p. 171; Wall 2007).

Whilst there is a discernible agreement within intelligence literature regarding the aim of the field being directed to: a) reducing uncertainty and b) assisting decision-makers make an informed decision on how to respond to or shape the security environment (Bammer 2010; CIA 2009; Eck, Clarke & Petrossian 2013; Fingar 2011a; Ratcliffe 2016; Weiss 2007; Wheaton & Richey 2015), there has been some challenges in defining the profession (Walsh 2011); for example, whether intelligence is a tool to explain (a scientific approach) or understand (an interpretive approach) the security environment (Walsh 2011, p. 283). These distinct aims stem from a bifurcating choice between the ‘positivist’ position and the ‘interpretivist’ perspective on understanding intelligence. Methodologically, the positivist position concentrates on describing and explaining phenomena (Johnson 2006a; Lillbacka 2013; Mackenzie 2011; Ryan 2015; Walsh 2011, p. 283-285; Zohar 2013), whereas the interpretivist view focuses on an understanding and interpretation of the objects in

question (Carson et al. 2001, p. 6; Grant & Giddings 2002; Moini 2011; Xinping 2002).

The intelligence field is constituted by a broad church of practitioners, but in an attempt to satisfy such a diverse tradition, Patrick Walsh outlined three core characteristics which arguably represent the foundation of the intelligence profession: the ‘intelligence environment’ (the focus of the collection and analysis), ‘secrecy’ (covert action and collection) and ‘surveillance’ (observing the subjects in question) (Walsh 2011, p. 29). Other scholars, such as Jerry Ratcliffe, have observed similar models for defining the intelligence enterprise, such as his ‘3-I’s model covering the following elements: interpret the environment, inform decision-making, impact on the environment (Ratcliffe 2004). Kahn, an historian in cryptology, emphasises that ‘intelligence’ should have an impact on the environment, by accurately perceiving the nature of threats, so as to ‘optimize one’s resources’ (cited in Pringle Jr 2015, p. 843). While much attention has been devoted to outlining the importance of adequate collection – or sufficient information and data on the identified subject – it is important to acknowledge that at the heart of ‘intelligence’ is the process and product of analysis (Agrell 2002; Bruce & George 2008; Davis 1995; Fini 2004; Herbert 2013; Horelick 1964; Pfautz et al. 2006; Phythian 2017).

Organizationally, the central role of the analyst within the intelligence community was outlined by the former Director of Central Intelligence at the CIA, William Colby, who observed that ‘At the center of the intelligence machine lies the analyst, and he [sic] is the fellow to whom all the information goes so that he can review it and think about it and determine what it means’ (Colby 1992, p. 21). As the information and data is collected, the analyst then engages in the process of interpreting that information and data (Johnson 1996, p. 657). If the intelligence enterprise can be characterised as a knowledge-building activity, then analysts can be understood as the ‘knowledge workers’ (Brodeur & Dupont 2006). The analyst must decide, based on often incomplete, ambiguous or deceptive information, whether a threat exists and assess its precise nature (Knorr,

1979, p. 70). The intelligence product then allows for the decision-maker to optimize their resources (Kahn cited in Pringle Jr 2015, p. 843). The focus of the work of the analyst, within the organisation, is shaped by the aims set by that organisation. These aims can broadly cover the following categories: 1. Prescriptive models require analysts to represent how systems could work; for example, engineers use this model to outline how a structure could be built; or within political science to provide clarity regarding how a political organisation could operate. 2. Descriptive models are used to understand a given situation and the way it functions; this could involve describing the parts of a system and how they behave together. 3. Predictive or exploratory models represent the fashion by which a dynamic system could function in the future, under certain circumstances (Waltz 2014, pp. 2-3).

Moving beyond the broad aims of intelligence, it is necessary to outline aspects of the activities of analysts – being principally the business of analysis itself. Precisely what steps are followed within ‘intelligence analysis’ to deliver assessments and judgments is elusive or defies clear articulation (Gentry 2016; Herbert 2013; James et al. 2017; Kreuzer 2016; Lowenthal & Marks 2015; Zafeirakopoulos 2014). This is arguably because publications on this topic do little to provide definitive guidance as to what universally accepted actions comprise analysis and the purposes these actions are designed to support (Eck, Clarke & Petrossian 2013, p. 10). In a broad sense, Lord Butler in his 2004 report on Weapons of Mass Destruction outlined the aims of ‘intelligence analysis’ in terms of providing ‘validation’ and ‘assessment’ (Butler 2004, p. 5). The process of ‘validation’ is aimed towards ensuring the collection of information and data is accurate (Barnes 2016; Butler 2004, p. 9; Howis 2015; Jones 1993; Pringle Jr 2015; Smith 2014). Following the validation phase is the efforts towards providing ‘assessments’, a process generally conducted by subject matter experts, involving the assemblage of intelligence reports into the meaningful context of the security environment (Butler 2004, p. 10; Clarke 2013; Gainor & Bouthillier 2014; Marrin 2016; Pherson & Beebe 2015). It is widely acknowledged within the intelligence field that ‘analysis’

in terms of the aims of ‘validation’ or ‘assessment’ does not impart judgments of certainty (Bammer 2010; Butler 2004, p. 15; CIA 2009; Fingar 2011a; Phythian 2012; Posner 2006). This acknowledgement is based on the recognition that evidence and clues are sometimes incomplete, ambiguous, or deceptive and the world of human affairs is often contingent on a range of (largely indeterminate) factors (Agrell & Treverton 2015; Hicks 2013; Hoffman et al. 2011; Marrin 2013; Puvathingal & Hantula 2012; Townsley, Mann & Garrett 2011; Zegart 2009).

With respect to navigating the complex security environment, intelligence analysis as a field has drawn on a series of disciplines and social research methodologies, ranging from how to best understand political behaviours to illicit drug markets (Bruce & George 2008; Chauvin & Fischhoff 2011; Sinclair 2010; Walsh 2011). The field of intelligence has a strong alignment to that of a ‘social science’, an acknowledgement which can be observed by Sherman Kent, one of the earliest pioneers in the field at the CIA. As Kent observed, ‘…the social sciences are very largely constituting the subject matter of strategic intelligence’ (Kent 1949, p. 156). Whilst there are methods and aspects of intelligence that involve the ‘hard’ sciences, it is at heart a social science in many of its core characteristics (Chauvin & Fischhoff 2011; Dupont 2017; Fingar 2011b; Hudson 2009; Phythian 2017; Tang 2017; Walsh 2011, p. 287). To the extent to which ‘intelligence analysis’ can be understood as a practice in social science, this raises the question whether analytical methods can accurately measure variables, and therefore what can really be definitively ‘known’ in the intelligence context (Hall & Citrenbaum 2010; Phythian 2017; Walsh 2012, p. 244).

In relation to the acknowledgement that intelligence analysis is largely devoted to exploring epistemological considerations, from what is known to that which is not known, it is not surprising that there is a strong interest in viewing aspects of analysis psychologically (Heuer 1999; Hoffman et al. 2011; Kan et al. 2013; Puvathingal & Hantula 2012; Wastell, Clark & Duncan 2006). This is particularly the case with

reference to understanding cognition within analysis (Hudson 2009; Manjikian 2013; Waltz 2014, p. 1). Analysts are required to be sensitive not just to the conclusions they arrive at, but also to how they reached such knowledge claims. As Heuer observed:

Intelligence analysts should be self-conscious about their reasoning processes. They should think about how they make judgments and reach conclusions, not just about the judgments and conclusions themselves (Heuer 1999, p. 31). In this psychological sense, intelligence analysis is an activity which engages in ‘meta-cognition’, or as Mark Lowenthal remarks, ‘thinking about thinking’ (Lowenthal cited in Moore 2007, p. 8). The psychological features of intelligence analysis are one of the most widely accepted aspects within this field and are featured prominently in the literature (Agrell & Treverton 2015; Bang 2017; Coulthart 2017a; Evans & Kebbell 2012; Gentry 2014; Hare & Coghill 2016; Kreuzer 2016; Lowenthal 2013; Marchio 2014; Pherson & Beebe 2015; Phythian 2017).

Given that the practice of intelligence analysis is based around knowledge claims, evidence, and clues (Agrell 2002; Clare 2012; Johnson 2010; Ratcliffe 2014), it has been generally acknowledged that the types of reasoning employed throughout the analysis process can profoundly influence the judgments generated by the analysis (Bruce 2008, p. 135; Fingar 2011b; Heuer 1999; Lahneman & Arcos 2017; Lillbacka 2013; Lowenthal & Marks 2015; Phythian 2017; Tan 2014; Zohar 2013). The intelligence scholar Edward Waltz outlines the broad reasoning processes involved in intelligence analysis in terms of the distinctions between analysis and synthesis (Waltz 2003, p. 16). Analysis meaning the movement from presumed effects (the imagined solution) backwards – by searching for antecedent causes which could deliver the effect. This process is followed until the causes are known for being responsible for the effect. Demonstrating that the effect is caused by the proposed process amounts to validating the hypothesis. Synthesis, in contrast, proceeds by already knowing the causes, and constructing or assembling the cause-to-effect chain which proves the hypothesis (Waltz 2003, p. 160). The final outcome of this process is the intelligence analysis product

which can be the representation of static content or dynamic relationships of the objects in question, in relation to the security environment (Coulthart 2017a; Dunn 2012; Eck, Clarke & Petrossian 2013; Goodman & Omand 2008; Odom 2008; Prunckun 2015; Vandepeer 2011b; Waltz 2003, p. 3).

With respect to more clearly acknowledging the different types of ‘evidence’ which may be drawn upon within the field of intelligence analysis, Pherson and Pherson refer to Schum’s schema (2001), outlining six main types of evidence in intelligence analysis (Pherson & Pherson 2012, pp. 90-92). These types of evidence are as follows:

1) Tangible evidence: direct observation, and consists of such materials as original documents, pictures, or physical objects. 2) Authoritative evidence: scientific data such as the periodic table of elements and tidal charts, government records such as birth and death certificates, property records and motor vehicle records. 3) Testimonial evidence: reports of a development, conversation or event by an observer or participant in the activity. 4) Circumstantial evidence: conclusions that rest on some observations plus assumptions that the analyst has made. 5) Negative Evidence: information that falsifies or is not consistent with a hypothesis. 6) Missing Evidence: information that one would expect if a hypothesis were to be verified, but which has not been found (at least presently).

Great expertise is central to clearly identifying the most credible clues and evidence, along with making reliable and accurate judgments and assessments (Bruce & George 2008; Dahl 2012; Hicks 2013; Kreuzer 2016; Martin et al. 2011b; Mole 2012; Moore, Krizan & Moore 2005; Phythian 2017; Waltz 2014). For example, there was more evidence indicating at the time that Saddam Hussein had WMDs (Dahl 2014; Jervis 2006, p. 14) compared to the meagre evidence that Osama Bin Laden was in a hidden compound in Abbottabad (Intelligence analysis in the 21st century 2016). As remarked by Robert Jervis, it is sometimes the case that, in the intelligence world, scenarios which have the least amount of

evidence can turn out to be the closest to the truth (Jervis 2010a). This general acknowledgement regarding the difficulties facing analysts in their core task of understanding the reliability and credibility of evidence and clues is captured by Roberta Wolhstetter’s summary of the failure to predict and prevent the Pearl Harbor attack:

After the event … a signal is always crystal clear; we can now see what disaster it was signalling since the disaster has occurred. But before the event it is obscure and pregnant with conflicting meanings (Wolhstetter 1962, p. 387). The goal of intelligence analysis is the provision of reliable and credible assessments to demarcate truth from falsehoods and sometimes reveal deception (Corkill 2011, p. 8). Whilst this objective is clear, in practice this aim has been described by intelligence scholars and practitioners as being at times elusive (Dahl 2017; Ellis-Smith 2016; Hicks 2013; Jensen 2018; Intelligence analysis in the 21st century 2016). Government reports and academic articles into ‘failures of intelligence’ often refer to analytical shortcomings in terms of not sufficiently making the epistemological distinctions between knowledge claims based on inferences and belief, in contrast to more objective and credible knowledge (Betts 2009; Cooper 2005; Hatch 2013; Lahneman & Arcos 2017; Marrin 2011a; Pritchard & Goodman 2008; Shelton 2011; Smith 2014; Vrist Rønn 2014). For example, being mindful of the epistemological difference between what one knows and what one believes can test the mettle of the best analysts (Agrell 2012; Herbert 2006, p. 667; Westera et al. 2013; Puvathingal & Hantula 2012; Smith 2017). The former US Secretary of Defence Donald Rumsfeld made the now famous observation regarding ‘known unknowns’ and ‘unknown unknowns’. One of the points Rumsfeld was making was that for any given intelligence problem there are an indefinite quantity of unknown facts, some of which are relevant and others which are not. The central task of the analyst is to identify the relevant ‘knowns’ and possible ‘unknowns’, whilst also being aware they do not know all the unknowns (Evans & Kebbell 2012; Knorr-Cetina 1999; Lowenthal 2014; Moore 2007).

A core aim of intelligence analysis is an activity of transitioning from ‘knowing’ to ‘understanding’ (Ellis-Smith 2016, p. 36). ‘Knowing’ refers to identifying what pieces of the puzzle are relevant (Trent, Patterson & Woods 2007; Hoffman et al. 2011; Lowenthal 2011; Mole 2012; Nolan 2012); ‘understanding’ involves recognising the most salient pieces of information and fixing them in context to the whole picture (Bar-Joseph 2010; Dahl 2017; Puvathingal & Hantula 2012; Warner 2017; Zohar 2013). The merits of discovering new clues or evidence are only valuable in terms of contributing to understanding the context of the security environment (Brown 2007, p. 339; Marrin 2012b; Martin et al. 2011a; Pherson 2013; Ryan 2006; Treverton & Gabbard 2008; Wirtz 2014a). Understanding provides a greater perception of the objects, persons or groups, or locations within the context of the security environment. Such understanding is only valuable if it is communicated effectively and in a timely manner (Agrell & Treverton 2015; Bruce & George 2008, p. 10; Clarke 2013; Davis et al. 2015; Ellis-Smith 2016; Marrin 2017). ‘Understanding’ translates to proving decision-makers with either a) insight – knowing how and/or why something is happening, and/or b) foresight – using gained insights to anticipate what may plausibly occur in the future (Ministry of Defence 2010; Hare & Coghill 2016; Westera et al. 2013; Wippl 2014).

The intelligence analysis discipline is most certainly plural (Evans & Kebbell 2012; James et al. 2017; Kreuzer 2016; Lowenthal & Marks 2015; Peters & Cohen 2017; Treverton & Gabbard 2008, p. 3). In the military context, the aims of analysis could be directed towards penetrating the secrets, capabilities, and intentions of states, to short-term tactical analysis to support current operations in an effort to protect assets and resources on the battlefield (Bang 2017; Coyne 2014; Czerwinski 1998; Evans 2009; Mole 2012; van Riper 2006; Wahlert 2012). In the law enforcement field, intelligence analysis is oriented towards uncovering, disrupting and potentially (or even ultimately) prosecuting criminal behaviour (Evans & Kebbell 2012; James et al. 2017; Silverman 2007; Townsley, Mann & Garrett 2011; Wall 2007). For national security, intelligence analysis can

assist with planning for the future, by identifying emerging trends and the nature or levels of risk (real or perceived) posed by threat actors, either of state or non-state entities (Fishbein & Traverton 2004; Goodman 2009; Hare & Coghill 2016; Howis 2015; Manningham-Buller 2005). Analysts may be directed to pierce the shroud of secrecy—and sometimes deception—that state and non-state actors use to mislead (Fournie 2004; Pherson 2013; Picornell 2013; CIA 2009, p. 1; Smith 2014; Whitney 2005).

The intelligence cycle A common reference made around the discipline of intelligence analysis concerns the ‘intelligence cycle’. The intelligence cycle constitutes a methodology that is traditionally presented or described as a sequential process (George 2011; Hatch 2013; Herman 1996, p. 56; Hulnick 2006; Johnson 2009; Phythian 2013). The intelligence cycle is intended to present the analyst with a series of steps to guide their analytical activities (Quarmby & Young 2010, p. 12). This process is graphically represented below.

Figure 1: The Intelligence Cycle

(Johnson 1986, p. 2)

The fundamental aim of the intelligence cycle is the process of breaking down the information and data to their essential respective components, then constructing meaning from those data sets (Baartz

2005; Hatch 2013; Holmes 1992; Kreuzer 2016; Mitchell 2005). The cycle is partly designed to assist analysts to ‘cope with epistemic complexity’ (Herbert 2006, p. 667; Heuer 1999; Richards & Pherson 2010). This model or process generally takes the following form: 1) identifying the requirements of the customers who will consume the intelligence products; 2) collecting ‘raw’ information and data as required, potentially via human sources, electronic communication signals, imagery and geospatial intelligence; 3) processing the collected information and data to further assist with the analysis phase; 4) analysis of the raw information and data to generate a ‘finished’ intelligence product; 5) dissemination of the product to the consumers (Tan 2014, p. 219). The process also includes feedback from the policymakers concerning how effective and useful the product was for their needs (Evans 2009; Lowenthal 2014, pp. 54-67). As observed by the practice of intelligence analysts, the ‘intelligence cycle’ is not generally sequential as traditionally articulated or visually represented (Corkill 2011; Gill 2009; Hulnick 2006; Johnson 2009; Lowenthal & Marks 2015; Martinez-Conesa et al. 2017; Waltz 2003). Instead, the analyst is constantly in a never-ending conversation with policy makers and collectors over events and developments (Dahl 2012; Ellis-Smith 2016; Howis 2015; Kreuzer 2016; Marrin 2003a, p. 623; Quarmby & Young 2010, p. 15).

The ‘intelligence cycle’ has been recognised by some as representing a ‘scientifically founded methodology’ (Lillbacka 2013, p. 305; Marrin & Torres 2017; Prunckun 2015). As Prunckun observes, ‘Scientific methods of inquiry are founded on the steps of the intelligence cycle – problem formulation, data collection, data collation, analysis (including hypothesis testing), and dissemination’ (Prunckun 2014, p. 68). On the other hand, others have claimed that since the process of accurately identifying and perceiving risks cannot be always codified, the ‘intelligence cycle’ is best understood as involving both ‘art’ and ‘science’ (Cain 2012, p. 137; Evans & Kebbell 2012; George 2010; Hulnick 2006; Johnson 2010; Mudd 2015). One of the key observations within the ‘art’ versus ‘science’ debates regarding the defining characteristic of ‘intelligence’ and

‘intelligence analysis’, is the acknowledgement by some that without a universally accepted definition, analysts may fluctuate between the goals of science – dedicated to objectivity and rigour – or art – being open to possibilities and creativity (Jensen 2018; Kerbel 2008; Lowenthal & Marks 2015; Moore 2007; Wirtz 2014a). This leads into a broader discussion regarding how to best characterise the nature of ‘intelligence’, specifically in relation to arguments as to whether it is an ‘art’ or a ‘science’, or some combination of both.

Intelligence analysis as ‘Art’ Prominent scholars in the area of intelligence analysis have identified a lack of research into the value of non-structured or ‘artistic’ methods and ways of understanding and advancing intelligence analysis (Bang 2017; Corkill 2011; Coulthart 2016; Dreisbach 2011; Lahneman & Arcos 2017; Marrin 2012c, p. 540; Wirtz 2014a). Closely related to this general acknowledgment is the underlying question regarding whether intelligence analysis should be accepted as an ‘art’, drawing largely on subjective and intuitive judgments (Alschuler 2007; Horan 2007; Jensen 2018; Khalsa 2009; Lahneman & Arcos 2017; Wirtz 2014a), or a ‘science’, characterised by largely structured and systematic analytic methods (Coulthart 2016; Folker 2000, p. 6; Marrin 2005; Marrin 2017; Prunckun 2015; Random 1958; Vandepeer 2011b). Advocates of intelligence analysis as an ‘art’ (Betts 2009, pp. 9-10; Cain 2012; Gill 2009; Hicks 2013; Jensen 2018; Martinez-Conesa et al. 2017; Mudd 2015; Schum 1987) argue that since the variables are so complex and indeterminate, analysing them using allegedly scientific methods constitutes a pseudoscience (Betts 1987, p. 338; Innes, Fielding & Cope 2005; Wirtz 2012). For analysts to develop and exercise their substantive expertise requires acknowledging that such judgments depend to a degree on exercising their intuitive skills (Betts 2009; Bruce & George 2008; Carson 2009; Evans & Kebbell 2012; Lahneman & Arcos 2017; Wastell, Clark & Duncan 2006). Those who promote intelligence analysis as an art emphasise the methodological value of the logic of induction and empiricism, by paying closer attention to changing patterns that enhance or can draw on the intuitive function of the

analyst (Czerwinski 1998, p. 139; Dahl 2017; Lahneman & Arcos 2017; O’Connor & McDermott 1997a; Wastell, Clark & Duncan 2006).

In contrast, there are those who view intelligence analysis as more of a ‘science’, placing greater emphasis on the logic of deduction, particularly regarding the value accorded to subject matter experts and their respective theoretical models for understanding and explaining aspects of the security environment (Bruce & George 2008; Clarke 2013; Johnson 2003; Marrin 2011b; Moore, Krizan & Moore 2005; Warner 2017). The ‘artistic’ elements of intelligence analysis refers to more personal or localized considerations of the analyst’s instinct, education, and experience (Corkill 2011; Czerwinski 1998, p. 139; Dexter, Phythian & Strachan-Morris 2017; Mudd 2015; Pherson 2013; Wirtz 2012). This acknowledgement of the roles of instinct, education and experience of the analyst are recognised as being important for their respective analysis because it is these features that constitute the foundations of ‘expertise’, particularly regarding ‘skilful’ analytical judgments (Breckenridge 2010; Bruce & George 2008; Evans & Kebbell 2012; Goodman & Omand 2008; Hegarty 2000; Moran 2016; O’Connor & McDermott 1997a; United Nations Office on Drugs and Crime 2011; Wheaton 2011; Wirtz 2014b).

With respect to the general acknowledgement that intelligence analysis is largely a cognitive activity, involving some elements of ‘artistic expertise’, the field of cognitive psychology further articulates and substantiates this claim. In relation to the nature of perception and pattern recognition, this psychological process is more characteristically intuitive and ‘artistic’ – a view which is accepted by some intelligence scholars (Galotti 2017; Kałamała et al. 2017; Kan et al. 2013; Martin 2012; Steen & Kastelle 2012; Varela, Thompson & Rosch 2017; Vrist Rønn 2014; Wagemans et al. 2012; Wastell 2010). Studies into decision-making within psychology – acknowledged within the intelligence field – further indicate that intelligence analysts, involved in making decisions, engage in processes that are characteristically non-structured (and therefore non- scientific) and ‘intuitive’ (Czerwinski 1998; Dexter, Phythian & Strachan-

Morris 2017; Dreisbach 2011; Folker 2000; Gladwell 2011; Kan et al. 2013; Lerner 2007; Rosenbaum 2007).

Pioneers within cognitive science and decision-making processes, Amos Tversky and Daniel Kahneman, revealed in their studies in the 1970s and 1980s that people routinely use heuristics and intuition to make decisions (Coulthart 2016, p. 11; Kahneman 1973, 2003, 2011; Kahneman & Tversky 1982; Martin 2012; Tversky & Kahneman 1971, 1973, 1975). Structured thinking is not the ‘natural’ way the human mind works (Gilovich, Griffin & Kahneman 2002; Jones 2009, p. 8; Martin 2012; Martin et al. 2011a). Studies into the decision-making processes of intelligence analysts have generally revealed that analysts use a more non-structured and ‘intuitive’ style or approach to analysis (Bruce 2008; Coulthart 2016; Feltz 2008; Khalsa 2009; Martin et al. 2011a; Phythian 2017). Taken together, such acknowledgements indicate there is merit to the position that intelligence analysis can be partly characterised as involving ‘artistic’ features.

Intelligence analysis as ‘Science’ An understanding of intelligence analysis with a view to make it a more scientific enterprise became even more popular after the 9/11 attacks in the United States (Agrell & Treverton 2015, pp. 1-15; Bruce 2008, pp. 171- 190; Coulthart 2017a; Marrin & Torres 2017). Gormley captures this sentiment by stating that ‘The chief analytic shortcoming… is the decidedly unscientific nature of the intelligence community’s approach to analysis’ (Gormley 2004, p. 14). The desire to make the ‘tradecraft’ of intelligence analysis more ‘scientific’, by adopting so-called scientific principles and methods, has a long history. Some of the earliest writings in the field, including from Sherman Kent (a seminal figure in the CIA, previously a professor in History) advocated scientific methods not only to understand certain problems but to make verifiable assessments (Agrell 2012, p. 130; Kent 1949). Such early thinkers, such as R.A. Random, strongly disapproved of intuition – as Random wrote in 1958 that the rejection of the scientific methodology in favour of intuition would be like abandoning

rationality in favour of ‘intuitive guesses and unanalysed conjectures’ (Random 1958, p. 2). Other intelligence scholars have argued that the scientific method provides the underlying method for intelligence analysis (Wilcox cited in Laqueur 1983; Marrin 2012c, p. 531). Outlining the popular perception of science, as understood broadly speaking by some within intelligence, will serve as a useful reference point. The intelligence scholar Washington Platt noted in 1957:

The so-called ‘scientific method’ means different things to different people, but the basic features are much the same. These features are: collection of data, formation of hypotheses, testing the hypotheses, and so arriving at conclusions based on the foregoing which can be used as reliable sources of prediction (Platt 1957, p. 75). As Stephen Marrin and Jonathan Clemente have observed:

…the intelligence analysis process, though an approximation of the scientific method, does not parallel it exactly because no experiments are possible in the international arena. Yet, most writers who focus on analytic tradecraft – whether they realise it or not – portray the intelligence analysis process as a version of the scientific method (Marrin & Clemente 2005, p. 711). This view that intelligence analysis can be considered a methodology approximating the scientific method (Marrin 2003a, p. 623; Schum 1987), is generally considered ‘scientific’ to the degree in which the process is ‘systematic’ and ‘logical’ (Agrell & Treverton 2015; Howis 2015; Prunckun 2014, p. 68; Wirtz 2014b; Ylikoski 2017). This notion is observed by Martin et al.: ‘As a science, intelligence analysis is a systematic process, which generates and tests hypotheses objectively. Following the scientific method, analysts adhere to rules to develop sound and logical judgments’ (Martin et al. 2011b, p. 30).

A common argument for making intelligence analysis more systematic, and thereby allegedly more scientific, can be found in various academic quarters (Agrell & Treverton 2015; Coulthart 2016; Folker 2000; Prunckun 2014). According to some, the intelligence analysis field could benefit from adopting structured techniques within analysis as a means of becoming partly more ‘scientific’ (Artner, Girven & Bruce 2016; CIA 2009; Coulthart 2016; Folker 2000, pp. 1-2; Gendron 2012; Pherson 2013; Spielmann 2014). The purported benefits of aligning intelligence analysis

more closely with ‘scientific’ standards and processes would be the transition from a ‘tradecraft’, characterised as ad-hoc processes and standards, to a modern ‘profession’, possessing a more disciplined and systematic methodology (Agrell 2012; Gentry 2016; Kreuzer 2016; Marrin 2007a, p. 7; 2017; Prunckun 2015).

Marrin contrasts the scientific, structured methods with analytic techniques which are not structured (Marrin 2007a, p. 7). These non- structured methods are viewed as being essentially ‘intuitive’, characterised broadly as an ‘instinct’ or ‘gut feeling’, which do not demonstrate explicit reasoning processes (Brock 2015; Dreisbach 2011; Folker 2000; Horan 2007; Kan et al. 2013; Lerner 2007; Martin et al. 2011b). Importantly, the extent to which the analytical process is made explicit or externalised (in writing or graphically) has been observed by some as making such analytical methodologies more ‘scientific’ (Coulthart 2016; Johnson 2005; Marrin 2007a; Spielmann 2014; Townsley, Mann & Garrett 2011) – unlike intuitive forms of analysis which are largely non- explicit. As will be examined in greater depth in Chapter 3 around structured analytical techniques, or SATs, such analytical tools are aimed towards making the process of analysis more scientific. These techniques may include exercises such as Alternative Competing Hypothesis or ACH. ACH involves the systematic consideration of different hypotheses (Pherson 2005). Other techniques include Devil’s Advocacy, which involves imagining plausible counter-arguments which may challenge or undermine the main contention, or Key Assumptions Check, an exercise engaged in checking the veracity of key assumptions (Pherson 2005). These techniques are offered by some intelligence scholars and practitioners as ways for developing analysis along more ‘scientific’ principles by making the process more transparent and systematic (Coulthart 2016; Marrin 2007a; Pherson & Beebe 2015; Prunckun 2015; Richards & Pherson 2010). Adding structure or methodological ways for explicitly examining issues within intelligence analysis is aimed towards making the process and the product more ‘scientific’, which will be examined in greater depth in Chapter 3 and Chapter 4 respectively.

Professional and institutional efforts to improve law enforcement, particularly with reference to the broad strategy of ‘intelligence-led’ policing –which offers policing units a more systematic approach for understanding and responding to the security environment (Ericson & Shearing 1986; James et al. 2017; Jerry Ratcliffe & McCullagh 2001; Peters & Cohen 2017; Ratcliffe 2016; Townsley, Mann & Garrett 2011) – can be understood as an aim to align law enforcement work flows and practices along ‘scientific’ principles. As intended within the aims of science, intelligence analysis has been viewed as a means by which police resources can better understand how, when and why crimes are occurring (Bradley & Sedgwick 2009; Cope 2004; James et al. 2017; Manning 2001; Prunckun 2013). The process of the ‘rationalisation’ of policing (Manning 2001; O'Malley 2016) has shaped policing services to become more cost- effective by targeting limited resources towards identified risks (Ericson & Haggerty 1997; Knutsson 2017; Lewandowski, Carter & Campbell 2017; Maguire 2000; Scott 2017). In a broader historical sense, the development around ‘intelligence-led’ policing emerged from a significant shift from reactive policing measures to a more proactive approach (Evans & Kebbell 2012; Gill 2000; Johnson 2000; Maguire 2000; Sheptycki 2009; Wall 2007). This arguably represents an alignment of law enforcement practices with the broad aims of science, particularly regarding attempts to develop a clearer understanding of the underlying principles (perceived or real) to recognise and shape the security environment.

Education and training The ‘art’ vs. ‘science’ debate contains a subset of the more general question regarding the type of education and training intelligence analysts should receive (Dahl 2012; Gormley 2004; Hart & Simon 2006, pp. 35-60; Modern Intelligence Analysis 2011). One of the educational reforms in intelligence analysis within the US, specifically within the CIA, was the creation of the Sherman Kent School and the Career Analyst Program. The School was designed in mid-2002 to teach new analysts methods and techniques to improve the production of more accurate analysis (Davis 2002b; Wheeler 2015). This was the first comprehensive training program

for intelligence analysts in the US (Davis 2002b). A core focus of this program has been developing more systematic approaches to analysis, ‘by structuring their analysis more along the lines of the scientific method’ (Marchio 2014; Marrin 2003a, p. 609).

The former Director of the CIA George Tenet described the importance of an analyst’s expertise coming down to the right training and education, combining rigor and insight to produce sound analysis (Tenet 2000). The analyst’s insight comes from their capacity to adhere to procedural and disciplinary expertise (Kreuzer 2016; Lowenthal & Marks 2015; Mudd 2015). Analysts are guided by the logic of induction and deduction in this process (Marrin 2003a, p. 623).

With respect to the education and training of analysts and aligning such processes with scientific professions, some intelligence scholars have argued that intelligence analysis can benefit from learning from the medical field and the adoption of similar models of diagnosis (Kerbel 2008; Manjikian 2013, p. 1). Richards Heuer, a preeminent figure within the intelligence field, pointed towards the medical field as a profession that could be emulated. As he states (Heuer 1999, p. 62), the doctor observes the symptoms of the patient, and by using their specialised knowledge of the body, a hypothesis is generated to explain such observations, followed by tests to collect further information to evaluate the hypothesis and make a diagnosis. This medical analogy focuses attention on the capacity to correctly identify and evaluate all plausible hypotheses. In this sense, collection is focused narrowly on information that could reveal alternative hypotheses. As Heuer stated, ‘While analysis and collection are both important, the medical analogy attributes more value to analysis and less to collection than the mosaic metaphor’ (Heuer 1999, p. 62). The rationale for large technical collection systems has been explained as being a consequence of the ‘mosaic theory of intelligence’, in which a clearer picture of reality is derived from the assemblage of pieces of information into a ‘mosaic or jigsaw puzzle’ (Marrin 2003b, pp. 40–59). The medical analogy attributes more value to analysis and less to collection, compared to the mosaic metaphor (Heuer 1999, p. 62).

Whilst it has been acknowledged that both the intelligence and medical professions hold themselves to high rigorous standards for the collection and analysis of information and data (Fingar 2011a, p. 4; Manjikian 2013, p. 564), the argument has been made by some intelligence scholars that medicine is more of an art than a science (Laqueur 1983). Laqueur observes the similarities in the analytical processes.

…the student of intelligence will profit more from contemplating the principles of medical diagnosis than immersing himself [sic] in any other fields. The doctor and the analyst have to collect and evaluate the evidence about phenomena frequently not amenable to direct observation. This is done on the basis of indications, science, symptoms… The same applies to intelligence (Laqueur 1983, pp. 534- 535) The medical diagnostic practice of identifying, collecting, analysing and disseminating is arguably similar to the intelligence cycle (Converse & Pherson 2008, p. 1). Marrin and Clemente further claimed that both the medical and intelligence professionals apply similar general approaches to acquire information (Marrin & Clemente 2005, p. 709). Once the analyst has collected sufficient data and information this is followed by applying relevant disciplines, such as military science, psychology, economics and so forth, to better understand the implications of those changes. Such a process, according to Marrin and Clemente, is similar to the process physicians utilise when diagnosing patients (Marrin & Clemente 2005, p. 711).

Another similarity between the medical field and intelligence analysis is the challenges both face in integrating the specialists’ specific diagnosis or assessment into the broader context (Marrin & Clemente 2005, p. 713; Oliver 2007; Wirtz 2014a). It has been acknowledged that intelligence analysts and medical professionals deal with similar problems, ranging from alternative hypotheses to disconfirming evidence (Pherson 2008, p. 1). Deductive and inductive reasoning are utilised by both fields to navigate through such problems, of distinguishing relevant information (signals) from irrelevant information (noise) (Marrin & Clemente 2005, p. 715; Marrin & Torres 2017; Prunckun 2015).

Underlying the perceived similarities between intelligence analysis and the medical profession is the belief that as more information is collected, the practitioner will become more confident in their assessment (Treverton 2009, p. 40). Yet even within the medical field, there is an acknowledgement from several memoirs and studies that medical diagnosis is not necessarily always systematic and the accuracy of diagnoses is not necessarily increased with more collected information and data (Groopman 2007, p. 36; Mol 2002; Schaler, Lothane & Vatz 2017). There is some recognition within some quarters of the medical profession that the process of diagnosis involves considerations that are not characteristically ‘scientific’ or structured, but concern the doctor’s bias, craft skills and what Polanyi would refer to as their ‘personal knowledge’. The post-structuralist Michael Foucault presented a similar argument, that the work of the medical doctor is influenced by their surrounding culture, to the extent to which they do not ‘discover’ the truth ‘out there’ but rather assemble it together in their mind, which is partly a product of their environment (Manjikian 2008, p. 335). It is overly simplistic to understand the work of the doctor or the intelligence analyst as neutral observers who merely collect and analyse the ‘facts’ (Manjikian 2013, p. 571).

Those scholars who promote the medical profession as a field to emulate refer principally to the perceived way the physician engages in a structured approach to their work – which lends itself to making it easier to teach others (Coulthart 2016; Folker 2000, p. 14; Hoffman et al. 2011; Marrin & Torres 2017; Moore, Krizan & Moore 2005). Polanyi, who completed training as a physician, argued against this idea that the medical profession is highly structured and the learning of this field can be easily or even optimally transmitted by explicit knowledge via rules and procedures (Doing 2011; Fennell 2016; Johnson 2016; Nye 2011; Polanyi 1962a). Polanyi provided examples of this, such as the situation in which the physician may be able to distinguish epileptic seizures from other types of seizures, but they are not necessarily capable of completely articulating how they know this (Polanyi 1961, p. 4). This is akin to the

dilemma facing the analyst as to deciding which particular analytical methods are best suited to solving specific types of problems (Criminal Intelligence: Manual for Analysts 2011; Heuer 2005, p. 75; Heuer 2008a, pp. 7-10; Rodgers 2006, p. 623). As Stephanie Lawson observed, the ‘facts do not speak for themselves’, but require the individual’s knowledge to make sense of all the pieces of information together in context (Lawson 2008, p. 584). Cooper’s study into the analytical culture in the US intelligence community observed that professions such as medicine and law refer to themselves as ‘practices’, indicating that the roots of such systems (ethics, ethos, skills) are values and skills best transmitted by people and learned from practice (Cooper 2005, p. 28). Even within scientific fields, including medicine, there remains the important feature of learning from other practitioners in that local tradition. Specifically regarding the knowledge and skills which are transmitted and understood in a ‘tacit’ or non-explicit fashion, as a path to developing expertise in that field (Brock 2015; Henry 2010; Quarmby & Young 2010; Scheffer, Baas & Bjordam 2017; Vogel 2013a).

Conclusion Importantly, studies into how scientists perform their activities, from research to analysis, is generally characterised as being less ‘systematic’ and more of a ‘bricolage’ approach (Beveridge 2017; Garfinkel, Lynch & Livingstone 1981; Scheffer, Baas & Bjordam 2017; Smedslund 2016; Turpin, Garrett-Jone & Rankin 1996). The ‘bricoleur’, as Lynch and Woolgar have argued (1990), essentially improvises with a ‘mixed bag of tools’ as a means of dealing with a variety of contingencies and situations which make experiments work ‘as they should’. In this sense, even within the realms of science there are aspects of ‘art’ or ‘artistic’ judgment. Taken together, since the work of the intelligence analyst is at times ‘messy and contingent’ (Dahl 2017; Davis et al. 2015; Herbert 2013; Innes, Fielding & Cope 2005, p. 51; Lowenthal & Marks 2015), rather than being ‘systematic’ and ‘logical’, it would be perhaps apt to understand the activities of analysts as that of an ‘artistic enterprise’, involving aspects of both ‘art’ and ‘science’ (Bang 2017; Beveridge 2017; Corkill 2011, p. 9;

Hicks 2013; Jensen 2018; Manjikian 2013; Mudd 2015; Phythian 2017; Richards 2010; Walsh 2017; Wirtz 2014a). Whilst there has been a sustained interest with regard to exploring the nature of intelligence analysis, there is no universal consensus regarding how best to comprehend the discipline. In particular, there are gaps in the literature with respect to the epistemological dimensions of intelligence analysis, in terms of both the process of analysis, and the products, or what it means to ‘know’ something. Polanyi’s views regarding how scientists engage in solving problems will serve to contribute to ways for epistemologically discussing and understanding the process and product of intelligence analysis.

Chapter 2: Exposition of Polanyi

Introduction According to some intelligence practitioners and scholars (Folker 2000, p. 13; Kerbel 2008; Lahneman 2010b; Marrin 2012a; Moore, Krizan & Moore 2005, p. 211; Wirtz 2012), ‘intelligence analysis’ can be characterised as engaging elements of both ‘art’ and ‘science’. Unfortunately, intelligence literature does not represent a rich understanding of science, nor much guidance on the optimum mixture of the ‘art’ and ‘science’ features (Artner, Girven & Bruce 2016; Coulthart 2016, p. 39; Jensen 2018; Johnson 2003; Pherson 2013). Such a formula may or may not exist for science or intelligence analysis and this thesis is not concerned with exploring what this combination could optimally be. The focus of this thesis is to examine the way Polanyi’s standpoint of science can contribute towards the discourse within intelligence analysis regarding both the process of solving problems and a clearer understanding of what it means to ‘know’ something as a knowledge product. This chapter does not aim to describe all the features included in Polanyi’s view of science. To do so would require a thesis entirely dedicated to his thought (Johnson 2016; Kane 1982; Kiyimba 2009; Peck 2006). The aim of this chapter is to examine Polanyi’s views into the nature of science, in particular his concepts of ‘personal knowledge’ and ‘tacit knowing’, indicating the bearing this has for the intelligence analysis profession. Polanyi’s argument that the skills and expertise involved in the process of medical diagnosis is ‘… as much an art of doing as it is an art of knowing’ (Polanyi 1962a, p. 54) draws attention to the importance of sufficiently acknowledging tacit knowing in the problem-solving process. The treatment Polanyi gives to the relationship between explicit and tacit knowing, along with considerations regarding ‘practical knowledge’ and a ‘skilful performance’, provides a position that contributes to the discourse within intelligence analysis regarding the process of solving problems. Polanyi’s concept of ‘personal knowledge’ contributes towards a more nuanced epistemological

framework for explaining what it means for analysts to ‘know’ intelligence products.

Whilst it is not within the scope of this thesis to discuss in depth the social aspect of tacit knowledge, as understood by Polanyi, it is worth briefly acknowledging this feature. Largely because of its mode of acquisition, tacit knowledge is deeply personal. Individuals develop and acquire their tacit knowledge from practice and from emulating others, such as within a master-apprentice relationship. Beyond the personal dimension of tacit knowledge is the communal or local aspect of this form of knowledge. Tacit knowledge is not exclusively possessed by individuals, but also contained within and across social groups. Intelligence products generated by intelligence agencies or law enforcement typically involve large teams of people whom contribute to the tacit knowledge of the group. The final intelligence product should not be understood as an outcome of a sole individual but the group as a whole, the function of which is partly the contribution of social tacit knowledge. Several studies have outlined the local or social aspect of tacit knowledge (Cambrosio & Keating 1988; Collins 2001; Kaiser 2005; MacKenzie & Spinardi 1995; McNamara 2001; Ouagrham-Gormley & Vogel 2010). The creation and production of social tacit knowledge, along with the transferring of such knowledge within a local tradition, is dependent on social connections between individuals and groups (Ouagrham-Gormley 2014, p. 26). Polanyi does offer some arguments regarding the optimum conditions for individuals to develop these social connections across groups as a way for practitioners to draw on and contribute towards the reservoirs of social tacit knowledge (Fischer & Mandell 2009; Gulick 2016; Nye 2011; Polanyi 1962a, 1962b). It has been acknowledged within the intelligence field that knowledge itself is partly socially/organisationally ‘constructed’, which explains sometimes the tensions between how different agencies represent knowledge in diverging ways (even when based on the same or similar evidence or knowledge) (Hunter and MacDonald, 2017; Ingold, 2000; Jensen, 2018; Jervis, 2010). The same can also be observed regarding how knowledge is partly

politically/socially/organisationally constructed within the scientific community (MacKenzie, D & Spinardi, G 1995; Martin, B 2012; Massingham, P 2016; Oliver, WR 2007; Ouagrham-Gormley, SB 2014). However, it is beyond the scope of this thesis to elaborate on the socio- political aspects of tacit knowledge and how Polanyi’s concepts that deal with this dynamic have a bearing on aspects of intelligence analysis.

Discovery as the aim of scientific research Within the realm of intelligence, epistemological considerations are a key focus regarding the accuracy and reliability of assessments and judgments (Bang 2017; Dreisbach 2011, pp. 757-792; Herbert 2006, pp. 666-684; 2013; Lillbacka 2013; Vrist Rønn 2014). Such considerations include an interest in exploring whether intelligence analysis is a science or an art, or some combination of the two. Polanyi’s view of science can shed light on this discussion. What are the main features of science, according to Polanyi? In his view, the aim of scientific research is the discovery of new facts and laws in the world. For Polanyi, the truth is an objective condition and finding the truth is achieved by the correspondence of a theory to an objective reality (Jacobs 2001, p. 464). Polanyi dismisses cognitive relativism, or the relativity of reality based on our perception (Polanyi 1962a, pp. 315–16). Polanyi’s outlook of science should not be misconceived as being ‘constructivist’ – the idea that reality is meaningless and we ‘construct’ the value for it ourselves. Polanyi was convinced that there was an objective reality ‘out there’; yet, in order to make it intelligible we must attempt to ‘… establish and make our own’ interpretation and meaning (Polanyi 1966b, p. 80). This underlying engagement of the personal act within the process and product of knowing will become clearer in later discussions of his concept of personal knowledge. In Science, Faith and Society (Polanyi 1964, pp. 22-25) he explained discovery by way of simple analogy: ‘Suppose you wake up at night. Curious noises are noticed; speculations about wind, rats, burglars, follow’ (Polanyi 1964, p. 23). Eventually one might decide the best speculation/hypothesis is that the sounds are those of a burglar. By ‘drawing together various indications’ (foot falls, for example) the theory

(representing the discovery) of the burglar is confirmed (Polanyi 1964, p. 23).

According to Polanyi, the process of discovery gets under way

the very moment when, certain impressions being felt to be unusual and suggestive, a problem is presenting itself to the mind; it continues with the collection of clues with an eye to a definite line of solving the problem; and it culminates in the guess of a definite solution (Polanyi 1964, p. 25). For Polanyi, the process of discovery is an intimate blend of sense experience and imagination, of observing, hearing, touching and hypothesizing. There is no pure observation as such. It is important to acknowledge that this view of science is not universally shared by scientists or philosophers of science, as previously observed in the Introduction. Polanyi’s views of the practice of science challenge conventional thinking around the Positivist position on science and the idea of a ‘scientific method’ (Kelleher 2008/2009, p. 8; Creswell & Poth 2017; Johnson, 2006a, p. 224; Mackenzie, 2011, pp. 543-46; Manjikian, 2013, pp. 563-67; Ryan, 2015, pp. 417-31). It is precisely because Polanyi offers a provocative approach for understanding these epistemological issues which can serve to promote discussion within the discourse of intelligence analysis.

For Polanyi, there is hypothesizing behind the very act of observation (Polanyi 1951, p. 19). Polanyi cites examples to illustrate this. These examples include that of a child, when presented with a series of objects on a tray, will notice only those which they have some previous familiarity; another example involves the Fuegians, whom were visited by Charles Darwin from the Beagle, who noticed the small boats on the shore, but did not pay attention to the large ship (the largeness they were unfamiliar with), laying at anchor in front of them (Polanyi 1951, p. 19). Because there is hypothesizing behind the act of observation, for an observer to see an object in one way, for Polanyi, excludes the possibility of them seeing it in another way (Polanyi 1951, p. 19). In this sense, Polanyi’s understanding of how we perceive further articulates conceptually the acknowledgement within the intelligence field that the

function of theorising that goes behind and informs observations is key to what is perceived; for example, Wolhstetter’s argument that the Pearl Harbor attack was not perceived or anticipated by the American military because of the accepted theory that the Japanese would not pursue such a course of action (Wolhstetter 1962). In a similar spirit, the 9/11 Commission found the attacks were not prevented partly because of a ‘failure of imagination’ (Kean & Hamilton 2004, p. 336; Pherson & Beebe 2015; Ryan 2006; Wastell, Clark & Duncan 2006).

According to Polanyi, scientific investigations involve a perennial interaction of imagination and observation. Polanyi’s burglar example, considered above, serves to illustrate a further feature of discovery, being the way they are made. A series of empirical clues is presented such as curious noises – until one more clue is noticed, and this one proves to be decisive. The burglar theory is now established. The key here is guessing, and hopefully correctly, as to what theory best represents the subject in question. According to Polanyi’s outlook, this is a fundamental aspect of science.

Law enforcement and security agencies are aware that some aspect of guesswork is required in the process of problem-solving, since there are generally too many targets and ways of undermining security to systematically examine every single possible scenario (Gigerenzer & Brighton 2007; Ferguson 2012; Horan 2007; Kerr et al. 2005; Lerner 2007; Rosenbaum 2007). Whilst the intelligence field acknowledges that it is partly engaged in a game of guessing (Alschuler 2007; Bruce & George 2008; Colapietro 2011, p. 58; Frank 2012; Zafeirakopoulos 2014), the art of inquiry as understood by Polanyi provides a richer language and epistemological basis for recognising this aspect within both science and intelligence analysis. Polanyi’s perspective will be presently outlined.

Polanyi draws a distinction between two types of discoveries. There is a) assigning the pertinent clues and evidence to the most suitable (already existing) theory; and b) uncovering a new aspect of reality, a novel type of entity or law of nature that has previously been unknown. The intelligence analyst largely engages in the category (a) of making

discoveries (Lillbacka 2013; Manjikian 2014; Marrin 2011b). Aligning observations and empirical clues to suitable theories still present complex challenges to the analyst, even when the already existing threat type is known.

Importantly, in relation to the key role of making discoveries within science, Polanyi emphasises that such acts of ‘illumination’ should be viewed as being a ‘tentative discovery’ (Polanyi 1962a, p. 121; emphasis added), requiring testing and verification. This is because a discovery, in the sense that Polanyi means of such acts, may be shown to be mistaken once tested (Fennell 2016, pp. 45-6). Polanyi also noted that verification is a formal aspect of discovery (Fennell 2016, p. 47). Polanyi argued that ‘verification’ is a process which is a ‘commitment’ (Polanyi 1962a, p. 215) to developing a defensible case for having one’s own claims accepted by others (Polanyi 1962a, p. 181). Verification is building a case, based on collected evidence, to prove the truth of a theory. Falsification is using evidence (or its absence) to reveal the lack of truth of a theory. Scientists propose hypotheses that may explain their observations. Once they are generally accepted by the scientific community, the hypothesis becomes a ‘theory’, having passed sufficient tests and observations (Merriam-Webster n.d.). It is accepted in science and the intelligence fields that strictly speaking theories are never completely ‘proven’, since new evidence may come to hand which presents a more accurate theory (Agrell & Treverton 2015; Ellis-Smith 2016; Kerr et al. 2005; Prunckun 2013, 2015; Tang 2017).

Polanyi clarifies that in relation to the distinction between hypotheses and theories, the former being a possible proposition to explain a phenomenon, it is the latter which ‘… is the universal intent of a scientific discovery’ (Polanyi 1966b, p. 78). This is the fundamental goal of science broadly speaking. A similar ‘universal intent’ can be found also in the realm of intelligence analysis, which can be observed by the significant resources allocated to collecting and analysing data and information, as a means for discovering, verifying and understanding the objects in question. Such efforts to develop a clearer understanding of aspects of the

security landscape will allow for decision-makers to optimise their resources (Bammer 2010; CIA 2009; Eck, Clarke & Petrossian 2013; Fingar 2011a; Ratcliffe 2016; Weiss 2007; Wheaton & Richey 2015). Yet importantly there is the acknowledgement within the intelligence field that ‘theories’ cannot be ‘tested’ in the same definitive way that the hard sciences can (Cain 2012, p. 137; Evans & Kebbell 2012; George 2010; Hulnick 2006; Johnson 2010; Mudd 2015). In the realm of intelligence, this can be observed by the way the US intelligence community has generally used ‘practical experience’ rather than ‘formal validation methods’ for selecting and using analytical tools (Agrell & Treverton 2015; Coulthart 2017a; Johnson 2005, p. 72; Martin et al. 2011a; Wirtz 2012). This is partly because there are no universally accepted methods for validating such analytical tools and to what extent variables can be known and therefore tested in the intelligence context (Coulthart 2016; Folker 2000; Pherson & Beebe 2015; Townsley, Mann & Garrett 2011; Walsh 2012, p. 244).

On the psychological plane of making discoveries, Polanyi comments that most of the time the scientist is engaged in fruitless efforts, sustained by a passion that takes beating after beating for months on end. After much effort, the contours of shapes are revealed at times, which may suddenly reveal themselves to be sharp outlines of certainty, only to dissolve again with consideration of further experimental observations (Polanyi 1964, p. 16). Polanyi conceptually articulates epistemological and psychological challenges that are akin to those acknowledged within intelligence analysis. For example, intelligence assessments regarding Saddam Hussein’s alleged stockpile of WMDs, which turned out to be ‘dead wrong’ (United States Department of State 2005, p. 2), is illustrative of what Polanyi means by the challenges facing scientists epistemologically and psychologically. Such a view further articulates the extent to which, in relation to psychological considerations, the difficulties facing the scientist are similar to those facing the intelligence analyst, as has been generally acknowledged within the intelligence field (Agrell &

Treverton 2015; Corkill 2011; Hicks 2013; Manjikian 2013; Puvathingal & Hantula 2012; Tang 2017; Legrand & Vogel 2012).

No overarching ‘scientific method’ Drusilla Scott’s book Everyman revived: The common sense of Michael Polanyi (Scott 1985, p. 30) captured Polanyi’s argument that scientists ‘cannot work to rule’. To what aspect(s) of Polanyi’s view was she referring? Polanyi denies there is any such thing as ‘the scientific method’, which is to say there is no single set of method rules that scientists have to follow in order to do proper scientific research. Scientific theories (Polanyi 1952, pp. 218-219) cannot, says Polanyi, be discovered by scientists following any set rules and nor can they ‘be verified nor falsified by experience according to any definite rule’. According to Polanyi’s view, ‘Discovery, verification and falsification proceed [according to certain] …maxims which cannot be precisely formulated’, and their application ‘relies in every case’ on the scientist, who decides which maxims to follow, being something they have to work out for themselves (Polanyi 1964, p. 28). There are no definite rules for deriving the truth of a scientific proposition from observational data via a process of verification (Polanyi 1964, p. 29 ).

In The Logic of Liberty, Polanyi (1951, p. 26) observed that the transmission of ideas and concepts is mostly not by precept but by example. As Polanyi observed, ‘The whole practice of research and verification is transmitted by example and its standards are upheld by a continuous interplay of criticism within the scientific community’ (Polanyi 1951, p. 26). Even in science, Polanyi claims, there is no textbook that even attempts to teach the way discoveries are made, nor what evidence should become accepted as substantiating a discovery.

Polanyi's account of science contrasts that of Karl Popper, an important figure in the philosophy of science. Popper’s firm belief is that scientists are distinguished by their rigorous use of criticism and self- criticism (Popper 1959). This is the view known as falsificationism (Polanyi 1962a, pp. 63, 64, 167). The scientist’s task, according to Popper, is to

use criticism against a hypothesis, looking for evidence that can falsify the hypothesis. Polanyi rejected Popper’s view of science, and he seems to have had Popper’s view in mind when he wrote in his 1952 essay The Stability of Beliefs (Polanyi 1952, p. 227) that the scientific method is often discussed in terms of ‘caution’, which Polanyi takes to be another name for scientists’ use of criticism and self-criticism. That scientists use caution, proceed cautiously and critically, is a widespread view, Polanyi suggested, but he insisted caution is not unique to science. It does not distinguish science from other disciplines. As Polanyi observes,

But the exercise of special caution is not peculiar to the scientist. The practice of every art must be restrained by its own form of caution. The precision of the lawyer, the poet's fastidiousness, the sculptor’s touch, are as many restraints by which these various professions are guided (Polanyi 1964, p. 227). Furthermore, there is no rule to tell scientists ‘the moment of deciding the next step in research of what is truly bold and what is merely reckless’ (Polanyi 1964, p. 227). The dilemma facing the scientist, Polanyi contends, is that they cannot rely on any set ‘scientific method’ for necessarily guiding them in their investigations.

Whilst it is acknowledged by both Polanyi and Popper (Polanyi 1952, pp. 15-16), along with the intelligence field generally, that ‘science’ as an enterprise involves more than the accumulation of ‘facts’ (Intelligence analysis in the 21st century 2016; Modern Intelligence Analysis 2011; Phythian 2017; Picornell 2013; Townsley, Mann & Garrett 2011), Polanyi offers an alternative way for representing the activities of scientists. It is in this manner that Polanyi’s view of science contributes to the discourse on understanding the process and product of intelligence analysis. The intelligence field acknowledges that the aim of intelligence analysis is to provide assessments, which should not be confused with ‘facts’ (Cook & Smallman 2008; Coulthart 2017a; Parliamentary Joint Committee on Law Enforcement 2013, p. 22; Hare & Coghill 2016; Pfautz et al. 2006; Stohl 2012; Warner 2017), especially since assessments cannot impart certainty (Butler 2004, p. 15; Parliamentary Joint Committee on Law Enforcement 2013; Kean & Hamilton 2004; Ryan 2006; Wastell,

Clark & Duncan 2006). Polanyi emphasises that what is not sufficiently acknowledged within knowledge claims more generally (beyond science itself) is the key role of the personal participation of the practitioner in the act of making judgments within their research and analysis (Polanyi 1962a, p. 20). Polanyi explains this by way of examining how scientists routinely dismiss evidence and facts as ‘anomalies’ if such observations contradict their hypothesis.

Polanyi argued that, despite the traditional view of science, it is not the case that theories are invalidated by discrepancies with observed data and theoretical prediction. Such discrepancies are often classed as ‘anomalies’. As Polanyi observed, variations to planetary motions, as noted over 60 years preceding the discovery of Neptune, were disregarded as anomalies by most astronomers, with the hope that in the future there would be a theory that could accommodate these variations without the need to significantly revise Newtonian gravitation. Polanyi claims that the reverse can also be true, namely that a series of observations which at one time were considered important facts may later be discredited ‘without ever having been disproved or indeed newly tested, simply because the conceptual basis of science had meanwhile so altered that the facts no longer appeared credible’ (Polanyi 1962a, p. 309). The personal act of judgment forms an essential aspect of science (Polanyi 1962a, p. 20). This view of science contributes towards the discourse within intelligence analysis by drawing attention to the important aspect of the personal dimension concerning knowledge claims.

As outlined in Chapter 1, the intelligence enterprise has clearly indicated a sustained interest for aligning itself professionally with the ideals of science (Cooper 2005; Coulthart 2017a; Gill 2009; Innes, Fielding & Cope 2005; Kent 1949; Marrin 2011b, 2017; Marrin & Clemente 2005; Pherson & Beebe 2015; Prunckun 2015; Spielmann 2014; Wirtz 2014a). Importantly, this includes the ideal within science of ‘falsificationism’, along with the role of empiricism and the logic of induction involved in the process of making discoveries and solving problems. Polanyi challenges the extent to which these ideals of falsificationism, empiricism and

induction play in the process of making discoveries and solving problems. This will be examined in greater detail both in Chapter 3 in terms of understanding the process of solving problems with respect to intelligence analysis, and Chapter 4 with regards to the intelligence product or knowledge claims. A more in-depth explanation of Polanyi’s understanding of tacit knowing will clarify precisely what epistemological considerations are involved in the process of solving problems facing the scientist and intelligence analyst alike.

Tacit knowing Polanyi argues that to account for the process of solving problems and making discoveries there needs to be sufficient recognition of the important role of tacit knowing and the relationship this knowledge has with explicit knowledge. According to Polanyi, this form of tacit knowledge is less testable than is explicit, propositional, formulated knowledge. Yet it is this tacit knowledge that serves as a guide to help the practitioner, even if only in an unclear fashion, with skilfully performing tasks. Popper refers to this knowledge as a 'faith in quite hazy ideas' (Popper 1959, p. 16) and, like other philosophers of science have, he omitted it from his analysis of science (Scott 1985, p. 46). In contrast, Polanyi locates this form of knowledge, what he refers to as ‘tacit knowing’, as an essential element of science and his epistemology. It has been recognised by some that the intelligence profession has largely adopted Popper’s characterization of science (Agrell & Treverton 2015; Coulthart 2017a; Manjikian 2013; Prunckun 2015; Tang 2017, p. 666; Townsley, Mann & Garrett 2011). Arguably, this partly explains the lack of acknowledgement and depth of discussion regarding the tacit aspects of knowledge within the intelligence domain.

Polanyi builds part of his argument for tacit knowledge as a legitimate form of knowledge by extending upon some aspects of Gestalt psychology, of the notion that the habit of the mind is to integrate parts of the visual field into a whole in order to understand the order of the world (Burley & Freier 2004, p. 324; Kałamała et al. 2017, p. 1; Wagemans

2013, p. 1219). In an epistemological sense, Polanyi presents an alternative argument to the conventional view that to ‘know’ can only be defensible if it is explicit or propositional knowledge (Doing 2011; Fennell 2016; Fothe 2012; Nye 2011; Turner 2014). Tacit knowledge does not conform to the traditional definition of ‘knowledge’ as being ‘justified true belief’ (Turner 2014, p. 33). This is because for ‘knowledge’ to be considered ‘justified knowledge’, such knowledge claims must be made articulate by reason (Bang 2017; Marrin & Torres 2017; Prunckun 2015; Turner 2014, p. 35; Wirtz 2014b). Polanyi argued that ‘tact knowledge’ can still be a valid form of knowledge and can extend beyond the explicit, without needing to unnecessarily privilege propositional knowledge (Apczynski 2014, p. 21; Grant 2007; Tebble 2016; Turner 2012).

A cornerstone of Polanyi’s theory of knowledge in general, and scientific knowledge in particular, is what he referred to as tacit knowing. In the words of one scholar, tacit knowing comprises ‘a range of conceptual and sensory information and images that can be brought to bear in an attempt to make sense of something’ (Hodgkin 1992, p. 15). Polanyi explored this topic of tacit knowledge to a significant degree. His most detailed and clearest analysis of this form of knowing occurs in his book, The Tacit Dimension, where he epitomised in the idea of tacit knowing and knowledge in the epigram, ‘We can know more than we can tell’ (Polanyi 1966b, p. 4). By this, Polanyi means that there are two types of knowledge, being explicit and tacit. Explicit knowledge, or propositional knowledge, covers facts about ourselves and the world that we are fully conscious of, such as the capital of Australia being Canberra, red being a colour, and gravitation being a theory that explains why objects fall to the ground when released from above. Yet propositional knowledge does not exhaust all the knowledge that we possess. While we can easily communicate this type of knowledge, which is one of its aspects, we also know things tacitly, which is knowledge we cannot (at least not completely) articulate (Jha 2011; Johnson 2016; Kiyimba 2009; Ray 2008; Tebble 2016, p. 20). Such knowledge includes how we use a language or understand a mathematical equation, which is at times difficult or

impossible to communicate it to others and codify (Ouagrham-Gormley 2014, p. 36). This discussion of tacit knowledge may appear somewhat arcane – but it is not. The tacit-explicit dimension of knowledge is considered one of the most widely explored topics in the field of Knowledge Management (Garrick & Chan 2017; Giurgiu, Barsan & Mosteanu 2015; Grant 2007, p. 174; Martinez-Conesa et al. 2017; Mohajan 2016; Turner 2012), and this area of study can be observed within intelligence literature (Fishbein & Traverton 2004; Lahneman 2010a, p. 616; Ouagrham-Gormley 2014, p. 36; Tang 2017; Taylor et al. 2013; Vogel 2013b). When the tacit-explicit dimension is explored within the intelligence field, it is largely related to discussions regarding the development and transfer of ‘know how’ knowledge, (Alschuler 2007; Cooper 2005; De Franco & Meyer 2011; Dennis 2013; Margolis et al. 2012; Ouagrham-Gormley 2014; Taylor et al. 2013; Turner 2012; Vogel & Dennis 2012). Despite these discussions regarding the tacit-explicit dynamic, the intelligence field does not sufficiently acknowledge ‘tacit’ knowledge in the process of analysis (Dennis 2013; Kamarck 2005, p. 12; Margolis et al. 2012, p. 2; Ouagrham-Gormley 2014; Tang 2017; Vogel 2013a, 2013b).

Polanyi is not claiming all knowledge is tacit knowledge. He acknowledges the distinct character of explicit or propositional knowledge. Explicit knowledge, Polanyi argues, can be observed in the formulation of scientific theories, experiments, laws of nature in science textbooks. It may be possible to express partly what was previously tacit knowledge (Garrick & Chan 2017; Grant 2007; Jones 2009; Von Krogh, Ichijo & Nonaka 2000). But Polanyi emphasises that tacit knowledge can never be fully expressible in words or diagrams. Polanyi argues that this is acknowledged implicitly by the fact that universities devote much effort to teach students in practical classes to identify various specimens of rocks, plants and animals, because propositional knowledge is limited in this regard to some degree (Polanyi 1966b, p. 5). A glance at contemporary university courses in medicine clearly reveals this acknowledged understanding in the value of practical classes for developing expertise.

Such claims are supported by the widespread recognition within the discipline of psychology that most of what we know we cannot articulate with words (Galotti 2017; Keil & Wilson 2000; Kingstone, Smilek & Eastwood 2008; Varela, Thompson & Rosch 2017). The efforts by universities to teach practical classes, Polanyi argued, partly accounts for this important aspect of tacit knowing, despite the fact such knowledge cannot be fully expressed or codified. Polanyi highlighted the somewhat paradoxical nature of the relationship between reason on the one hand and tacit ways of knowing on the other (Aligica & Tarko 2012; Fennell 2016; Kiyimba 2009; Meek 2011; Mullins 2013; Tebble 2016, p. 22). As will become clearer in discussing his understanding of tacit knowledge, this acknowledgement importantly becomes further clarified in his exposition of ‘practical knowledge’.

The historian Mary Jo Nye outlined the way Polanyi emphasised the practice of science. For example, Polanyi highlighted considerations regarding the building of laboratories, developing new techniques, amongst other issues confronting scientists of a practical nature within their activities (Nye 2011). Nye provides some historical context to this contribution of Polanyi to the field of science, stating that most scientists who reflected on the philosophy of science in the 19th and 20th centuries generally described the fundamentals of scientific knowledge, specifically in regarding ideas and theory. As Nye observed, ‘They did not emphasize practical skills and laboratory routines’ (Nye 2011, p. 43). Polanyi differed significantly in this regard, focusing much of his attention on the practical skills of the scientist in their pursuit to make discoveries and solve problems. Polanyi was not interested in promoting an idealist outlook of science, rather a more grounded view of the activities of scientists. These views of Polanyi regarding tacit knowing, conjoined with his ideas of ‘practical knowledge’, formed the basis for understanding the process of perception and the logic of making discoveries. The fashion by which discoveries are made, along with how problems are solved, according to Polanyi, was a telling indication or clue for the nature of how we perceive in general. As Polanyi stated, ‘The logic of perceptual integration may

serve therefore as a model for the logic of discovery’ (Polanyi 1969, p. 139). Polanyi builds upon this argument around the nature of perception and the process of solving problems, by discussing in-depth epistemological considerations around ‘practical knowledge’ and ‘skilful practice’.

Practical knowledge and skilful practice To better understand this bridge Polanyi viewed between the logic of discovery and that of perception, it is necessary to explore his way of understanding the skilled practitioner and the role of ‘practical knowledge’. The practitioner, according to Polanyi, possesses ‘practical knowledge’ of a tacit nature, which allows them to perform skilfully in their field. Yet, agents with knowledge of these practices would agree that many particulars of the skills involved in these practices are unspecifiable. The agents may have forgotten them, or they may never have been conscious of them (Polanyi 1958, p. 52). Polanyi states that there are many traditional industries and professions, including medicine, in which the agents have made judgments ‘without any clear knowledge of the constituent detailed operations’ engaged in the practice (Polanyi 1958, p. 52). Polanyi writes:

To percuss a lung, is as much a muscular feat as a delicate discrimination of the sounds thus elicited. The palpation of a spleen or a kidney combines a skilful kneading of the region with a trained sense for the peculiar feeling of the organ’s resistance (Polanyi 1969, p. 126). In other practices, such as wine tasting, there is less of a requirement to know how to create such goods involving ‘skilful practice’, and more of an emphasis on connoisseurship, for judging the quality of such products. In either type of practice, of skilfully performing a medical procedure (the act of doing), or of skilfully judging a wine (connoisseurship), there is the engagement of tacit knowledge, according to Polanyi. For either the wine connoisseur or the surgeon, the practitioner observes a set of rules that the agent may not be consciously aware of (Polanyi 1958, p. 49). Importantly, the practitioner observes rules and maxims in the tradition, but such rules do not determine the correct

performance of that activity or the mastery of that practice (Polanyi 1958, p. 50). The practitioner who can carry out such practices embodies the ‘practical knowledge’ Polanyi refers to. Such knowledge is akin to what Aristotle referred to as phronesis or ‘practical knowledge’, referring to the ability to apply a given rule to the correct situation (Lowney 2011, p. 31). Similar to Polanyi’s concept of ‘practical knowledge’, phronesis constitutes a bottom-up ‘practical’ knowledge, the exercising of which cannot be reduced to propositional rules.

With respect to Polanyi’s argument that the practitioner can engage in a ‘skilful performance’ without being aware of the precise rules for doing so, does not necessarily mean there are no such rules. Polanyi is emphasising that the practitioner does not need such propositional knowledge in order to perform the skilful actions (Jones 2009, p. 5). Recognising the face of a person we are familiar with generally occurs without our conscious explicit knowledge of how we come to such recognition, because it largely escapes verbal articulation (Polanyi 1966b, p. 4). Polanyi also cited the case of a psychiatrist who revealed to his students a patient who was having a fit, later discussing in class whether it had been an epileptic or a hysteron-epileptic seizure. The physician noted, ‘Gentlemen,’ he said, ‘you have seen a true epileptic seizure. I cannot tell you how to recognize it; you will learn this by more extensive experience’. As Polanyi observed regarding this exchange, the psychiatrist understood how to recognise the disease, but he could not communicate precisely how he knew such things. The psychiatrist recognised the disease by attending to the entity as a whole, and doing so by relying on a variety of clues which he could not clearly speak of or specify with propositional or explicit knowledge (Polanyi 1961, p. 4). Yet this lack of articulation does not prevent or interfere with the skilful performance (Polanyi 1962a, p. 51). Examples from the medical profession are particularly pertinent, given the acknowledgement within the intelligence field by some that ‘medical diagnosis is similar to intelligence analysis’ (Cook & Smallman 2008; Corkill 2011; Kent 1949, p. vii; Laqueur 1983; Marrin 2017, p. 543; Oliver 2007; Rodgers 2006; Wirtz 2014a). This acknowledgment of the way we

may know things but cannot clearly articulate them, along with how we perceive things tacitly, is extended by Polanyi in his treatment of the two main ‘terms’ of understanding tacit knowing; broadly speaking, what he described as ‘from-to’ epistemological orientation, involving the tacit powers of integration.

According to Polanyi, tacit knowing can be understood more clearly with reference to ‘the two terms of tacit knowing’ which invariably conjoin together in the act (Polanyi 1966b, p. 9). There is a) ‘proximal’ knowing, which is perceiving something in isolation, or ‘knowing by relying on’ (which is a subsidiary awareness), and b) ‘distal’ knowing or ‘knowing by attending to’ (which is a focal awareness), by attending to or integrating particulars of which they contribute to the whole. The ‘proximal’ term consists of observing objects in isolation, and the ‘distal’ term refers to seeing those same things as a coherent entity (Polanyi 1969, p. 140). In tacit knowing, according to Polanyi, we are always attending from the proximal to the distal (Polanyi 1969, p. 141). This process of ‘from-to’ amounts to revealing the meaning of the object(s) in question. According to Polanyi, ‘We may say that a scientific discovery reduces our focal awareness of observations into a subsidiary awareness of them, by shifting our attention from them to their theoretical coherence’ (Polanyi 1969, p. 140). A person trains their attention on one thing, as for example the scholar trying to make sense of a difficult paragraph of text or an investigator developing an understanding of some evidence and clues. In each case, Polanyi would say, the subject is relying on ‘particulars’ (particular sounds, sensations, movements) ‘… for the purpose of attending to the…’ interpretation as a whole. Knowing the ‘main drivers’ or the key aspects of a problem is an important aspect of intelligence analysis (Cain 2012; Dahl 2012; Ellis-Smith 2016; Gentry 2016; Herbert 2013; Mudd 2015; Vandepeer 2016). But how these pieces of the puzzle are brought to bear on the overall meaning is Polanyi’s contribution to the discourse on intelligence analysis. Polanyi elaborated on this as a dynamic process, known as tacit knowing, involving these two ‘terms’: moving from the proximal to the distal, the latter of which ‘is specifiably

known’ (Polanyi 1966b, p. 9). It is precisely Polanyi’s concept of tacit knowledge and how it relates to skilful practice that has a bearing on understanding similar processes and considerations of the intelligence analyst.

Polanyi’s concept of tacit knowledge is expanded upon in his treatment of the ‘dynamic structure’ of this form of knowing involved in a skilful practice (Polanyi 1962a, p. 63). We perform a skill by relying on the coordination of elementary muscular acts, and we are aware of having exercised these correctly by accomplishing our skilful performance. We are aware of them in terms of this performance, and not (or only partially) aware of them in themselves (Polanyi 1962c, p. 601). In terms of the ‘dynamic quality’ of a performance (involving skilful practice), such a quality is lost if it is studied by its separate constituent parts (Polanyi 1969, p. 126). When we focus all of our attention on the proximal, we observe that object in isolation. According to Polanyi, the ‘dynamic structure’ of tacit knowing becomes apparent in the operations and interactions of the individual using their tools. The tool becomes operationally an extension of the individual. Within this ‘dynamic structure’, Polanyi claimed it is not feasible for the individual to focus simultaneously on the operations of the mind and the observations the mind generates. These two fundamentally different types of foci are mutually exclusive. It is not possible to attend simultaneously to the object of the action and the action itself, according to Polanyi. ‘This fact’, according to Polanyi, can be widely generalized.

Returning to the argument that we can know things without being able to communicate them completely (as to how we know them), Polanyi explains the relationship between our tacit and explicit knowledge with respect to how tacit coherence operates. According to Polanyi, we are able to impart what we know by attending to them proximally, but we are either uncertain or not conscious of those aspects we know distally. It is this latter form of knowledge which we rely on for attending to something else, which is the meaning of that knowledge (Polanyi 1962c, p. 601). For example, as I read the words on a page I am attending to something else, being the intended meaning of that knowledge. These two types of

awareness – of proximal and distal – are both fundamental in relation to the tacit coherence of an entity (Polanyi 1969, p. 140). The latter is characteristically tacit, since we cannot precisely specify what particulars are being relied on (since we are only subsidiarily aware of them) to attend to an entity as a whole (Polanyi 1962c, p. 601). This act of integration is what Polanyi means by tacit knowing (Polanyi 1969, p. 140; italics original).

Taken together, Polanyi’s concept of tacit knowing serves to bridge the link between perceptual coherence, involving ‘practical knowledge’. This relationship is fundamental to Polanyi’s contribution to science. In terms of understanding tacit knowledge with respect to the act of ‘illumination’ involved in making discoveries, Polanyi presents the argument in Personal Knowledge that such a process can be characterised as crossing a logical gap (Apczynski 2014; Fennell 2016, p. 46; Johnson 2016; Rutledge 2015). Yet in the later work of Polanyi in the Tacit Dimension, he downplays this notion of crossing a gap, emphasising instead the role of ‘tacit integration’. At the core of tacit knowing, involving an ‘informal logic of science’ (Polanyi 1966a, p. 155; original emphasis) is the exercising of our ‘powers of perceiving coherence’ (Polanyi 1966a, p. 139), which is an act of integrating an inference from proximally known clues to a distally known object as a whole (Fennell 2016, p. 46). Even Polanyi’s treatment of logic in the act of making a discovery is further illustrative of the practical processes involved by scientists, processes which are elusive and belie efforts to formalise or codify the so-called ‘scientific method’ (Agler 2011; Meek 2011; Nye 2011; Rutledge 2015, p. 12; Turner 2014).

Polanyi argued that tacit knowledge was an important feature involved in the ‘structure of skills’, clarifying how a skilful performance or task within a field can be conceptualised. Polanyi’s concept of the ‘structure of skills’ has three main epistemological implications: a) the practitioner, in the performance of their duties, is exercising skills based on rules or methods that they are not entirely aware of; b) the accepted ‘rules’ within the tradition, considered to be valuable for the skilful performance of

a task, do not determine the skilful performance necessarily within the individual practitioner; c) the ‘rules’ within the tradition can only be integrated with the ‘practical knowledge’ by way of the student practicing and submitting, observing and emulating the skilful performance of a skilled master. As Polanyi makes clear regarding the structure of skills, ‘Science is operated by the skill of the scientist’ (Polanyi 1962a, p. 51), representing the reality that it is from exercising their skill that they shape their scientific knowledge. As Polanyi observes, ‘We may grasp, therefore, the nature of the scientist’s personal participation by examining the structure of skills’ (Polanyi 1962a, p. 51). The underlying clue to this recognition of the ‘structure of skills’, as Polanyi understands it, is the concept that ‘the aim of a skilful performance is achieved by the observance of a set of rules which are not known as such to the person following them’ (Polanyi 1962a, p. 51). Polanyi gives the example of the cyclist and swimmer who generally are not aware of how they cycle or swim respectively (Polanyi 1962a, p. 51). On the other hand, it has been observed within policing studies, for example, that competencies for successfully reading deceptive behaviour, to perceiving how all the clues cohere together, involve a skillset beyond propositional knowledge (Carson 2009; Evans & Kebbell 2012; Lerner 2007; Taylor et al. 2013; Legrand & Vogel 2012). Polanyi’s in-depth treatment of tacit knowing provides a more detailed language for sufficiently explaining and understanding this important epistemological feature within such skilful tasks.

Tacit knowing and learning Closely related to Polanyi’s acknowledgment that the rules of an art do not determine the correct practice of that art, is his recognition and treatment of the tacit process of learning within a discipline. As Polanyi stated regarding scientific research, the rules of an art never

…determine [its] practice … they are maxims, which can serve as a guide to an art only if they can be integrated into the practical knowledge of the art. They cannot replace this knowledge (Polanyi 1962a, p. 52).

This is because the formulation of rules cannot completely list the types of situations, timing and correct application for all scenarios, as the actions require. Since the ‘skilful performance’ draws on the tacit knowing of the practitioner, such knowledge can only in part be learnt from masters in the field, according to Polanyi. Whilst this notion of the importance of learning from (ideally gifted) masters in the field has been acknowledged within the realm of intelligence analysis (Bruce & George 2008; Devine & Loeb 2014; Lillbacka 2013; Marrin 2017; Sinclair 2010; Walsh 2017; Wastell, Clark & Duncan 2006), Polanyi’s concept of tacit knowing provides an enriched way for the intelligence field to discuss and understand various epistemological considerations.

This acknowledgement regarding how the practitioner perceives things, of tacitly cohering all the pieces of the puzzle together, hints towards the important role of tacit knowing in this process of cognition. An important implication of this acknowledgement, according to Polanyi, was the primacy of students learning such tacit skills from a gifted master in the respective tradition. Polanyi’s understanding of a ‘master’ in a craft does not differ substantively with the view of a ‘master’ within intelligence analysis. The general understanding of the ‘master’ in the intelligence field is captured by David Shaw in his article Managing Analysis, writing that the ‘master’ is one who has been recognised by other masters in the field, principally by demonstrating an accomplished set of skills of a high standard (Cooper 2005, p. 6; Shaw 2007, pp. 39-40). Polanyi argued that some skills can only be communicated by example, not precept, requiring years of training under the tutelage of a skilled master (Polanyi 1962a, p. 54). It is pointless for the physician to be simply reading about descriptions of syndromes without learning how to clearly identify the correlated symptoms in a patient (Polanyi 1962a, p. 54). The significant periods of time students of medicine, biology and chemistry spend in practical courses demonstrates the degree to which the transmission of skills is communicated from master to student (Polanyi 1962a, p. 55). It further demonstrates the art of knowing remains unspecifiable at the heart of the sciences (Polanyi 1962a, p. 55). Such arguments have a bearing on

intelligence analysis by emphasising the need to sufficiently acknowledge that cognition involves tacit knowing and such a process of perception can be assisted by learning from gifted masters in the tradition.

Any skilled activity, Polanyi points out, is partly learned and acquired by training within a tradition. Whilst there is a place for textbook- based or formalised learning for a profession, such methods of communicating the ‘practical knowledge’ have limitations, since such tacit knowledge defies formulation. Polanyi emphasised that the student must submit to the teacher to learn the correct performance, but ultimately, the student must develop the ‘right feel’ for the performance by gaining the ‘knack of it’ (Polanyi 1969, p. 126). The ‘practical knowledge’, which is key to the skilful performance within a field, is not only self-acquired but also learned from, ideally, accomplished practitioners. The learning of this knowledge is partly gained by way of observation and emulation/imitation. Says Polanyi in Personal Knowledge:

To learn by example is to submit to authority. You follow your master because you trust his manner of doing things even when you cannot analyse and account in detail for its effectiveness. (Polanyi 1962a, p. 55) The student learns by observing the master, emulating their efforts by their example, unconsciously picking up the rules of the art – some of which are not explicitly known to the master. The same can be applied to the intelligence analyst. For example, James Wirtz, in his review of Heuer and Pherson’s published methodological handbook, Structured Analytic Techniques for Intelligence Analysis, states that despite the valuable recipes contained in the handbook for analysis, ‘…certain rudimentary skills are necessary to cook a soufflé. The novice also is unlikely to get the soufflé to rise without having first watched the chef bake one’ (Wirtz 2014b, p. 425). Such learning of these rules, according to Polanyi, can only be assimilated by the individuals who surrender themselves uncritically to one who has mastered the field. Polanyi states:

A society which wants to preserve a fund of personal knowledge must submit to tradition. In effect, to the extent to which our intelligence falls short of the ideal of precise formalization, we act and see by the light of unspecifiable knowledge and must acknowledge that we accept the

verdict of our personal appraisal, be it at first hand by relying on our own judgment, or at second hand by submitting to the authority of a personal example as carrier of a tradition (Polanyi 1962a, p. 55). According to Polanyi, tacit knowing constitutes an important feature of the knowledge contained within a tradition and as practiced within it. This has important implications for the teaching and learning in that respective tradition. Polanyi was convinced that the personal aspect of knowing and of skilful performance illustrates this. Scientific research, Polanyi observed, is a profession that contains some knowledge that cannot be formulated and described in diagrams, or verbally in detail in writing or speech. Elements of a tradecraft cannot be communicated in detail by prescription because there are no explicit rules and knowledge as such. As Polanyi argued, ‘The aim of a skilful performance is achieved by the observance of a set of rules which are not known to the person following them’ (Polanyi 1962a, p. 51). This can find support in Einstein, who stated

If you want to find out anything from the theoretical physicists about the methods they use, I advise you to stick closely to one principle: Don't listen to their words, fix your attention on their deeds (Einstein 1934, p. 12). The implications of this recognition are that since the sort of knowledge for the performance of some complex processes defy formulation or codification they cannot be transferred by prescription, since no prescription exists. Such knowledge, Polanyi observes, ‘…can be passed on only by example from master to apprentice. This restricts the range of diffusion to that of personal contacts’ (Polanyi 1962a, p. 55). Therefore, within a tradition, tacit knowledge features as a central component both for understanding the ‘practical knowledge’ involved for skilful practice, and epistemological considerations regarding learning within the tradition.

Polanyi emphasised that by submitting to authority the apprentice learns by emulation. The apprentice submits to the master because they trust their manner of practicing the art within the tradition. They unconsciously pick up the hidden rules of the art, even those rules the masters themselves are not explicitly aware of. A society which desires to

preserve personal knowledge, according to Polanyi, must sufficiently acknowledge this within the tradition (Polanyi 1962a, p. 55).

Personal knowledge Polanyi places the ‘knower’ at the centre of his concept of personal knowledge (Fennell 2016; Jha 2011; Johnson 2016; Meek 2011; Schmitt 2011-2012, p. 18). An important reason for this being, according to Polanyi, is that true knowledge necessarily depends on our capacity to use it (Aligica & Tarko 2012; Apczynski 2014; Fothe 2012; Narvaez 2012, p. 26). This holds a striking similarity with intelligence analysis, which generally refers to both the process and product of knowledge as being valuable only in terms of how well it can be used to assist decision-makers by reducing ignorance (Chang & Tetlock 2016; Corkill 2011, p. 4; Coulthart 2017a; Defence 2010; Mudd 2015; Omand 2014, p. 22; Pherson 2013; Smith 2017).

Personal knowledge is a central concept of Polanyi, reflected in the fact it is the title of his major book. Yet in some respects, the concept remains elusive. One question arising in relation to it is whether Polanyi is saying the scientific researcher is using personal knowledge in their research, or whether the scientist actually produces personal knowledge, or whether the discovery at which the scientist is aiming is personal knowledge, or some combination of these. Before outlining personal knowledge in detail, it should be distinguished from tacit knowing. The clearest distinction between the two is the function of the knowing/knowledge in terms of intent. In this sense, it would be best to understand tacit knowing as a process and personal knowledge as a product. In this respect, the function of tacit knowing refers to the process of perception, or more precisely how objects are perceived. The way the practitioner engages in their ‘practical knowledge’, by moving beyond the proximal focus to a distal awareness of how all the pieces of the puzzle cohere together, is an act of tacit integration. This is the functional characteristic of tacit knowing as a process. The ‘practical knowledge’ involved in the use of tools and the ‘skilful performance’ of a task can be

understood in this ‘tacit’ way of knowing. Personal knowledge, in terms of intent, is directed towards a responsibility of knowing, a commitment to the universal standards and principles of science as the individual scientist understands them within the tradition. Polanyi’s conception of personal knowledge, involving a ‘from-to’ (tacit) relation, locates responsibility at the centre of his epistemology. If all knowing requires a form of orientation, as Polanyi argues there must be, then there must be a knower. Personal knowledge acknowledges the key role of the knowing individual with respect to knowledge itself as a product. In this sense, the knowledge produced is my personal knowledge, which is achieved partly by way of tacit integration of perception, or tacit knowing as a process.

Scientific research, for Polanyi, involves much knowledge of the kind he describes as ‘personal’. To describe knowledge as ‘personal’ means it is ‘my knowledge’ – knowledge that in certain respects is uniquely my own, which cannot be obtained by following method-rules that are set down in a science textbook. Importantly, the use of knowledge that includes personal elements and colours the knowledge that the investigator or problem solver produces as a solution seems to be true for Polanyi of virtually any professional discipline. Polanyi uses the term ‘personal knowledge’ to describe situations in which the scientist (or thinker in some discipline outside science) is having to make their own judgement about how to proceed. Personal knowledge is involved in the exercising of skills (Polanyi 1962a, pp. 31-2). As suggested above, scientific research in Polanyi's view cannot be conducted by scientists mechanically applying certain rules. The maxims of research, which change over time and which are somewhat vague, have to be interpreted and cautiously followed by scientists, making use of their trained and experienced judgment. He speaks of the ‘personal participation of the scientist even in the most exact operations of science’ (Polanyi 1962a, p. 22). Polanyi writes in Personal Knowledge that the knowledge of the scientist, the scientific researcher in other words, is ‘personal in the sense of involving the personality of him who holds … [the belief that what he is aiming to discover through his research actually exists], and [it is also

personal] in the sense of being, as a rule, solitary’ in the spirit of being a conviction of a particular scientist. The scientist’s ‘act of knowing’ involves them exercising their ‘personal judgement in relating evidence to an external reality, an aspect of which he is seeking to apprehend’ (Polanyi 1962a, pp. 24-25). Personal knowledge engages aspects of tacit knowing, or unformulated ways of knowing that cannot be rigorously specified or set down on paper or in detail.

In the Preface of Personal Knowledge Polanyi noted how he regarded knowing as an active and skilful understanding of whatever it is a scientific researcher has attained knowledge. Knowing, for Polanyi, involves the subordination of specific details in the process of producing a ‘skilful achievement, whether practical or theoretical’ (Polanyi 1962a, p. iv). At the same time, as the scientist draws on their personal knowledge and talents for how to conduct research, such levels of perception enters and colours their pursuit of discovery. As stated in Chapter 1, this is also recognised within the intelligence realm regarding the acknowledgement that different forms of reasoning profoundly influence the judgments arrived at (Bruce 2008, p. 135; Heuer 1999; Jervis 2010; Kerr et al. 2005; Pherson & Beebe 2015; Walsh 2017; Waltz 2014; Wirtz 2014a).

One aspect of scientific work as involving personal knowledge concerns the points discussed above, of scientists deciding whether positive evidence verifies a hypothesis, and when negative evidence falsifies a hypothesis. According to Polanyi (1952, p. 219), ‘Discovery, verification and falsification proceed according to’ vague maxims which scientists apply, using their ‘personal judgment’. The ‘maxims and the art of interpreting them’ are scientific premises or beliefs that are incorporated in the unwritten ‘tradition of science’.

Following this dismissal of a method of science that can be formulated and codified, Polanyi’s concept of personal knowledge constitutes an alternative view for understanding the enterprise of science. By extension, some aspects of this view of science relate to considerations facing the intelligence analyst. Whilst Polanyi did emphasise the importance of the verification of theories and hypotheses,

underpinning such considerations was what he understood as being even more characteristic of the nature of science – the pursuit of worthwhile problems, coming from the individual’s personal knowledge. Polanyi states: ‘It is of the essence of the scientific method to select for verification hypotheses having a high chance of being true’ (Polanyi 1962a, p. 30). Such decisions, Polanyi argued, ultimately spring from the scientist’s personal knowledge. The philosopher Feyerabend came to a similar conclusion, stating, ‘This is how modern physics started: Not as an observational enterprise but as an unsupported speculation that was inconsistent with highly confirmed laws’ (Feyerabend 1970, p. 120). In this sense, Polanyi’s concept of personal knowledge contains the basis from which to reconsider important aspects of the enterprise of science itself. This is especially in relation to epistemological considerations, or the understanding of the nature of the knowledge produced by scientists. Polanyi argued that it would be a more accurate picture of science to describe such an enterprise, as guided by personal knowledge, as a pursuit in the face of and despite contradictory evidence and clues (Kessel 1969, p. 1000; Picornell 2013; Townsley, Mann & Garrett 2011). This has a bearing on intelligence analysis with respect to efforts and challenges facing the analyst in developing a theory that explains a subject that is against the dominant view. The email from Glenn Carle1 (see Appendix 1) to the author explores a series of interrelated issue around the difficulties facing intelligence practitioners who present their assessments which are inconsistent with or challenge the orthodox position.

As previously discussed regarding ‘proximal’ and ‘distal’ ways of knowing, such an awareness of these relations can be understood as being deliberately directed by the intention of the investigator’s personal knowledge; specifically, their ‘responsible act claiming universal validity’ (Polanyi 1962a, p. iv). The scientist’s understanding springs from their

1 Glenn Carle provided permission to have this email published in this thesis. Glenn Carle had a career in the CIA for 23 years, working in a number of posts around the world across several issues. Some of these issues included transnational threats, including organised crime, terrorism and narcotics. He is the author of The Interrogator: An Education (2011), which provides an account of his experiences of being involved in conducting interrogations in the post 9/11 U.S. Global War on Terrorism.

‘contact with a hidden reality’, which represents the basis for expecting further unknown true discoveries, anticipating indeterminate ‘true implications’ of the understanding that has been attained to this point (Polanyi 1962a, p. iv). Such a process of perception, involving tacit knowing, is driven by an aspect of the investigator’s personal knowledge, which Polanyi referred to as their ‘intellectual commitment’. These arguments have a bearing on intelligence analysis by articulating the difficulties associated within the process of solving problems. The commitment and the knowledge itself are ‘inherently hazardous’, which is to say they can never be definitively proven correct – a maxim which is similarly acknowledged within the intelligence profession (Chauvin & Fischhoff 2011; Eck, Clarke & Petrossian 2013; Lowenthal & Marks 2015; Moore 2007; Prunckun 2013). According to Polanyi’s view, knowledge involves a skilful construction by the scientist in their attempt to responsibly represent aspects of reality (Polanyi 1962a). Importantly, Polanyi stressed that in ‘every act of knowing’ there is present an affective element, which he referred to as being a ‘personal coefficient’. This ‘personal coefficient’, Polanyi argued, can be understood as the ‘passionate contribution of the person knowing what is being known’, which is ‘no mere imperfection but a vital component of his knowledge’ (Polanyi 1962a, p. v). The bearing this notion of personal knowledge has for the analyst is that it provides a conceptual foundation for acknowledging the importance of the analyst’s own knowledge as a core component to their knowledge claims. Polanyi’s concept of personal knowledge contributes to this discourse within the field of intelligence analysis especially since the core function of intelligence analysis is epistemological, regarding what it means to ‘know’ something.

Conclusion This chapter has explored Polanyi’s conceptualization of the nature of science and the activities of scientists. Polanyi emphasised the ‘practical knowledge’ of scientists regarding how he understood the processes of a ‘skilful performance’, being illustrative of the instrumental function of tacit knowing. It is here that one of the most significant features of Polanyi’s

nuanced understanding of tacit knowing is made clear. The significance of Polanyi’s concept of tacit knowing goes beyond the recognition that ‘We can know more than we can tell’ (Polanyi 1966b, p. 4). It is the relationship between tacit and explicit knowledge that is the key to understanding the value of Polanyi’s treatment of this concept. Polanyi argued that there was a strong nexus between tacit knowing and personal knowledge, which convinced him that the logic of perception (made possible partly by tacit knowing) was the same as the logic of discovery, and therefore of the knowledge produced – involving the investigator’s personal knowledge. Polanyi’s nuanced language of covering ‘proximal’ and ‘distal’ awareness, and how these relate to perception, provide the intelligence field with a novel intellectual landscape for considering a range of conceptual and practical issues in an epistemological sense. Chapter 3 will expand upon the discussion of tacit knowing as a process by further examining Polanyi’s understanding of perception and problem-solving as aspects of a ‘skilful practice’. These discussions, applied to epistemological considerations facing the intelligence analyst – particularly in relation to the process of problem-solving and the use of Structured Analytic Techniques (SATs) – will enrichen the language and logic for understanding these issues facing the analyst. Chapter 4 will expand upon Polanyi’s theory of personal knowledge, applying such arguments in relation to intelligence analysis as a product. Polanyi’s concept of personal knowledge posits that the personal coefficient of the ‘knower’ is central to understanding knowledge claims, particularly in relation to what it means to ‘know’ something. This has a bearing on the intelligence analyst in relation to contributing to a detailed and novel way for perceiving the discipline itself.

Chapter 3: Tacit knowing

Introduction The logician Keith Devlin, in writing about the 13th century mathematician Leonardo Pisano – famous for communicating to the West the Fibonacci principles, remarkable new ways for writing numbers and calculating with them – observed that it often takes true genius to ‘see the greatness in the commonplace, and to recognize the potential to change the world in what seems to most people to be a mundane or obscure idea’ (Devlin 2017, p. 21). Polanyi’s position springs from the mundane acknowledgement that ‘We can know more than we can tell’ (Polanyi 1966b, p. 4). Moving beyond this claim, Polanyi discerns the importance of tacit knowing within a ‘skilful performance’, which contributes towards a better understanding of problem-solving within intelligence analysis. Specifically, his arguments regarding the ‘structure of skills’ involving tacit knowing and ‘practical skills’ provide a more detailed and technical account of the process of solving problems, which is transferrable to intelligence analysis.

It has been recognised within the national security field, as observed within various government reports and commissions into the intelligence failures of the 9/11 attacks (Kean & Hamilton 2004) and the WMD scandal (United States Department of State 2005), that such mistakes are partly attributed to poor analytical tradecraft, along with poor information sharing across intelligence and law enforcement agencies. The substandard analysis partly led to impoverished intelligence assessments, captured in a statement made within the 9/11 Commission Report of the identified problem for not sufficiently ‘connecting the dots’ (Kean & Hamilton 2004, p. 400). One of the responses to this problem, demonstrably observed in the United States’ Intelligence Community, has been the institutionalization and legal requirement, via the Office of the Director of National Intelligence (ODNI), of Intelligence Community Directive 203 Analytic Standards, requiring analysts to use Structured Analytic Techniques, or SATs (Gentry 2015). These analytical tools, designed to better manage and standardize the performance of analysis,

are an attempt to align the profession with scientific principles. Epistemologically, it can be argued that SATs elevate propositional knowledge and insufficiently recognise the value of ‘tacit’ or non- propositional ways of knowing within the process of solving problems in intelligence analysis. Polanyi’s emphasis on the tacit aspect of making discoveries and solving problems raises questions regarding the epistemological utility of some SATs that emphasise explicit knowledge for solving problems.

Since tacit knowing defies complete articulation, either through words or graphically (Vandepeer 2014, p. 31), Polanyi argued that one of the best ways to recognise this way of knowing is from observing skilful performances and discussing the related epistemological considerations of such activities. In this regard, the aim of this chapter is to explore Polanyi’s understanding of a ‘skilful performance’, which serves to contribute a clearer and nuanced epistemological understanding of tacit knowing within the process of solving problems within intelligence analysis. Polanyi’s understanding of the nature of perception, involving the practitioner’s tacit skilful powers of integration, provides a language and logic for detailing the process of solving problems. These insights are transferrable to the tradecraft of intelligence analysis. These discussions will then lead to exploring the logic underpinning the use of SATs within intelligence analysis, which will be examined with respect to Polanyi’s arguments regarding tacit knowing and the skilful usefulness of tools within the process of solving problems. Polanyi’s concept of ‘destructive analysis’ provides a technical epistemological warning regarding the use of analytical tools or methods, such as SATs within intelligence analysis, which may lead to the over-compartmentalization of the process of solving problems.

Tacit knowing and the use of tools Polanyi’s concept of tacit knowing amounts to a more complete epistemological account of what is precisely meant by ‘know how’, or that form of knowledge drawn on by the practitioner to skilfully perform tasks.

Discussions within intelligence literature regarding what is required for reliable and accurate analysis amounts to the acknowledgement that ‘know how’ is key to understanding and defining the ‘skilful’ performance of analysis (Alschuler 2007; Cooper 2005; De Franco & Meyer 2011; Dennis 2013; Margolis et al. 2012; Ouagrham-Gormley 2014; Taylor et al. 2013; Turner 2012; Vogel & Dennis 2012). Acknowledgements within intelligence literature of the importance of ‘know how’ range from the national security sphere, such as correctly assessing the existence of WMD programs (Godson & Wirtz 2000; Kay 1995; Phythian 2009; Vogel & Dennis 2012), to law enforcement, for example accurately identifying deception within written witness statements (Ekman 2009; Kang & Lee 2014; Picornell 2013). Polanyi’s concept of tacit knowing, particularly with respect to the skilful use of tools, posits a more nuanced understanding of the recognition within the intelligence field that ‘know how’ is involved within analysis. Taken together, Polanyi’s treatment of the concept of tacit knowing expands upon the precise meaning of ‘know how’ and by extension develops a more epistemologically rich understanding for the process of analysis, particularly in relation to the skilful use of tools.

Polanyi’s epistemology acknowledges the function of tacit knowing in all knowledge (Grene 1977, p. 165), and that knowledge is an activity best ‘… described as a process of knowing’ (Polanyi 1969, p. 132). With respect to better understanding the ‘usefulness’ of analytical tools within intelligence analysis as a process, it is necessary to briefly return to the recognition that the key to tacit knowing is the relation of the tacit to the explicit (Grene 1977, p. 165). That is, the recognition that the process of knowing involves a transition from our powers of tacit integration (involving perception and skill) to explicit knowledge. In this sense, the ‘usefulness’ of tools, according to Polanyi, should assist this process of tacit integration, allowing or assisting the ‘skilful performance’ as intended by the practitioner.

The understanding offered by Polanyi in relation to the usefulness of tools can contribute towards developing a more epistemologically robust account for discussing some of the strengths and limitations of SATs as

analytical tools; specifically, with reference to Polanyi’s broad claim that a tool is not useful if it does not assist the process of problem-solving it was intended for. In Polanyi’s words,

An object alleged to be a tool is not a tool if our conception of its alleged use is altogether mistaken… or if it otherwise fails to serve its alleged purpose; it is an error to rely on a tool in such a case (Polanyi 1962a, p. 249). This has a bearing on intelligence analysis with respect to recognising, broadly speaking, that the usefulness of a tool should be judged with respect to its intended aim.

One of the ways that Polanyi examines how tacit knowing operates in the function of a task is in relation to how we visually and psychologically perceive objects. Polanyi’s understanding of the tacit operations involved in the process of solving problems springs from Gestalt psychology, specifically regarding the way a figure within a background is identified (Burley & Freier 2004; Hamlyn 2017; Kałamała et al. 2017; Wagemans 2013). In outlining Gestalt psychology, Polanyi refers to Rubin’s ‘vase or faces’, which is an image that at one moment can reveal two faces in profile, with a space in between, or a vase with space on the outside. For Polanyi, this principle of visual perception demonstrates that we perceive the object by virtue of its surroundings (Polanyi 1969, p. 111). In this sense, Polanyi emphasises that perception of an object takes place by sufficiently contextualizing the subject. Polanyi concludes that such visual illusions represent how our attention is directed both subsidiarily on the clues around the object, but also on the figure itself (Polanyi 1969, pp. 112-3). As Polanyi states,

This interplay of background and figure illustrates a general principle: the principle that whenever we are focusing our attention on a particular object, we are relying for doing so on our awareness of many things to which we are not attending directly at the moment, but which are yet functioning as compelling clues for the way the object of our attention will appear to the senses (Polanyi 1969, p. 113). For Polanyi, this general principle is key to understanding the nature of tacit knowing within the operations of a task. He observes that the perception of the subject under investigation derives from a mutual stimulus between their tacit knowledge and observation throughout this

entire process (Polanyi 1964, pp. 29-30). The practitioner, according to Polanyi, tacitly integrates these observations throughout the process of analysis. This understanding of perception, involving the tacit operations of the observer switching between their focal and subsidiary awareness, provides a nuanced way for recognising the nature of perception in the process of intelligence analysts solving problems.

The Knowledge Management field, dealing with a range of epistemological considerations (particularly from an organisational or institutional perspective) has been drawn on by some intelligence scholars regarding ways for overcoming information and data management issues facing intelligence analysts (Bhaskar & Zhang 2007; Glomseth & Gottschalk 2009; Gottschalk 2007; Jones 2007; Phythian 2017; Tang 2017). The Knowledge Management field has addressed some of the challenges facing intelligence analysis (Phythian 2017), such as the relationship between knowledge and time (‘temporal convergence’) (Martin, Philp & Hall 2009) and strategy-making processes for modelling knowledge (Massingham 2004). Tellingly, the intelligence field acknowledges that ‘tacit’ or ‘intuitive’ ways of knowing have not been sufficiently recognised within the process of analysis (Dennis 2013; Kamarck 2005, p. 12; Margolis et al. 2012, p.2; Ouagrham-Gormley 2014; Tang 2017; Vogel 2013a, 2013b). This is despite the fact that the intelligence analysis field has drawn on aspects of the Knowledge Management literature (Fishbein & Traverton 2004; Lahneman 2010a, p. 616), which devotes sustained attention towards the exchange between tacit and explicit knowledge (Garrick & Chan 2017; Martinez-Conesa et al. 2017; Nonaka & Takeuchi 1995; Rothberg et al. 2017; Turner 2012; Von Krogh, Ichijo & Nonaka 2000). The implication of this lack of engagement or discussion within intelligence analysis around the tacit-explicit dynamic or dimension of knowing amounts to a restricted understanding of the process of analysis. A limited understanding of the process of analysis was observed by Charles Allen, previously the Associate Director of Intelligence at the CIA, who stated: ‘We’re not very good at evaluating the quality of intelligence analysis independent of the outcome. We’re

outcome oriented, rather than process oriented’ (Allen 2000). Therefore, if the policy succeeds and the outcome is desirable, then there is not much attention devoted to understanding the process of intelligence. This can undermine the very essence of intelligence analysis cumulatively (Allen 2000), principally by having an incomplete or impoverished understanding of the process of analysis (Cooper 2005, p. 38). Polanyi’s discussions regarding the operations of tacit knowing within the problem-solving process contributes towards an expanded way to understand this process within intelligence analysis, specifically by providing a detailed account for how a ‘skilful performance’, involving the tacit knowing of the practitioner, is key.

As explored in Chapter 2 within the sub-section Tacit knowing, perception is an activity and it normally involves some degree of skill.2 As acknowledged within the field of psychology, what we perceive and the degree or depth of our perception is an outcome of some engagement in skill (Chase & Simon 1973; Chi, Glaser & Farr 2014; Hamlyn 2017; Ingold 2000; Kristiansen, Jensen & Trabjerg 2014). For example, the sportsperson in reading the ‘play’ of a game, anticipating where to be in a moment’s time; or the musician performing an instrument, creating harmonies and melodies. Polanyi adds to this acknowledgement, particularly by taking inspiration from findings within Gestalt psychology (Burley & Freier 2004; Kałamała et al. 2017; Wagemans 2013), that the process of perception is achieved by the ‘tacit powers’ of the practitioner, specifically involving the switching between ‘distal’ and ‘proximal’ awareness. In this sense, the ‘tacit integration’ of ‘particulars’ within the practitioner’s ‘subsidiary awareness’ offers a more complete and technical understanding of the process of using ‘tools’. The tool should assist this process of perception, according to Polanyi. He gives an example of the process of perception in this sense as the way a tune can be recognised, by comprehending how all the notes come together as a whole. To focus one’s attention on single notes within the tune is to lose the

2 There are exceptions to the general rule that perception involves some level of ‘skill’. For example, the perception of danger or some kind of immediate threat to one’s safety could be perceived by instinct, requiring no prior learning, practice or refinement for recognising the threat.

comprehension of the tune overall (Polanyi 1962a, p. 59). This is illustrative, Polanyi argued, of how our attention can hold only one focus at a time, of ‘proximal’ or ‘distal’ awareness. Polanyi argued this scheme can be applied and expanded with respect to the ‘usefulness’ of tools. As Polanyi claims, ‘If we discredit the usefulness of a tool, its meaning as a tool is gone. All particulars become meaningless if we lose sight of the pattern which they jointly constitute’ (Polanyi 1962a, p. 59). Importantly, according to Polanyi, there is cause for concern if a so-called ‘tool’ diminishes or potentially undermines the process of tacit knowing: specifically, by confusing the epistemological ‘from-to’ orientation, moving from ‘particulars’ to ‘subsidiary awareness’. As will become clearer later in this chapter, particularly around the discussions regarding ‘destructive analysis’, this warning of Polanyi regarding the use of tools has a bearing on some of the potential dangers of using some SATs within the process of analysis. Before exploring Polanyi’s discussions of tacit knowing in relation to a ‘skilful performance’ for solving problems, it will be necessary to outline and contextualize the logic underpinning the use of SATs within intelligence analysis.

SATs: an outline The reason for choosing to explore SATs within intelligence analysis is that these techniques form in part the foundation for contemporary analytic tradecraft practices (United States Marine Corp, 2011, p. 2; Coulthart 2016; Frank 2012, p. 91; Heuer 2008b; Wirtz 2014b). In this regard, SATs represent a way for thinking about analytic practices, processes and ‘skill’ within this field. SATs have emerged partly in response to the broad recognition that despite some efforts being devoted to some aspects of analysis (presentation and outcomes), disproportionately less has been focused on what occurs intellectually in the process of thinking (Martin et al. 2011a; Moore 2007, p. x; Pherson 2013, p. 54).

Providing some historical context of the emergence of SATs, with a particular focus on the US, will clarify the motivation behind the introduction of these techniques. At the outset, for the purposes of this

thesis, it is important to acknowledge that SATs may serve a beneficial function within the overall process of intelligence analysis. The aim of this chapter is not to discredit the potential value of SATs, but rather to discuss Polanyi’s arguments regarding how problems are skilfully solved and the centrality of tacit knowing within this process. These discussions will include Polanyi’s epistemological considerations in relation to the use of SATs within intelligence analysis.

The motivation for improving the accuracy and tradecraft of intelligence analysis within the national security field has largely been the result of intelligence failures, notably the 9/11 attacks and the war with Iraq over Saddam Hussein’s alleged possession of WMDs. Various Commissions and government reports into intelligence failures have cited poor critical thinking as a weak link contributing to such intelligence mistakes (Betts 2009; Bruce & George 2008; Butler 2004; Flood 2004; Hatch 2013; Kean & Hamilton 2004; Smith 2014; Wastell 2010). As Pherson and Pherson observed:

Almost every post-mortem of past intelligence failures concludes that analysts were working from outdated or flawed mental mindsets and had failed to consider alternative explanations. Most recently, the Iraq WMD commission’s indictment of ‘poor tradecraft’ (critical thinking) and the 9/11 Commission’s judgment that analysis suffered from a ‘failure of imagination’ signalled the need to incorporate more rigour and creativity into the analytic process (Pherson & Pherson 2012, p. xxi). The Silbermann-Robb WMD Commission, in criticizing US intelligence analysis, reported a lack of rigorous analysis within the intelligence community and that the conclusions of analysts were based not so much on knowledge but on inferences and assumptions (United States Department of State 2005). It is clear that basing analysis on inferences and assumptions rather than reliable knowledge can lead to mistakes (Betts 2009; Lefebvre 2004; Lowenthal 2008; Pritchard & Goodman 2008; Vrist Rønn 2014).3

Following US government investigations into the intelligence failures of Iraq and the 9/11 attacks, in 2004 the US implemented the

3 The distinctions between knowledge and assumptions or beliefs is explored in greater depth in Chapter 4, within the section Genuine knowledge and belief.

Intelligence Reform and Terrorism Prevention Act. A standout feature of this Act is how it aims to reform the daily tasks of analytic performance, what Betts refers to as the ‘enemy’ of intelligence analysis – particularly around inherent cognitive limitations of intelligence analysis (Betts 2007). This Act and the Directives which followed have been referred to as ‘analytic transformation’ (Fingar 2011a; Immerman 2011). The Act legally requires that analysts are trained in and apply Structured Analytic Techniques (SATs) (section 1017, sub-section A).4 By 2004 a series of techniques came to be termed under the umbrella Structured Analytic Techniques, or SATs (Walsh 2012). The US government in 2004 also issued an Intelligence Community Directive that stipulated Analytic Standards for intelligence analysis and production, largely adopting SATs as the principal logic and methodology. An indication of the widening acceptance of the need and alleged value of SATs for the intelligence analysis process can be partly witnessed in the introduction of a new intelligence journal, created in 2016, entitled the International Journal of Intelligence, Security, and Public Affairs, which calls for contributions in the ‘form of analyses that make transparent and explicit their analytic methods/techniques used.’5 Taken together, the legal requirement for analysts within the US Intelligence Community to utilise SATs, coupled with the growing scholarly attention towards unpacking the potential value of such analytical tools (Borg 2017; Coulthart 2017b; Marrin 2012b; Pherson & Beebe 2015; Prunckun 2015; Heuer 2005, 2008b; Spielmann 2014; Townsley, Mann & Garrett 2011), signals a strong institutional and intellectual interest for examining SATs within the discipline of intelligence analysis.

The theoretical basis for SATs came from psychology research conducted by Amos Tversky and Daniel Kahneman in the 1970s and 1980s (Coulthart 2016, p. 11; Kahneman 1973, 2003, 2011; Kahneman & Tversky 1982; Martin 2012; Tversky & Kahneman 1971, 1973, 1975). The key finding from their research was that people use heuristics to make

4 For details of the Directive see https://fas.org/irp/dni/icd/icd-203.pdf. 5 For more details see http://www.tandfonline.com/loi/usip20#.VuXXbUely-d. Date accessed 13.09.16

quick judgments, and such processes lead to predictable mistakes and errors (Coulthart 2016, p. 11). Richards Heuer then adopted these findings into intelligence analysis with his classic book The Psychology of Intelligence Analysis (1999), which became the justification or foundation for SATs (Coulthart 2016, p. 11). Heuer claimed that the subjects in Tversky and Kahneman’s study were comparable to intelligence analysts, who also use heuristics that can lead to errors (Heuer 1999). Heuer and Pherson posited the argument that analysts should transition from using quick-thinking and intuitive techniques, which engage heuristics, to those involving slower and more effortful or rigorous consideration (Richards & Pherson 2010).

SATs are techniques designed to lay bare the thoughts of the analyst, by externalising their thinking and thereby increasing the transparency of the evidentiary basis of claims (Coulthart 2016, p. 937). These techniques are designed to potentially reveal any bias and misleading logic or hypotheses within the analysis, by increasing the transparency of the thought-processes or knowledge claims, and thereby increasing the accountability of the analytical process (CIA 2009; Marrin 2007a; Martin et al. 2011a; Richards & Pherson 2010). Such techniques are designed to engage ‘meta-cognition’, or in the words of Mark Lowenthal, ‘…thinking about thinking’, to be mindful not just of the conclusions reached but how one reasons in this process (Lowenthal cited in Moore 2007, p. 8). SATs provide an ‘audit trail’, allowing outside observers to better understand the analytic basis of conclusions reached (Schwartz cited in Marrin 2007a, p. 7). The notion is that such processes will make it easier for analysts and co-workers to identify mistakes in their thinking, facilitating productivity and strengthening the reliability of the knowledge-base.

The fundamental goals of SATs are to a) decompose the data and information for clearer analysis and b) reveal how the analyst arrived at their conclusions (ideally reducing bias in the process). This process is designed to assist analysts deal with what has been described as an increasingly more complex security landscape (Fingar 2011b, p. 7).

Heuer’s seminal work The Psychology of Intelligence Analysis (1999), covering cognitive limitations such as bias and mindsets, trumpeted the value of structured ways of analysing issues, to minimize but not entirely overcome these limitations facing the analyst.

A useful document that provides an overview of SATs is the publication A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis, prepared by the US Government (CIA 2009). This document characterises the various SATs according to their purpose. Diagnostic techniques are characterised in their aim for making analytical arguments, assumptions, or gaps of intelligence more transparent; contrarian techniques challenge current thinking; and imaginative techniques are designed to develop new insights, form new perspectives or create new outcomes (Primer 2009, p. 5).

Many of these techniques can have a cross-over effect, possibly achieving a combination of these functions. At the foundation of SATs is ‘Making analytic arguments more transparent by articulating them and challenging key assumptions’ (Primer 2009, p. 2). Importantly, it can be argued that SATs are deliberately designed to emphasise explicit knowledge and limit or reduce tacit knowing within the process of solving problems within intelligence analysis. With respect to Polanyi’s understanding of a ‘skilful performance’, in which tacit knowing is central to this process, SATs may epistemologically undermine the process of solving problems within intelligence analysis.

Tools, objectivity and bias At the core of intelligence analysis remains the capacity to read and critically consider the information, which is a process subject to fallibility (Dahl 2017; Jervis 2010; Lowenthal & Marks 2015, p. 665; Warner 2017). SATs have been promoted by some as a means of reducing fallibility by increasing the analyst’s objectivity (Quarmby & Young 2010; Tan 2014, p. 221). This idea is derived from the belief that analysts are both the cornerstone of fulfilling the intelligence capability and ‘the weakest link in any intelligence process’ (Quarmby & Young 2010, p. 221). This is due to

a variety of reasons, from the inability to deal with complexity to resistance to change (Pherson & Beebe 2015; Quarmby & Young 2010, p. 221; Heuer, Pherson & Beebe 2009; Wirtz 2012). These tools are intended to promote objectivity, since they are deliberately designed to be disruptive of ‘natural’ (principally unstructured) thought processes, by guiding the analyst to systematically consider and externalise their thoughts and the basis of their knowledge claims (Kan et al. 2013; Pherson & Beebe 2015, p. xxxv; Prunckun 2015).

In this respect, a principal aim of SATs is the exposing of the analyst’s mental ‘mindset’ (Frank 2012; Marrin 2016, 2017; Pherson & Beebe 2015; Heuer 2008b). The term ‘mindset’ refers to the representations of the analyst’s thinking within their analysis (Waltz 2014, p. 1). ‘Mindsets’ are the set of expectations that shape how people see the world, built upon conceptual models, or epistemological foundations for ways of thinking about what we know and our confidence in knowledge claims (Muller 2007; Pherson & Beebe 2015; Richards & Pherson 2010; Wirtz 2012, 2014a). As the analyst makes their thoughts more explicit in words or graphics, they render the basis of their thoughts more clearly intelligible to others (Waltz 2014, p. 1). Analytical tools therefore aim to represent the analyst’s understanding of observations and their reasoning process in an explicit fashion. The goal of SATs is therefore to generate reliable analysis, in terms of revealing uncertainties, ambiguities and flaws in understanding evidence, assumptions or inferences that can be hidden or ignored if they remained implicit (Waltz 2014, p. 2). Waltz notes Polanyi’s concept of tacit knowing, specifically that such forms of knowing serve as a precursor to discovery and logical forms of reasoning (Waltz 2014, p. 46). Waltz adds that tacit knowing constitutes the ‘formative’ stages of the mind’s mindset, or ways for knowing things (Waltz 2014, p. 46). A more accurate representation of Polanyi’s understanding of tacit knowing is that such tacit mental operations are integral to the whole cognitive process – they are not limited to being ‘formative’ to the mind’s ‘mindset’ (Polanyi 1966b, pp. 4-6). We come to perceive precisely because of our mental powers of tacit integration, according to Polanyi.

Tacitly integrating pieces of the puzzle together to form a whole is not ‘formative’, but essential for the entire process of cognition, according to Polanyi (Polanyi 1966b). In this sense, Polanyi re-casts epistemological considerations of tacit knowing with respect to efforts to use SATs to represent the analyst’s mental ‘mindset’. Specifically, according to Polanyi’s view, sufficient acknowledgment of tacit knowing is fundamental for more completely and accurately recognising the process of solving problems. It is precisely because of the practitioner’s tacit powers of integration that the process of solving problems is possible (Apczynski 2014; Mullins 2013; Polanyi 1957, 1966b).

The popularly recognised ideal for the scientist to reduce the personal elements from their work is captured in the character Sherlock Holmes as he instructs Doctor Watson, ‘It is of the first importance not to allow your judgment to be biased by personal qualities’ (Conan Doyle cited in Lathrop 2008, p. 328). This fictional character can even be found referenced as the embodiment of the ideals of intelligence, such as: ‘The real intelligence hero is Sherlock Holmes, not James Bond’ (Friedman 1998, p. 159). This sentiment can be found echoed in Heuer and Pherson’s discussion of the allegedly problematic nature of relying on intuitive and non-scientific analysis (Richards & Pherson 2010). Other studies into the decision-making process of intelligence analysts have called for more accountability, or even that they should ‘take responsibility’ (Martin et al. 2011b, p. 16) by revealing how they arrived at conclusions with propositional or explicit knowledge.

Polanyi challenges this aim of improving ‘objectivity’ and reducing ‘bias’, where possible, since according to his view such goals misrepresent the importance of the personal ‘tacit’ engagement of the practitioner in the process of solving problems and making discoveries (Polanyi 1962a, p. 1). A prominent argument Polanyi articulates regarding this view is that the scientist must draw on their personal ‘tacit knowledge’ and avoid detachment, in order to solve problems (Polanyi 1962a). According to Polanyi, since all knowledge and perception contains to varying degrees a personal affirmation (Polanyi 1962a, pp. 64-75),

Polanyi’s view offers an alternative way for considering the aims within the intelligence field: specifically, another way for considering the process of solving problems in contrast to the conventional goal for improving objectivity of knowledge by removing the ‘personal’ aspect within the process of analysis.

As O’Connor and McDermott argue, the problem-solving process can be understood as a progression from ‘analysis’, involving the interrogation of sources and ideas, to ‘synthesis’, or the cohering together of all the pertinent information, data and theories to provide an explanation (O’Connor & McDermott 1997a, p. 12). Such a view of problem-solving is consistent with the general view of intelligence analysis regarding the importance of contextualizing the information with respect to the security environment (Eck, Clarke & Petrossian 2013; Evans & Kebbell 2012; Wood et al. 2017; Zegart 2009). Polanyi adds to this by emphasising that the individual practitioner as a ‘knower’ is key to the process of solving problems in general. As he argues, the individual knower skilfully integrates their ‘focal’ attention within their ‘subsidiary awareness’, in order to solve problems (Polanyi 1966b, 1969). This skilful integration does not occur naturally or automatically, but as a result of the wilful act of the knower. Polanyi is not claiming that the practitioner should fail to be critical of their thinking, or mindful of how they reached certain conclusions. Polanyi’s understanding of the nature of perception, of the ‘knower’ being centre-stage, emphasises that the epistemological basis of removing the individual as much as possible to reduce bias and improve objectivity is misguided for solving problems and recognising knowledge claims.

Skilful performance Polanyi’s discussions regarding a ‘skilful performance’, involving the practitioner’s tacit knowing, offers a more detailed epistemological account of the process of solving problems with regards to intelligence analysis. Whilst some intelligence scholars have touched on the tacit-explicit interaction (Dennis 2013; Margolis et al. 2012; Vogel 2013a; Waltz 2014, p. 62), Polanyi’s detailed examination of this topic can develop a more

robust understanding of this area within intelligence analysis. According to Polanyi, the process of solving problems requires the practitioner to take account of the complementary relationship between their explicit and tacit knowledge. As Polanyi states, ‘These two aspects of knowing have a similar structure and neither is ever present without the other’ (Polanyi 1966b, p. 7). The practitioner is able to solve problems skilfully by drawing on their trained judgment and combining tacit relations which are indefinable (Polanyi 1962c, p. 8). With respect to the acknowledgement that human judgment is key within the process of intelligence analysis, including the (algorithm-rich) field of Big Data (Ahmadi, Dileepan & Wheatley 2016; Strang & Sun 2017; Van Puyvelde, Coulthart & Hossain 2017a, p. 1400; Van Puyvelde, Coulthart & Hossain 2017b), Polanyi adds to this recognition that such analytic judgments should take account of the dynamic relationship between explicit and tacit knowledge.

This dynamic relationship plays a significant role for understanding the process of a ‘skilful performance’ in solving problems with regards to the practitioner’s decisions as to what evidence or clues to collect and the meaning of such sources of information and data. Decisions regarding what evidence to accept or reject is a key concern of both the scientist (Devlin 2017; Laqueur 1983; Polanyi 1964; Sokal 2001) and the analyst (Betts 2009; Coulthart 2017a; Gill 2009; James et al. 2017; Marrin 2017; Phythian 2013). To better understand the nature of these decisions and appreciate the ‘skilful practice’ involved in this process requires a clearer epistemological picture of the distinction of these choices. Professor of philosophy Chris Mole, writing on the key philosophical lessons for the criminal and military intelligence analyst, outlines that there are two categories of evidence or clues – those that are ‘reliable’ and those that are ‘resilient’ (Mole 2012). According to Mole, evidence is ‘reliable’ if it is usually true, all things being equal. But what if other things are not equal? What if a different piece of evidence or clue offers alternative ways for interpreting the subject? What to believe in, or what the clues or evidence amount to in terms of significance, is a question of ‘resilience’. Two sources may be highly reliable, but differ in their resilience. In various

intelligence and science contexts, this is very difficult to identify and requires, according to Polanyi, a ‘skilful practice’ to make such distinctions around the weighting of evidence and clues. The resilience of claims is of a higher value for better understanding the topic or situation in question, because it more completely represents the context of the security environment (Mole 2012).

Unlike the more exact sciences, in which, for example, ‘Pure gold is recognised by testing’ (Leonardo da Vinci cited in Bramly 1994, p. 303), it is not possible to consistently and definitively ‘test’ whether a hypothesis or evidence is ‘reliable’ or ‘resilient’ within intelligence analysis, like the social sciences more generally (Bruce & George 2008; Marrin 2017; Phythian 2017; Pringle Jr 2015; Townsley, Mann & Garrett 2011). Polanyi’s understanding of a ‘skilful performance’, involving the practitioner’s ‘tacit powers’ of integration, epistemologically articulates the need to sufficiently acknowledge the importance of tacit knowing within the process of analysis. Polanyi’s concepts of tacit knowing with respect to a ‘skilful performance’ provides an expanded way for epistemologically recognising more completely the way the practitioner tacitly decides on the ‘reliability’ and ‘resilience’ of evidence and clues. As will be presently examined, Polanyi’s concept of ‘indwelling’, which further expands on the concept of a ‘skilful performance’, provides a more in-depth and detailed epistemological understanding of the skilful use of tools, such as SATs, within the process of intelligence analysis.

The concept of ‘indwelling’ ranges in meaning from the use of tools (Polanyi 1962a, p. 58), to the transmission of culture (Polanyi 1962a, p. 219) to the appreciation of art (Polanyi 1962a, p. 234). ‘Indwelling’ is, according to Polanyi, expressed in the scientist exercising their selection and skilful use of tools. It occurs within the act of the practitioner making the tool form a part of their own body and mind (Polanyi 1962a, p. 61). As Polanyi stated, ‘when we accept a certain set of pre-suppositions and use them as our interpretive framework, we may be said to dwell in them as we do in our own body’ (1958, p. 60). According to Polanyi, the scientists pours themselves into the tool, assimilating the tool as parts of their own

existence (Polanyi 1962a, p. 61). This process occurs by the scientist merging their ‘subsidiary’ and ‘focal’ awareness. As Polanyi observes, ‘we can say that for the knower the subsidiaries have a meaning which fills the center of his focal attention’ (Prosch & Polanyi 1975, p. 38). As outlined in Chapter 2, within the section Tacit knowing, the act of knowing is the culmination of the process of orientation between the practitioner’s subsidiary and focal awareness. With respect to the use of tools, this process of orientation – the ‘from-to’ relationship of knowing – is assisted by the skilful engagement of tools, in which the practitioner ‘dwells’ in the exercising of those tools (Polanyi 1962a, p. iv).

Polanyi further elaborates that the skilful performance is achieved by a deliberate effort of ‘indwelling’, which involves the expansion and interiorisation of the inquirer within the topic and tools within their use (Polanyi 1969, p. 148). Indwelling involves the acquiring of a new consciousness, the outcome of a repeated mental effort for the ‘service of some purpose’ (Polanyi 1962a, p. 62). The purpose of indwelling is reaching an understanding of the joint meaning of the ‘particulars’ (Polanyi 1966b, p. 18), or how all the pieces of the puzzle cohere together to form a holistic picture. Throughout this process there is always the personal participation of the researcher, as Polanyi states: ‘Into every act of knowing enters the ‘personal coefficient’ (Polanyi 1962a, p. 17). The implication is that, according to Polanyi, all knowing amounts to the involvement of personal knowledge – a process in which the individual participates through indwelling (Prosch & Polanyi 1975, p. 44). The skilful navigation of a problem depends on the appropriate blending of the practitioner’s focal and subsidiary awareness. As Polanyi observes, ‘We are attending from these elementary movements to the achievement of their joint purpose’ (Polanyi 1962c, p. 10). This joint purpose is the tacit integration of subsidiaries into the focal awareness of the researcher, as a means for solving the set problems. Polanyi’s concept of ‘indwelling’ provides a more detailed and robust epistemological understanding of the use of tools within the process of solving problems. In this sense, Polanyi’s concept of indwelling presents a way for epistemologically considering the

use of SATs within intelligence analysis. These discussions contribute to this discourse by moving beyond the general acknowledgment within the intelligence field of the importance of ‘know how’, to more precisely understanding how we know, particularly in relation to the skilful use of tools.

Related to the recognition of the importance of ‘know how’ within the intelligence field is the acknowledged value of inquisitiveness within the analyst. This acknowledgement of the importance of inquisitiveness as a quality for the analyst to possess is partly captured by an analyst in Rob Johnson’s study Analytic Culture in the US Intelligence Community: An Ethnographic Study, who observes: ‘I’m looking for links and patterns. Once I figure out the pattern, I can figure out where to look next’ (Johnson 2005, p. 19). This value of inquisitiveness has been broadly recognised within the intelligence field (Bruce & George 2008; Carson 2009; Evans & Kebbell 2012; Hoffman et al. 2011). As the intelligence scholar Thomas Fingar observed regarding the inquisitive virtue within the intelligence analyst, ‘One of the most important parts of an analyst’s job is to formulate questions that will provide timely insight and can be answered with available or obtainable information’ (Fingar 2011a, p. 41). Richards Heuer observed the importance of analysts raising new questions, out of their own curiosity, that may lead to ‘previously unrecognized relationships’ (Heuer 1999, p. 75). In a similar spirit to inquisitiveness, ‘hunches’ have been acknowledged within the process of solving problems within the intelligence field (Jha 2011, p. 337; Lerner 2007; Silverman 2007; Smith 2017), including law enforcement (Ferguson 2012; Horan 2007; Rosenbaum 2007; Silverman 2007, p. 134). Polanyi adds to this acknowledgement of the importance of inquisitiveness within the process of solving problems by emphasising the way the practitioner’s tacit knowing is key to asking the right questions and sensing proximity to the solution (Polanyi 1957, p. 93). According to Polanyi, it is these considerations in relation to the process of solving problems that tacit knowing is central to, of sensing a suitable line of inquiry that could bear fruit in the future.

Polanyi added to the importance of inquisitiveness within the process of solving problems is the skill involved in choosing and using suitable heuristics. As Polanyi argued, ‘systematic’ analysis is often too laborious, of checking every single possible outcome methodically, compared to the use of suitable heuristics that are not systematic (Polanyi 1957, p. 97). Heuristics are key to progressing the process of solving problems since the premises of science cannot be explicitly formulated (Polanyi 1964, p. 85). The implications of this means that observations can lead to ‘… an infinite number of possible future observations’ (Polanyi 1962, p. 23). Without the practitioner utilising suitable heuristics, according to Polanyi, investigations could spread out into a ‘desert of trivialities’ (Polanyi 1962a, p. 135). Polanyi emphasises the heuristic model of discovery based on ‘intelligent effort’, involving the ‘skilful’ talent of the scientist (Polanyi 1957, p. 90).

Heuristics are involved in making comparisons from observations regarding whether deviations are entirely random or show a significant trend (Polanyi 1969, p. 106). According to Polanyi, in using heuristics the practitioner directs their attention towards a) highlighting suggestive features and b) remaining mindful of similar problems of which the solution is already known (Polanyi 1957, p. 99). Throughout this process the practitioner must draw on their mental powers of tacit knowing for solving problems (Polanyi 1962a). This would include assembling the facts together (Wastell, Clark & Duncan 2006, p. 36) and epistemological considerations regarding what evidence or clues are resilient and reliable, in the sense Mole makes of such distinctions within intelligence analysis (Mole 2012). The practitioner then anticipates a hidden potential solution, which will enable them to perceive deeper aspects of the problem (Polanyi 1957, p. 99). Polanyi gives the example of the astronomer, who must decide whether the observed deviations from the theoretical path of a planet demonstrate any regularity, and if so, what precisely the presence or absence of such deviations amounts to (Polanyi 1969, p. 107). These arguments provide a more detailed epistemological account of what is meant by ‘know how’ with respect to the analyst solving problems within

their work flows, by utilising their inquisitiveness and pairing this with suitable heuristics. This process of solving problems, according to Polanyi, is akin to the act of placing stones in an arch, whose stability depends on the stones’ placement (Polanyi 1962a, p. 136). Throughout this process the scientist reduces the logical gap, leading to the discovery; the final stages of this process are at an accelerated pace (Polanyi 1957, p. 100). The solution to the problem confirms the validity of the heuristic process (Polanyi 1957, p. 101).

Polanyi’s concepts of tacit knowing, including his discussions regarding the anticipation of solutions and sensing proximity to the answer, contributes to a more in-depth understanding of the generally recognised value of ‘know how’ required within intelligence analysis (Bruce & George 2008; Evans & Kebbell 2012; Kreuzer 2016; Lahneman & Arcos 2017; Vogel 2013a). Similarly, such discussions by Polanyi further contribute towards more completely recognising the role of ‘hunches’ in the process of analysis, particularly with his observations regarding the switching between intuitive and computational (more systematic) thinking (Polanyi 1957, p. 102). In this sense, the acknowledged value of ‘hunches’ can be recognised more completely as the prefiguring of potential solutions, followed by efforts to ‘prove’ such hypotheses (Hadamard 1945, p. 49), involving the ‘tacit powers’ of integration of the practitioner. In relation to the acknowledged need to find ways for testing the effectiveness and validity of SATs (Johnson 2005, p. 20; Walsh 2012, p. 253), Polanyi’s epistemological discussions regarding the practitioner solving problems highlights the need for recognising the role of tacit knowing within such tests.

Destructive analysis The general aim of some SATs, being to provide an audit-trail of analytical thought processes and explicitly revealing the logic behind knowledge claims, may generate some potential epistemological challenges to the process of analysis, according to Polanyi’s understanding of the process of skilfully solving problems. Polanyi’s treatment of his concept of

‘destructive analysis’ offers a critical examination for discussing epistemological aspects of the broad aims of SATs. Polanyi argues that compartmentalization (or the breaking down of issues into their constituent parts, which is involved in what he refers to as ‘destructive analysis’) is valuable for some aspects of analysis, but the practitioner should not overly depend on this form of knowing and the knowledge produced (Meek 2011, p. 240). This is because, according to Polanyi, the practitioner may fail to perceive the theory or object sufficiently as a whole. In a more technical sense, Polanyi warns that ‘destructive analysis’ can lead to the misguided focusing of attention wholly on the ‘particulars’, without sufficient recognition of ‘subsidiary awareness’, resulting in the ‘destruction of meaning’ (Polanyi 1962c, p. 601). The ‘meaning’ comes from a process of representing how all the pieces of the puzzle cohere together, involving the practitioner’s tacit powers of integration. As previously observed in the outline of SATs, the general aim of SATs is to externalise the thinking process, which arguably reveals the goal of privileging explicit knowledge within an epistemological hierarchy. The intelligence scholar Gregory Treverton made a similar warning to Polanyi’s ‘destructive analysis’, suggesting that analysts could be reduced to middle-weights if they were asked to become overly occupied with their methods and explicit regarding their logic, in the same way that chess masters can be reduced to middle- weights if asked to explain their moves (Treverton cited in Moore 2007, p. xviii). Epistemologically such techniques, which aim to be more ‘rigorous’ by elevating propositional knowledge (Agrell & Treverton 2015; Jervis 2010; Marrin 2017; Sinclair 2010, p. 5; Tang 2017), may undermine the analytical process itself by not sufficiently allowing the integration of tacit ways of knowing in this process. According to Polanyi, this would be a poor use of ‘rigour’ within a process of skilfully solving problems (Scott 1985, p. 24).

The intelligence scholar Amy Zegart drew attention to the perils of intelligence analysis as a process that does not sufficiently relate observations within the broader security environment. As Zegart observed regarding the risk assessments of Al Qaeda by the CIA before 11

September 2001, such reports ‘pointed out the trees but never provided a picture of the forest’ (Zegart 2009, p. 115). The fundamental lesson of this intelligence failure Zegart and other intelligence scholars draw attention to (Eck, Clarke & Petrossian 2013; Ellis-Smith 2016; Evans & Kebbell 2012; Hoffman et al. 2011; Wirtz 2014a) is that an improvement to the process of intelligence analysis needs to move beyond the assemblage of data and evidence towards an emphasis on skilfully integrating the observations within the security environment. Polanyi’s discussions around tacit knowing and personal knowledge contribute towards better understanding how such examples of ‘intelligence failures’, such as 9/11, can occur. According to Polanyi’s framework, such mistakes can occur because of the process and product of analysis inadequately cohering together the analysts’ tacit and personal knowledge within their assessments. This observation within the intelligence field highlights that any attempts to augment the value of intelligence analysis as a process and a product must sufficiently acknowledge and engage the tacit and personal knowledge of the respective analysts to assist with forming an accurate representation of the security environment.

It has been acknowledged within the intelligence field that since some SATs require the analyst to explicitly reveal their thought-processes, this may potentially carry certain risks of confounding the clarity of the practitioner’s thinking (Lillbacka 2013, p. 322; Vrist Rønn & Høffding 2012). Yet Polanyi’s discussion of ‘destructive analysis’ provides a more nuanced and detailed understanding of some of the specific risks of over- compartmentalisation within the process of analysis. Polanyi’s discussion of ‘destructive analysis’ offers a more in-depth treatment of the generally acknowledged observation within intelligence literature that intelligence analysis as a process should sufficiently locate the observations within the broader security environment (Margolis et al. 2012; Ouagrham-Gormley 2014; Smith 2017; Warner 2017; Zegart 2009). In this sense, Polanyi’s concept of ‘destructive analysis’ contributes towards a more nuanced understanding of epistemological considerations within the process of analysis and articulates some of the potential limitations of the use of

SATs. Specifically, Polanyi’s discussion regarding tacit knowing, involving a ‘from-to’ relation between ‘focal’ and ‘subsidiary’ awareness, articulates more precisely the aim of the process of the practitioner tacitly integrating and cohering the observations and information within the broader security environment.

Polanyi’s detailed understanding of the nature of perception, specifically that we perceive the object due to its context or environment (Polanyi 1969, p. 111), articulates the way the perils of ‘destructive analysis’, particularly over-compartmentalization, can undermine the process of intelligence analysis. Since SATs are generally intended to identify and externalise the basis of knowledge claims, this necessarily involves a system of categorizing and compartmentalizing knowledge, ranging from the type of information and data collected to the reliability of the knowledge or source. Such a process, as Polanyi recognises, can contribute to a clearer understanding of the issues. The problem of ‘destructive analysis’ occurs when this process of breaking down the issues into their constituent parts can lead to over-compartmentalization, which may undermine the capacity of the analyst to reconfigure all the information together again, to see the whole picture coherently. Wordsworth captures this sentiment of the dangers of compartmentalization within the process of analysis and understanding a subject:

Sweet is the lore which Nature brings; Our meddling intellect Misshapes the beauteous forms of things: - We murder to dissect (Wordsworth 2000, p. 131)

Since the broad epistemological logic and aim of SATs is largely devoted to promoting explicit knowledge, such an aim may undermine the process of tacit knowing, which may ‘murder’ the thinking process in order to ‘dissect’ it. This was Zegart’s key criticism of the CIA’s assessment of

the threat posed by Al Qaeda before the 9/11 attacks, namely, the report pointing out the trees but not a picture of the forest (Zegart 2009, p. 115).6

The general aim of SATs is partly to make the analyst aware of their ‘mindset’ (Muller 2007; Pherson & Beebe 2015; Richards & Pherson 2010; Wirtz 2012, 2014a), as a means for developing more objective analysis (George 2004, p. 312). SATs represent or are expressions of conceptual models, which serve to shape the mindsets of those who use such models for understanding knowledge. Conceptual models influence what is observed and how those observations are made intelligible. SATs in this regard privileges explicit or propositional knowledge, whilst simultaneously diminishing the significance of tacit ways of knowing. Such an approach to solving problems, without sufficiently acknowledging the primacy of tacit knowing within this process, may undermine not only the process but also the product of intelligence analysis. Specifically, what knowledge is generated and whether it is considered credible and acceptable. The intelligence scholar Graham Allison outlined how conceptual models shape analysis in terms of both the process and the product:

Conceptual models not only fix the mesh of the nets that the analyst drags through the material in order to explain a particular action, but they also direct the analyst to cast nets in selected ponds, at certain depths, in order to catch the fish he/she is after (Allison 1969, p. 273). The following historical analogy can serve to illustrate how conceptual models shape or sometimes dominate how problems are perceived and solved.

London in the mid-nineteenth century was in the throes of a cholera epidemic. The victim who suffers from cholera experiences extreme diarrhoea, losing many litres of water over several hours. The victim of this disease expires essentially from dehydration, as the organs of the body, which require sufficient levels of water, shut down. Despite the medical field during this period understanding the importance of hydration for healthy functioning bodies, and the observable effects of victims of cholera

6 The complete quote by Zegart is as follows: ‘Between 1998 and 2001, the CIA produced hundreds of analyses about Osama bin Laden and Al Qaeda… Not one of them provided a broad overview of Al Qaeda’s involvement in past terrorist activities, a comprehensive view of Al Qaeda strategy, or its relationships with other governments. And none assessed the nature or scale of the threat that Osama bin Laden’s terrorist network posed to the United States. CIA assessments pointed out the trees but never provided a picture of the forest.’

losing substantial volumes of water, it did not occur to them as a professional community that what was needed the most was rehydration for the victim (Johnson 2006b). Instead, what was offered was a whole range of cures that exacerbated the symptoms and accelerated the patient’s death. Solutions offered included leeching and the consumption of brandy. The medical field at the time was aware that these solutions further dehydrated the individual (Johnson 2006b).

This historical analogy from the medical field is apt, since Polanyi was himself qualified as a physician and well-versed within science, and the intelligence field has shown a keen interest in aligning itself with the medical profession and science more generally. Whilst the medical field during this time was not of the same standard of today, without adequate specialisation or sophisticated tools and methods for scientifically observing and testing hypotheses, there remains an important epistemological lesson that has a bearing on intelligence analysis. Specifically, this lesson highlights the dangers of over- compartmentalisation, which leads to inadequately integrating observations together, along with how dominant conceptual models shape (sometimes erroneously) how the problem should be perceived. The logic underpinning the use of SATs is to promote more ‘rigour’ within the problem-solving process (Chang et al. 2017, p. 4), which may lead potentially to the perils of ‘destructive analysis’, the outcome of which could be the exacerbation of the problem by not sufficiently integrating the information and data together to generate a coherent picture overall of the security environment. SATs in this sense may be akin to the solutions offered to those who suffered from cholera in London in the 19th century, i.e. remedies that (knowingly) exacerbated the symptoms rather than address them.

As Polanyi observed, our ‘interpretative framework’ is deeply implicated not just in how we discover but also what we perceive (Fennell 2016, p. 47). The logic underpinning the use of SATs may represent a broader conceptual model in this sense, which privileges analytical processes that are externalised whilst also diminishing the value of non-

externalised tacit knowing. The lack of research to demonstrate empirically that SATs improve the process or product of analysis should raise some scepticism around such techniques (Artner, Girven & Bruce 2016, p. 4; Borg 2017; Chang et al. 2017, p. 4; Gentry 2015; Heuer Jr 2009). With respect to Polanyi’s discussions regarding a skilful performance, this lack of demonstrating the effectiveness of SATs for improving intelligence analysis could be because such techniques fail to sufficiently acknowledge or engage tacit ways of knowing within the process of solving problems. Polanyi’s perspective of how the practitioner skilfully solves problems re- casts considerations around the tacit operations of the practitioner in the process of solving problems, and highlights some of the issues of over- compartmentalisation in this process. These views in relation to intelligence analysis and SATs in particular shed light on some of the epistemological problems some analytical techniques may pose for the analyst as they attempt to solve problems.

Conclusion As Polanyi makes clear, tacit knowing is central to the process of solving problems, the contents of which are indeterminate, and some of which can only be known ‘tacitly’ (Polanyi 1969, p. 140), defying complete propositional or explicit articulation. Because of this, Polanyi argued the best way to recognise the centrality of tacit knowing within the process of solving problems is in relation to examining epistemological considerations regarding a ‘skilful performance’. Polanyi’s discussions in this regard, which involves the practitioner engaging their tacit knowing (Mullins 2002, p. 206), provide an enriched outlook for similarly understanding the process of analysis within intelligence analysis. Polanyi provides a way for understanding the epistemological process of solving problems with tacit knowing being fundamental to this position (Polanyi 1969, p. 140). The key to Polanyi’s argument of the centrality of tacit knowing within the process of solving problems is not just that there are things ‘we know but cannot say’ (Polanyi 1969, p. 195), but the relationship between tacit and explicit knowledge. Specifically, the process of solving problems involves an epistemological orientation of what Polanyi describes as a ‘from-to’

relation. The practitioner moves ‘from’ their focal awareness ‘to’ their subsidiary awareness within the process of solving problems. For example, in the process of reading a text, we move from the words to their intended meaning. Polanyi expands upon this discussion of the ‘from-to’ relation of tacit knowing within the process of a ‘skilful performance’ with his concept of ‘indwelling’. This involves the practitioner skilfully assimilating the tools and methodologies selected so they form a part of their being, as an extension of an awareness of themselves (Prosch & Polanyi 1975, p. 38). For example, the investigator ‘dwells’ in the case they are pursuing, by assimilating the collection of evidence, clues and theories for explaining how it relates together. Polanyi’s discussions regarding this relationship between the practitioner’s ‘focal’ and ‘subsidiary’ awareness, along with his concept of ‘indwelling’, provides a more nuanced understanding of ‘know how’ within the process of intelligence analysts solving problems.

Polanyi casts some doubt towards systems of thinking, or tools that promote certain ways of thinking, which insufficiently engage or undermine tacit knowing within the process of solving problems. Since the general aim of SATs is arguably to promote explicit knowledge over tacit knowing (George 2010; Khalsa 2009; Marrin 2008, 2017; Martin et al. 2011a; Richards & Pherson 2010, p. 5), Polanyi’s epistemology raises some questions around such analytical tools. SATs may hold some value for unpacking certain issues for analysis (see Appendix 2)7 (Miller 1956; Rasanen & Nyce 2013; Heuer 2008b; Varela, Thompson & Rosch 2017), and in this sense these analytical tools may provide assistance from a cognitive point of view (CIA 2009; Trent, Patterson & Woods 2007; Folker 2000, pp. 1-2; Moore 2007). Yet Polanyi’s concept of ‘destructive analysis’ contributes a detailed epistemological warning regarding the perils of methodologies or tools, potentially some SATs, which can lead to the over-compartmentalization of the constituent elements being examined.

7 Philip Mudd provided permission to use this email in the thesis. His career included being the Deputy Director of the CIA Counterterrorist Centre. Transitioning to the FBI, he became the first Deputy Director for the National Security Branch in 2005, later adopting the role as a Senior Intelligence Advisor.

The warning of destructive analysis does not apply to all SATs equally. For example techniques like ACH or Devils’ Advocate are principally designed to imagine other ways of understanding issues or possible scenarios. On the other hand, SATs which are designed to have the analyst make their methods and logic explicit, by providing more ‘rigour’ via an audit trail of the analyst’s thought processes, possess a greater propensity (by design) to lead to the negative aspects of destructive analysis.

Such analytical tools may undermine the capacity of the practitioner to reconstitute these compartmentalized elements, reducing their capacity to cohere their ‘focal’ with their ‘subsidiary awareness’ (Polanyi 1962a, pp. 52-4). In this sense, Polanyi’s technical account for recognising the potential dangers of ‘destructive analysis’ sheds some critical light on the use of SATs, specifically with respect to tools that may undermine the process of analysis – by pointing out the ‘trees’ but not a complete picture of the ‘forest’ (Zegart 2009, p. 115). Sufficiently integrating and synthesising all the information and data together to develop a coherent picture of the security landscape is the defining characteristic of ‘quality analysis’ as a process both within the national security and law enforcement fields (Eck, Clarke & Petrossian 2013; Evans & Kebbell 2012; Nolte 2012; Wood et al. 2017; Zegart 2009). Taken together, Polanyi re- casts a perspective for more precisely understanding the epistemological primacy of tacit knowing with respect to the process of a ‘skilful performance’ for solving problems. Whilst the national security and law enforcement fields acknowledge that the process of solving problems within intelligence analysis involves ‘know how’, Polanyi contributes to this acknowledgement by providing a more in-depth epistemological account for the process of how we know.

Chapter 4: Personal knowledge

Introduction Taking a synoptic or broader view of the discipline of intelligence analysis, which can be characterised as a ‘knowledge-building’ activity (Bruce 2008, p. 171), it can be observed that the heart of the field is driven by epistemological considerations. The previous chapter focused on exploring Polanyi’s concept of a ‘skilful performance’, particularly in relation to developing a more nuanced understanding of skilfully solving problems, including the use of tools within the process of analysis. The aim of this chapter is to explore Polanyi’s concept of personal knowledge with respect to more completely perceiving what it means for the intelligence analyst to ‘know’ something as a knowledge product. Polanyi’s concept of personal knowledge contributes towards developing a more epistemologically robust comprehension of knowledge claims. The bearing this has for intelligence analysis is in relation to discussions regarding the ‘tradecraft’ or the discipline of this field. To examine this, a discussion of some key epistemoligies will be outlined, providing some context to Polanyi’s concept of personal knowledge and what this standpoint means in relation to what an intelligence analyst may claim to ‘know’. This will include exploring Polanyi’s arguments regarding the traditional distinctions within epistemology of ‘genuine knowledge’ and ‘belief’. Polanyi’s concept of personal knowledge will be further examined in relation to better understanding the education and training within the intelligence profession, by highlighting the primacy of the analyst, like the scientist, and of learning from their own practice and ideally under gifted masters in the field. Especially with respect to the general aim within the intelligence analysis field to become more closely aligned with ‘scientific’ principles, Polanyi’s concept of personal knowledge contributes towards this discourse by his re-casting of what it means to ‘know’ something, with the ‘knower’ being centre-stage for such knowledge products.

The School of Athens – Ways of Knowing The keen interest within intelligence analysis for understanding precisely what it means to ‘know’ something in terms of the intelligence ‘product’ is captured by former Secretary of State and U.S. Army Chief of Staff General Colin Powell, in the lead up to the 1991 Gulf War, directing the Intelligence Community to: ‘Tell me what you know, tell me what you don’t know, tell me what you think, and make sure you clearly distinguish cases’ (Powell cited in Whitney 2005, p. 204).

John Hamre, former US deputy Secretary of Defence, echoed this view of making these important epistemological distinctions, with reference to the incorrect estimate of Iraq’s WMD program:

Another observation I would make concerns what philosophers call epistemological questions: How do we know what we know, and how good is the information that comprises this knowledge? Is it reliable? Is it true? This is the core of the intelligence community’s problem (Hamre cited in George & Bruce 2008, p. 135). Some policy experts and political scientists routinely claim that, particularly in relation to the development of WMDs, such scientific knowledge is explicit (Ouagrham-Gormley 2014, p. 17). Acknowledging Polanyi’s concept of tacit knowing, Sonia Ouagrham-Gormley, in her study into the acquisition and use of specialised knowledge in relation to the development and identification of biological weapons, clearly indicates that what it means to ‘know’ something goes beyond ‘explicit’ knowledge (Ouagrham-Gormley 2014, pp. 17-36). Polanyi’s concept of personal knowledge, involving ‘tacit’ knowledge, provides a more robust epistemological account for understanding what it means to ‘know’ something, which necessarily goes beyond propositional or ‘explicit’ knowledge claims.

Since intelligence analysis is designed to reduce ignorance (Omand 2014, p. 22) with respect to what it means to ‘know’ something, exploring some of these broader concepts epistemologically will help with understanding intelligence analysis as a product. As acknowledged within the intelligence field, making these epistemological distinctions as outlined by Powell springs from meaningful analysis, achieved not simply by the

ability to recall or arrange the facts, but developing patterns that relate those facts to the broader concepts, and to engage procedures that facilitate this process (Elstein 1978, p. 276; Mathams 1988, p. 16). Polanyi’s concept of personal knowledge articulates the importance of the ‘knower’ within knowledge claims, which emphasises that the manner by which the ‘broader concepts’ are synthesised into understanding knowledge as a product should sufficiently acknowledge the ‘knower’ within such acts. Polanyi’s concept of personal knowledge serves to better understand what it means to ‘know’ something epistemologically as a knowledge product.

Intelligence analysis as a field shares many important epistemological similarities with science (problem-solving, making discoveries, skilful use of tools, verification of knowledge claims) and holds greater interest in a posteriori knowledge, rather than a priori knowledge (Agrell & Treverton 2015; Dexter, Phythian & Strachan-Morris 2017; Lahneman & Arcos 2017; Lillbacka 2013; Manjikian 2013; Vrist Rønn & Høffding 2012). Both a priori and a posteriori refer mainly to how, or on what basis, a proposition may be known (Greco & Sosa 2002, pp. 243-70). Both intelligence analysis and science alike focus on knowledge acquired from empirical observations, of knowledge that is characteristically a posteriori. In contrast, a priori knowledge develops knowledge not from relying on observations, but instead by reasoning about aspects of reality and developing maxims or principles to better understand reality. Examples of knowledge claims that are characteristically a priori include: cubes have six sides; blue is a colour; water is composed of H2O. These claims are true, but the truthfulness of such claims does not depend on empirical observations. These claims can be justified independent of observations (Audi 2010). These epistemological considerations, for what it means ‘to know’ something, carry implications both for understanding knowledge-processes but also for knowledge-products. With respect to intelligence analysis, these considerations are at times implicitly considered, regarding managing bias and uncertainties within complex

information systems (Bammer 2010; Duvenage 2010; Hoffman et al. 2011; Lowenthal & Marks 2015; Smith 2017).

It is fitting to use some aspects of Raphael’s masterpiece The School of Athens to explore some distinctions between the two fundamentally different forms of logic, being ‘induction’ and ‘deduction’, since Aristotle (featured prominently in this artwork) is perhaps the first philosopher in the West to have developed a rudimentary theory of tacit knowledge (Mohajan 2016, p. 8).

Figure 2: The School of Athens

Raphael (Winner 2012, p. 290)

For the purposes of this thesis, this artwork contains a visual representation capturing the two main philosophical views of gaining knowledge,

featuring the philosophers Plato and Aristotle in the centre. Plato is pointing towards the heavens, emphasising the rationalist mode of thinking – of logic and reason as the golden standard for generating knowledge. In contrast, Aristotle is pointing downwards towards the earth, emphasising empiricism, or insight generated from observations, as the most suitable

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path to gaining knowledge. These two broad modes of thinking hold diverging views regarding what it means to ‘know’ something. As will become clear, the standpoint characterised by Plato emphasises the rational form of knowledge, in contrast to Aristotle who emphasises observable evidentiary content of knowledge. Polanyi’s concept of personal knowledge, like science in general, sits between these two broad schools of thought. According to Polanyi’s concept, knowledge claims involve elements of both rationalism and empiricism. Yet importantly, in relation to intelligence analysis, the analyst should sufficiently recognise that knowledge claims should be understood and guided by the ‘knower’. This framework emphasises, in terms of forms of logic, engaging in both deduction and induction – along with appreciating their own personal knowledge. Knowledge claims also require, according to Polanyi, adequately taking into account the validation or acceptance of the community of practitioners within the respective tradition. Turning to some broader philosophies around what it means to ‘know’ something will provide greater context to Polanyi’s concept of personal knowledge and the bearing this has for understanding the discipline of intelligence analysis.

Plato’s emphasis on form places the primacy of logic and reason as the best means for gaining knowledge. Knowledge claims and knowledge processes are ‘correct’ as a product insofar as the process is adequately reasonable or logical. In terms of formal logic distinctions, this standpoint emphasises ‘deduction’. If the premises are correct, the answer must follow. With reference to intelligence analysis, efforts for generating universal standards and ‘rational’ processes for analysis could be characterised as being philosophically aligned to the position of Plato in this sense – of what could be described as a ‘top down’ approach. Efforts by some scholars to develop or discuss a universal intelligence analysis model (Dahl 2012; Marrin & Clemente 2006, pp. 642-665) are akin to this broader epistemological ‘top down’ approach. Such efforts for developing a ‘cohesive analytical identity’ (Kerbel 2008, p. 107), have been likened to an aim which is arguably a desire for artificial precision (Herbert 2013, p.

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654). Polanyi’s concept of personal knowledge and his broader discussions regarding the absence of ‘rules’ within science articulate more precisely why efforts for imposing ‘universal’ views or standards of science are misguided (Polanyi 1962a).

In contrast to the Platonic ‘top-down’ approach to understanding knowledge claims, there is the ‘bottom-up’ foundation as represented by Aristotle. Aristotle placed greater emphasis on empirically making observations, guided by the logic of induction for developing knowledge claims. The Canadian philosopher Louis Groarke explains Aristotle’s reasoning regarding why he advocated empiricism and induction as a means for generating knowledge claims and making discoveries:

On the Aristotelean account, induction is not a matter of proof but of discernment. We observe this or that instance and come to see the underlying principle at work… We do not prove through tabulation; we recognize, in a flash of illumination, what must always be the case (Groarke 2009, pp. 366-67). What this means in relation to intelligence analysis, to the extent to which analysts engage in empirical observations and utilise the logic of induction (Agrell & Treverton 2015; Lahneman & Arcos 2017; Marrin 2016; Mole 2012; Prunckun 2015), is that empiricism and induction operate as a heuristic method. Furthermore, there is the more recent addition of ‘abduction’ as a form of reasoning which has been acknowledged within the intelligence literature. Abduction refers to the best reasoning based on limited observations (Moore, 2007; Mullins, 2002; Fennell, 2016). Epistemologically, this is consistent with the claim that analysts operate as a ‘bricoleur’ (Innes, Fielding & Cope 2005, p. 50; Lynch 1993), specifically by recognising that they work with what analytical tools and data and information is available to them. This form of reasoning has become particularly relevant given the expansion of ‘open source’ material as engaged by intelligence agencies (Gaudet, 2013 , pp. 169; Zegart, 2009; Van Puyvelde, Coulthart, & Hossain, 2017b; Lowenthal, 2008). Polanyi’s understanding of knowledge more generally, of making decisions on incomplete information, is consistent with the principles of abduction.

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Polanyi’s concept of personal knowledge articulates epistemologically that within the field of intelligence the work of the analyst is too diversified for there to be a single overarching ‘top down’ approach to understanding knowledge claims as a product (Bang 2017; Chang & Tetlock 2016; Gentry 2016; Herbert 2013, p. 653; Kerbel 2008). According to Polanyi, since there is no ‘scientific method’, the scientist must draw on their personal knowledge, which importantly posits that knowledge claims must sufficiently acknowledge the role of the ‘knower’. This is the central argument underpinning the idea of a bottom-up understanding of what it means to ‘know’ something. Knowledge claims are affirmed by the ‘personal coefficient’ of the analyst’s personal knowledge, which according to Polanyi, is a fundamental feature of what it means to ‘know’ something (Polanyi 1962a, p. 267). Polanyi’s arguments in relation to the authority of science as a valid form of inquiry and way of understanding knowledge claims as an enterprise further highlights the bottom-up way of perceiving knowledge as a product. According to Polanyi, the ‘authority of scientific opinion’ is ‘essentially mutual’, being ‘established between scientists, not above them’ (Polanyi 1969, p. 56, emphasis in original). The authority of knowledge claims can therefore be characterised as being bottom-up, according to Polanyi’s view. This perspective has a bearing on the intelligence analysis discipline by offering an alternative way of considering a broad range of epistemological issues, principally in relation to what it means to ‘know’ something.

Just as there are epistemological considerations regarding the process of solving problems, as examined in Chapter 3, these ways of knowing carry over to what is known as a knowledge product. More precisely, the methodology and ‘skilful performance’ not only shapes how knowledge is generated, but also provides an epistemological basis for understanding the knowledge as produced. Whilst this is generally acknowledged within the intelligence field regarding ‘mindsets’ and ‘conceptual models’ (Allison 1969, p. 273; Muller 2007; Pherson & Beebe 2015; Richards & Pherson 2010; Wirtz 2012, 2014a), Polanyi’s contribution to this recognition is in relation to clarifying the primacy of the

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knower within knowledge claims. As Polanyi makes clear, since science is an enterprise that combines the logic of deduction, induction, and empiricism within the production of knowledge, it becomes a matter of emphasis regarding how the knower understands knowledge claims (Doing 2011; Gelwick 2005; Henry 2010; Polanyi 1966b). In relation to intelligence analysis as a ‘knowledge-building’ enterprise, knowledge claims should sufficiently acknowledge the knower, according to Polanyi’s understanding.

The outline of the distinctions between analysis and synthesis, as observed in Chapter 1 by Ed Waltz, served to highlight that the process by which problems are solved influences or shapes the knowledge produced. As Waltz argued, analysis operates by searching for presumed effects (possible solutions) backwards, or looking for causes that could produce a certain effect. Once the causes are known for producing a certain effect, the process amounts to the validation of a hypothesis. In contrast, synthesis proceeds by a position of theoretically generating plausible causes, by constructing or assembling the cause-to-effect chain – aiming to prove a hypothesis (Waltz 2003, p. 160). With reference to the School of Athens, there is Aristotle pointing towards the earth, representing analysis by referring to the evidence as the primary means to understand the effect-to-cause. In contrast Plato, who is pointing towards the heavens, represents the synthesis – the solution is in some respects considered to be already ‘known’ and the evidence is explained with reference to the overarching accepted theory or model (cause-to-effect). Polanyi’s epistemology, involving his concept of personal knowledge, contributes to this discussion with an understanding of knowledge as a product; specifically, the need to acknowledge that there is always a ‘personal coefficient’ (or bottom-up) affirmation of knowledge claims (Polanyi 1962a, 1969).

To tether these discussions around the broad top-down or bottom- up perspectives for understanding knowledge as a product, it would be useful to examine an example in the intelligence field. The CIA’s post- mortem analysis surrounding the Cuban Missile Crisis provides a suitable

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example of clearly observing some of the epistemological limitations with the top-down understanding of knowledge claims. In Sherman Kent’s words, ‘We missed the Soviet decision to put missiles into Cuba because we could not believe that Khrushchev could make a mistake’ (Kent 1964). Sherman Kent’s dismissal of the flawed analysis and report of the Cuban missile estimate ‘proved that the CIA was right and it was Khrushchev who had been in error!’ (Agrell & Treverton 2015, p. 72). Kent defended the methods, logic and reasoning behind the analysis. It was reality that was at fault; no amount of observations and inductive logic – before or after the result – could challenge Kent’s conviction that the process and logic which led to the (failed) analysis was not incorrect or at fault. This is because Kent’s epistemology emphasised the top-down understanding of knowledge. Specifically regarding the generally accepted conventions and rules regarding how international leaders could (or should) understand what is in their best interests. Such normative arguments spring from the top-down approach to understanding knowledge as a product. In this sense, clues and evidence that do not ‘fit’ with the accepted logic are demonstrably dismissed.

The key point here should not be that intelligence analysts can get it wrong, since it is a given this is inevitable (Betts 2009; Bruce & George 2008; Marrin 2004; Pherson 2013; Smith 2014; Wastell 2010). The facts and clues can at times be interpreted in an indefinite variety of ways, as can be observed by Michael Hayden’s (former Director of CIA and NSA) quip: ‘If it were a fact, it wouldn’t be intelligence’ (Hayden cited in Jervis 2010, p. 1). The real issue at stake relates to the basis from which knowledge claims are understood as knowledge ‘products’. In the case of the Cuban missile crisis, the incorrect intelligence report (in terms of the knowledge produced) was the outcome of mistaking provisional knowledge for actual knowledge (Agrell & Treverton 2015, p. 66). The comments by Sherman Kent in relation to the post-mortem report tellingly reveals that the top-down position for understanding knowledge claims, which emphasises established rules and maxims for comprehending the security environment, has an important bearing on understanding what it

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means to ‘know’ something. Polanyi’s concept of personal knowledge contributes towards epistemologically understanding the centrality of the knower within knowledge claims, or the bottom-up way of viewing knowledge claims. In this sense, Polanyi’s concept of personal knowledge provides an epistemological account for recognising some of the perils of understanding knowledge claims because of orthodox, or seemingly established rules or maxims (the top-down model).

The concept of personal knowledge, which is characteristically a bottom-up way of understanding knowledge claims, has a bearing on some contemporary challenges facing criminal intelligence analysts. For example, it can be argued that the epistemological challenges facing criminal intelligence analysts in relation to emerging cybercrimes partly indicate that the top-down established or traditional ways or rules for investigating these crime types are not useful. Specifically, the difficulties facing criminal intelligence analysts regarding identifying suspects in the cybercrime ‘virtual’ world, prioritizing avenues of inquiry, or attributing evidentiary guilt of an alleged offender, all require a clear understanding of what it means to ‘know’ something, in this case within the virtual online space (Collins & McCombie 2012; Décary-Hétu & Dupont 2012; Lanier & Cooper 2016; Leppänen & Kankaanranta 2017; Lyda & Hamrock 2006; Smith & Ingram 2017; Wall 2007). Polanyi’s concept of personal knowledge, which emphasises a ground-up approach to understanding knowledge claims, based on the experiences of the practitioners in the local tradition, may serve to more clearly understand the challenges facing law enforcement in the cybercrime field.

The bottom-up approach emphasises the value within knowledge claims of intuition and ‘hunches’ and modes of thinking which engage in non-formalised logic (Gigerenzer & Brighton 2007; Dreisbach 2011; Ferguson 2012; Horan 2007; Lahneman & Arcos 2017; Lerner 2007; Vrist Rønn & Høffding 2012). With respect to understanding ‘intuition’ with ‘tacit’ knowledge, Polanyi used the terms at times interchangeably (Polanyi 1963, p. 274). In some areas Polanyi made the point that the process of knowing could involve intuition, yet importantly acknowledged that such a

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form of knowledge is characteristically ‘tacit’, of being difficult to articulate completely (Polanyi 1963, p. 139).

This mode of thinking which elevates tacit knowledge and the understanding of the knowledge produced, was promoted by Carmen Medina, who was responsible for making a series of key knowledge sharing innovations within the CIA.8 Medina argued that for analysts to keep abreast of the fast-changing security environment they must be adventurous and be intuitive (Medina 2002, p. 3). Polanyi’s view contributes to this argument, by further articulating the way the knowledge produced by the investigator is closely related to the engagement of their tacit and personal knowledge (Polanyi 1962a). Importantly, Polanyi cautions that the scientist, and by extension the intelligence analyst, should be aware that induction and empiricism is often overstated within the path to making discoveries (Polanyi 1962a, p. 177). With respect to contributing to the discourse within intelligence analysis of what it means to ‘know’ something, Polanyi presents the view that ultimately the person involved in the process of problem-solving must draw on their personal knowledge and there should be a sufficient acknowledgement of the knower within understanding the knowledge products. This concept of personal knowledge further builds on arguments raised in Chapter 3 regarding the process of skilfully solving problems. As Polanyi articulates, there is a nexus between tacit knowing (or how we know as a process) and the need to recognise the personal knowledge of the knower within knowledge as a product (Fischer & Mandell 2009; Lemaitre 2017; Massingham 2016; Polanyi 1962a, 1966b; Wright 2016).

Genuine knowledge and belief The intelligence profession, like the broad field of science (Agrell & Treverton 2015; Rasanen & Nyce 2013), maintains that there is an important distinction between inferences characteristically based on ‘belief’

8 Carmen Medina was one of the leading instigators of Intellipedia, an online collaborative tool akin to Wikipedia, which is considered one of the most significant advancements for the intelligence community in the United States in terms of knowledge sharing. For more details of her innovations over her 32 years at the CIA, see http://usgif.org/about/board/20-carmen-medina.

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and judgments founded by ‘genuine knowledge’ (Wing 2000). One of the analyst’s core task involves ‘distinguishing genuine knowledge from mere belief,’ requiring a ‘demonstration of a clear connection between judgments and beliefs’ (Herbert 2006, p. 667). Herbert claims that the analyst requires a systematic effort to achieve this task of distinguishing between knowledge and belief. Otherwise, the analyst could succumb to ‘layering’ and ‘analytic bias’, the outcome of the ‘repacking of . . . highly processed judgments for later use as plain and simple facts’ (Herbert 2006, p. 669).

This raises the question regarding the difference between what one ‘knows’ and what one confidently ‘believes’. As Herbert asked, ‘How does someone delimit a relevant articulation of what he or she does not know?’ (Herbert 2006, p. 667). Polanyi’s contribution to this discussion is that there is always the personal participation within acts of understanding, but this does not make such understanding subjective (Polanyi 1962a, p. 1). Polanyi, like the Psychologist Carl G. Jung, attempted to bridge the tension between the inner subjective experience and outer scientific ways of producing knowledge (Hall 1982, p. 1). In Polanyi’s words, ‘The act of knowing includes an appraisal; and this personal coefficient, which shapes all factual knowledge, bridges in doing so the disjunction between subjectivity and objectivity’ (Polanyi 1962a, p. 17). Along the spectrum of knowledge as a product, Polanyi claims there are three types, being the following: objective, subjective and personal. Polanyi explains the distinction between objective and subjective knowledge by way of outlining the nature of a theory, as being ‘something other than myself’ (Polanyi 1962a, p. 4). The more a set of rules can be articulated and laid out in writing, the more it is characterised as a ‘theory’ (Polanyi 1962a, p. 4). The accuracy of a theory or lack thereof is independent of the individual examining or utilising it. Polanyi uses the analogy of a theory as similar to a map – which the scientist can use to find their way through uncharted territories. The accuracy of the map is independent of the user, and as such can be considered a form of knowledge that is outside the individual and is ‘objective’. ‘Objective’ forms of knowledge, such as a map, are

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impersonal (Polanyi 1962a, p. 4). Such an understanding of ‘objective’ and impersonal knowledge can be observed especially within the ‘hard’ sciences, such as physics and chemistry (Gillispie 2016; Karpenko & Claggett 2017; Weber 2017).

Polanyi diverges from the usual distinctions of understanding ‘objective’ and ‘subjective’ forms of knowledge to what he proposes as personal knowledge. This form of knowledge is neither objective nor subjective. Since the scientist submits to the requirements within their field, independent of them, personal knowledge is not subjective; and since the knowledge created by the practitioner’s actions is directed partly by their own efforts, the knowledge produced is not objective either (Polanyi 1962a, p. 300). It is a commitment to uncovering the truth that can safeguard the personal knowledge of the scientist from being merely subjective (Polanyi 1962a, p. 65). This sentiment is captured well by Polanyi’s explanation of the ‘heuristic passion’, an important aspect of personal knowledge:

Intellectual passions do not merely affirm the existence of harmonies that foreshadow an indeterminate range of future discoveries, but can also evoke intimations of specific discoveries and sustain their persistent pursuit through years of labour. The appreciation of scientific value merges here into the capacity for discovering it; even as the artist’s sensibility merges into his creative powers. Such is the heuristic function of scientific passion (Polanyi 1962a, p. 143). Polanyi made the distinction between our personal commitments and our subjective states, in which we endure our feelings. This is the nexus that constitutes the personal, which is neither subjective nor objective (Polanyi 1962a, p. 316). According to Polanyi’s view of science involving personal knowledge, the approximations to the truth can only be considered and understood by believing in an objective reality (Polanyi 1962a, p. 322). Polanyi’s concept of personal knowledge springs partly from his view of perception, which involves an active commitment and exercising of skill (Polanyi 1962a, p. iv). Polanyi’s logic of personal knowledge re-conceptualises the traditional epistemological distinctions between ‘genuine knowledge’ and ‘belief’ – which has a bearing within intelligence analysis regarding what it means to ‘know’ something.

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With respect to understanding the value of the knowledge product, some intelligence scholars have cautioned against the use of non- systematic heuristics within intelligence analysis (Dreisbach 2011; Herbert 2006, pp. 675–679; Pherson & Beebe 2015; Richards & Pherson 2010), due to the potentially unreliable knowledge produced (Dreisbach 2011, p. 758; Ferguson 2012; Martin et al. 2011a; Martin et al. 2011b). Other scholars have argued that systematic methods of analysis may discourage ‘gut instinct’ and can generate ‘sterile’ knowledge products and lack imagination (Sims cited in Johnson 2010, p. 399). Polanyi’s concept of personal knowledge contributes to recognising more completely the value of non-systematic heuristics for solving problems and understanding the knowledge produced (Polanyi 1957, p. 103). This is because, according to Polanyi, the generation of knowledge products cannot always be based on or relied on by formulated (or top-down) precepts, since no definitive propositional precepts or rules may exist (Polanyi 1964, p. 33-4).

It is not surprising that intelligence analysis as a field has sought to align itself as a discipline with science, since science and intelligence alike are concerned with both ‘verification’ and ‘falsification’ of knowledge claims (Agrell & Treverton 2015; Hicks 2013; Marrin 2011b; Legrand & Vogel 2012; Wirtz 2012). Efforts within the intelligence field to align the discipline of analysis with the aims of science, notably of ‘falsification’, have been promoted by several intelligence scholars (Lillbacka 2013, p. 307; Marrin 2017; Mathams 1988; Pherson & Beebe 2015; Prunckun 2015; Richards & Pherson 2010). With respect to the acknowledgement that different types of logic play a significant role in shaping the knowledge produced (Allison 1969, p. 273; Gaudet 2013; Hudson 2009; Turner 2014), this has a bearing regarding how the knowledge produced can be understood. Induction involves the process of discovery and the verification of a generalisation, depending on the availability of data and observations (Mathams 1988, p. 17; Tang 2017; Ylikoski 2017). Deduction is aimed towards the development of universal rules within a closed system (such as mathematics, physics). Yet, since intelligence analysis rarely deals with closed systems for analysis, it has been acknowledged

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within the intelligence field that there should be some caution for engaging in deduction (Jensen 2018; Mathams 1988, p. 17; Miller 2011; Wirtz 2012). This acknowledgement is largely the reason why some advocates of structured methodologies argue that intelligence analysis, in terms of the knowledge produced, can only approximate the discipline of science (Khalsa 2009; Marrin 2011b, p. 21; Marrin & Clemente 2005, p. 711; Spielmann 2014). Yet, as Polanyi makes clear, central to understanding knowledge claims within science is sufficient acknowledgement of personal knowledge, partly because there are no ‘rules’ in science. Because of this, the scientist, according to Polanyi, must draw on their personal knowledge to make decisions regarding, for example, whether evidence or clues are to be accepted or dismissed.

Polanyi’s contribution to this discussion regarding the role of different types of logic in understanding knowledge claims can be observed in his treatment of empiricism in the understanding of knowledge claims within science. These arguments presented by Polanyi have a bearing on both the national security and law enforcement fields of intelligence analysis, since these areas utilise empirical observations for developing and understanding knowledge claims (Dupont 2017; Evans & Kebbell 2012; James et al. 2017; Peters & Cohen 2017; Silverman 2007). With respect to the national security field, the centrality of empiricism can be observed with reference to the existence of large intelligence-gathering systems. Such systems are driven in part because of the belief that with more information and data collected the pieces of the puzzle will more clearly reveal the answers (Kevin O'Connell cited in Agrell & Treverton 2015; Eck, Clarke & Petrossian 2013; Treverton et al. 2006, p. 14). Polanyi challenges the epistemological basis for according undue credence to the alleged central role of empiricism and the logic of induction in science. As Polanyi observed, ‘The part played by new observations and experiments in the process of discovery in science is usually over-estimated’ (Polanyi 1964, p. 29). Such a view stands against the conventional understanding of science, as promoted by Karl Popper. Popper argued that the human mind is naturally inclined to think

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inductively, which provides the opportunity to find regularity in the world, yet generates unreliable knowledge, whereas the logic of deduction is contrary to the natural process of thinking (Popper 1966 pp. 23–27, 37–44, 66, 98, 104–105).

This popular view of science and how the knowledge produced by such a ‘scientific’ basis can be observed within intelligence literature. For example, Professor David Carter stated that the knowledge produced by intelligence analysis, like the discipline of science, involves the use of ‘…inductive and deductive reasoning…’ (Carter cited in Marrin 2012c, p. 531). Polanyi referred to two statements to outline this popular view of induction within the philosophy of science: ‘The philosopher of science is not much interested in the thought processes which lead to discovery…’ (Reichenbach cited in Einstein 1949, p. 289); or ‘The gist of the scientific method is…verification and proof, not discovery’ (Mehlberg 1955, p. 127). Polanyi argued that philosophers of science extensively refer to induction as an intellectual instrument for making discoveries and understanding knowledge claims, but when they at times realise that this is not how discoveries are made, or how knowledge claims are understood, they dismiss these instances by relegating such criticisms to psychology (Polanyi 1962a, p. 14). Polanyi opposed this popular view of science with respect to the logic of induction and empiricism to make discoveries and comprehend knowledge claims. This has an important epistemological bearing on intelligence analysis in terms of understanding what it means to ‘know’ something. Specifically, Polanyi more accurately represented the role of empiricism and induction within science, which recasts how to understand knowledge products within intelligence analysis.

Polanyi’s argument that scientific discoveries and the knowledge produced cannot be accounted for with reference to the accumulation of empirical observations and inductive logic articulates a similar position as understood from the intelligence profession regarding the limited value of empiricism and induction within understanding knowledge claims (Eck, Clarke & Petrossian 2013; Evans & Kebbell 2012; Wood et al. 2017; Zegart 2009). For example, as Roberta Wohlstetter observed regarding

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the case study of the intelligence failure of Pearl Harbor, such a failure occurred despite the possession of arguably9 sufficient data and information about Japanese military movements and intercepted communications. Yet, analysts were not able to separate the ‘signals’ from the ‘noise’ (Wolhstetter 1962), or generate a clear picture of what the existing information and data amounted to.

The analysts failed to produce ‘quality analysis’ as understood by O’Connor and McDermott; more specifically, analysts did not sufficiently synthesise the information to understand the security environment in context (O’Connor & McDermott 1997b; Silver 2012). Polanyi epistemologically articulates that empiricism and induction, whilst they serve a useful purpose up to a point, do not completely capture what it means to ‘know’ something, which requires sufficient recognition of the personal affirmation of knowledge claims (Polanyi 1962a).

Other leading figures within the intelligence field, such as Carmen Medina from the CIA and former US Secretary of Defence Colin Powell, have recognised the limitations of knowledge as a product that has been based on an undue reliance on empiricism and induction as a way of generating knowledge claims. Medina outlined and criticized the emphasis at the CIA on aggregating data and looking for new developments (based on empiricism and induction) which could provide insight (Medina 2002, p. 24). Within a similar spirit, Colin Powell, shortly after taking office, cancelled the production of The Secretary’s Morning Summary, the daily compilation of updates of overseas developments. Powell wanted ‘think pieces’ instead, which could provide more valuable knowledge in relation to contextualizing knowledge claims within the security environment (O’Connor & McDermott 1997a, p. 12) and therefore promote a clearer understanding (Johnson 2010, p. 401). Taken together, Polanyi’s contribution to this discussion relates to his emphasis on what it means to ‘know’ something, with the individual knower being centre stage. Polanyi challenged the orthodox understanding that science generally progresses

9 It is widely recognised within the intelligence field of the principle of ‘hindsight bias’, of the view that most events will appear clearer after the event has occurred.

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along by empirical observations and inductive logic. In this sense, Polanyi’s discussions regarding the nature of science and knowledge claims provides an enriched epistemological account of the criticisms raised by some within the intelligence field, notably of Powell and Medina, regarding the challenges to the value of empiricism and induction for understanding the security environment. To better understand knowledge claims, in the manner Medina and Powell intended, Polanyi’s articulation of personal knowledge outlines a foundation for sufficiently acknowledging the knower within knowledge claims. This concept provides a nuanced epistemological basis for recognising the ‘personal coefficient’ within knowledge claims and more completely recognising knowledge as a product (Doing 2011; Henry 2010; Polanyi 1966b).

Tradecraft: training and education Training and education for any profession or field of study can be understood as being aimed towards refining the process of skifully performing tasks and also the delivery of a quality end product (Giurgiu, Barsan & Mosteanu 2015; Pirolli 2006). Training and eduction are therefore directed towards developing the means, or the process of knowing, and the ends, or how the practioner comes to ‘know’ knowledge claims. It is the latter aim of training and education that this chapter will be focussed on.

With respect to how intelligence analysis is understood as a discipline, there are important considerations regarding what guiding principles should inform the teaching and learning of this profession. Part of the challenge behind deciding on such principles relates to the acknowledged ambiguity surrounding how analysts develop questions and arrive at judgments, based partly on an absence of empirical research (Cooper 2005; Fingar 2011a, p. 131; Herbert 2013; Vandepeer 2016, p. 29). This lack of understanding has implications for how to train analysts, specifically regarding the development of training programs and mentoring arrangements for assisting analysts (Heuer 1999; Lowenthal & Marks 2015; Phythian 2009). Some scholars of intelligence analysis have argued

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that the profession should move away from models of teaching and learning which emphasise the ‘craft’ by way of ‘apprenticeship’ under analytic ‘masters’ (Cooper 2005, p. 17; Marrin 2008, p. 139) to a more systematic and formal model. Polanyi challenges the argument that the teaching and learning of a profession can develop without the sufficient engagement of the skilled master in the field. Polanyi emphasised that the learning of a tradecraft, or the skilful exercising of a profession, requires training and education under (ideally) gifted masters of the field. Polanyi’s understanding of ‘masters’ in the field is not dissimilar to how the intelligence profession understands someone as being an ‘expert’. The ‘expert’ can be distinguished from ‘novices’ as understood within the fields from the military, to law enforcement and national security (Taylor et al. 2013, p. 479). For the purposes of this thesis the broad understanding of ‘expertise’ and ‘master’ will be understood as a ‘skillful execution of knowledge and skill to achieve effective action’ (Fiore, Hoffman & Salas 2008, p. 2). In this sense, the ‘master’ Polanyi refers to is an individual who has developed expertise and embodies the aptitudes, knowledge, and acumen for solving problems competently within a tradition (Johnson 2003).

The law enforcement literature which explores the investigative practice and skills of detectives reveals competing positions regarding the nature of such work (Brunger, Tong & Martin 2015; Reiner 2010). Of particular interest in terms of the knowledge and skills involved in the practice of criminal investigations is how such knowledge and skills are developed. Especially since intelligence analysis involves the skill of perception, of knowing what to look for and generating a clear understanding of the information and data, Polanyi’s position regarding the development of the skill of perception has some bearing for the intelligence analysis field. According to Polanyi, the capacity to correctly perceive objects is a skill acquired via a process of learning, practice, and experience (Polanyi 1969, p. 106). The refinement of the practitioner’s skill-set within a tradition involves the development of their personal knowledge. In this regard, Polanyi’s concept of personal knowledge

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contributes towards a deeper understanding of how the analyst learns to perceive what it means to ‘know’ truth claims, by exercising their personal knowledge. Since the practitioner, according to Polanyi, skilfully integrates the collected information and data and generates their own personal understanding of it, such an understanding constitutes the creation of new knowledge. Personal knowledge in this regard represents the skilful creation of new knowledge, being ‘personal’.

The transmission of a skilled craft, involving unspecifiable tacit elements, cannot be entirely taught by propositional knowledge, according to Polanyi. The apprentice must submit to the master practitioner because they trust their ways of solving problems, ‘…even when you cannot analyse and account in detail for its effectiveness’ (Polanyi 1962a, p. 53). This is because part of the development of the practitioner’s personal knowledge, in particular what it means to understand truth-claims, can only be achieved with respect to the transferring of ‘tacit’ knowledge, by way of example by the master. Whilst the intelligence field has observed the value in learning from (ideally gifted) experts in the field (Alschuler 2007, p. 3; Campbell 2011; Devine & Loeb 2014; Jensen 2011; Shaw 2007, p. 42), Polanyi provides a more complete epistemological standpoint for robustly understanding this requirement within the development of the practitioner’s personal knowledge.

Polanyi placed greater emhasis on understanding the practice of science as a ‘craft’, involving the individual exercising their ‘practical skills’ within a ‘skilful performance’, as examined in Chapter 3. With respect to understanding the discipline of intelligence analysis, especially with regards to what it means to ‘know’ something, Polanyi posited the requirement to sufficiently acknowledge the knower within the knowledge generated. This nuanced understanding of knowledge claims, involving the personal knowledge of the practitioner, epistemologically contributes to arguments raised within understanding intelligence analysis as a discipline. For example, Mathams, writing of intelligence analysis in Australia, observed that analysis is ‘… no simple or routine activity but a highly skilled and subtle task’ (Mathams 1982, p. 8). With respect to the

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learning of this profession, Robert Sinclair in his article Thinking and Writing observes that within the Directorate of Intelligence within the CIA there is the acknowledgement that analysis is a ‘craft’, most of which is learnt on the job (Sinclair 2010, p. 5). Polanyi adds to this, by clarifying that learning within a craft requires the individual to practice the tasks themselves, along with learning the ‘art’ which cannot be specified in propositional knowledge, since no prescription for such art exists; hence, the need to learn such knowledge from observing and emulating as an apprentice under gifted masters in the field, if possible (Polanyi 1962a, p. 55). Polanyi emphasises that the development of the skills required to perform a task adeptly within a tradition comes from the refinement of their personal knowledge, which is key to understanding knowledge claims and expertise within that tradition. Sinclair, in his writing of expertise, implicitly hints towards the personal dimension of knowledge which is partly tacitly known to the practitioner (Sinclair 2010, p. 5):

A centipede was happy quite Until a frog in fun said, “Pray, which leg comes after which?” This raised its mind to such a pitch It lay distracted in a ditch Considering how to run. Polanyi provides a more epistemologically detailed account for why an individual, even an accomplished expert, may be incapable of describing in words or graphically represent how they know certain things. As Polanyi clarifies,

The words I have spoken and am yet to speak mean nothing: it is only I who mean something by them. And, as a rule, I do not focally know what I mean, and though I could explore my meaning up to a point, I believe that my words (descriptive words) must mean more than I shall ever know, if they are to mean anything at all (Polanyi 1962a, p. 252). Polanyi argues that our ability to articulate ideas linguistically is partly a function of our inarticulate intelligence (Colapietro 2011, p. 53; Fennell 2016; Jha 2011; Meek 2011; Mullins 2013). With respect to learning within a discipline, Polanyi emphasised the need for the individual to learn ‘on the job’ by practice (Polanyi 1957, p. 95). Polanyi gives the

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example of learning to solve mathematical problems, a skill developed principally by practice (Polanyi 1957, p. 95). Polanyi’s nuanced arguments around the tacit aspect of learning contribute to the broad recognition within science and the intelligence field that expertise is partly developed by practice (Alschuler 2007; Carson 2009; Evans & Kebbell 2012; Giurgiu, Barsan & Mosteanu 2015; Kreuzer 2016; Ouagrham-Gormley 2014; Taylor et al. 2013). Polanyi’s inversion of the traditional hierarchy of what it means to ‘know’ something – with an emphasis on personal and tacit knowledge, rather than impersonal and propositional knowledge – provides an alternative epistemological perspective regarding how to perceive the development of expertise within a discipline (Fennell 2016; Fothe 2012; Mullins 2013). This contributes towards a deeper understanding of the tacit aspect of learning the tradecraft of intelligence analysis as a discipline.

Polanyi’s discussions of the development of expertise within a profession presents some epistemological insights into similar aims with intelligence analysis, regarding the practitioner expertly making discoveries and verifying knowledge claims. According to Polanyi, expertise in a field requires learning the ‘rules of the art’, which the individual practitioner assimilates by integrating such rules within their ‘practical knowledge’ (Polanyi 1962a, p. 50). Throughout the process of learning, Polanyi stressed that the development of expertise comes from the art of practicing heuristic reasoning within the field (Polanyi 1957, p. 96). The value for developing expertise in this manner was outlined by the son of Michael Polanyi, John Polanyi (who received the Nobel Prize in Chemistry in 1986). John Polanyi observed the central importance of developing a refined sense of curiosity as the hallmark of an expert scientist.10 As John Polanyi observes, ‘So, yes, we do turn over stones, but the art is to pick the right stone’ (Polanyi 2002). Sherman Kent, a seminal figure within the intelligence profession, made a similar observation regarding the challenges facing the historian for choosing the most suitable topic to study. In Kent’s words, ‘Everywhere he looks, he

10 A video presentation by John Polanyi uploaded on May 2010. An excerpt from this video on his website, which can be accessed online at: http://sites.utoronto.ca/jpolanyi/videos/.

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sees possible ground, and his very freedom to look everywhere is one reason why he sees no point in digging at any given spot’ (Kent 1967, p. 13). Kent adds that ultimately the decision rests with the inquirer’s ‘natural preferences’. Michael Polanyi was convinced that the way the inquirer decided on which stone to pick up, or which line of research could be most valuable, was a key aspect of the nature of science. Polanyi expanded on this aspect, by emphasising that these decisions involved skill, particularly of a ‘tacit’ nature, which required development and practice under rigorous training within a tradition. It is this training and practice which refines the practitioner’s skill, which forms aspects of their personal knowledge. As Polanyi observes,

The capacity for making discoveries is not a kind of gambler’s luck. It depends on natural ability, fostered by training and guided by intellectual effort. It is akin to artistic achievement, and like it, it is unspecifiable but far from accidental or arbitrary (Polanyi 1962a, p. 111). According to Polanyi, the development of expertise within a tradition can be assisted with a master-apprentice relationship. Whilst this relationship has been acknowledged in the learning of expertise in the intelligence field (Devine & Loeb 2014; George 2010; Johnson 2010; Pherson 2009) – especially with respect to the recognised skill of choosing suitable lines of inquiry as a means for navigating through volumes of data (Bruce 2008; Chi, Glaser & Farr 2014; George 2010; Mudd 2015; Wastell, Clark & Duncan 2006) – Polanyi provides a more complete epistemological explanation as to why this relationship is important for the learning of a tradecraft. According to Polanyi, since what it means to ‘know’ something involves ‘tacit knowledge’ (since all knowledge claims have ‘tacit’ epistemological roots), it follows that expertise in some areas partly depends on sufficiently observing masters in the field and rigorous practice (Polanyi 1969, p. 195). The primacy of an apprenticeship for developing expertise is because the master themselves may not be completely aware or conscious of how they know certain things, since they understand knowledge claims, at least partly, in a ‘tacit’ fashion.

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Polanyi’s contribution to this acknowledgement is that the reason this master-apprentice learning process is indispensable is due to the development and transmission of tacit knowing. As the practitioner within a local tradition is not always aware of how they perform certain tasks, since a propositional prescription may not exist for it (Polanyi 1966b, p. 4), Polanyi stressed that the refinement of expertise in a field requires a master-apprentice relationship, which is reflective of the optimum measures for ‘tacitly’ understanding knowledge claims or what it means to ‘know’ something. Polanyi epistemologically articulates and builds upon findings, particularly within the Knowledge Management field, that indicate how some activities of practitioners in diverse fields involve perceptual abilities and skills of a ‘tacit’ nature, revealing that what is ‘known’ cannot be reduced (at least not completely) to formulated propositional rules or routines for learning (Giurgiu, Barsan & Mosteanu 2015; Martin, Philp & Hall 2009; Massingham 2004; Petranker 2005; Styhre 2008, p. 13; Von Krogh, Ichijo & Nonaka 2000). Models of learning within intelligence analysis designed to develop expertise that promotes propositional knowledge (Agrell 2002; Chang & Tetlock 2016; Dahl 2012; Johnson 2007; Marrin 2008; Marrin & Clemente 2006, pp. 642-665) without sufficient acknowledgement of tacit knowing may lead to an incomplete or impoverished understanding of what it means to ‘know’ something, according to Polanyi (1964, p. 85).

Within some quarters of the intelligence field, there are those who elevate propositional knowledge whilst downplaying the value of tacit knowing both with regards to the learning and practice of intelligence analysis. This position can be observed in the following study into the ‘analytic culture’ in the US intelligence community.

As long as intelligence analysis continues to be tradecraft, it will remain a mystery…the lessons learned in tradecraft, unlike those of other disciplines, often occur without being captured, tested, or validated… data collected … indicated… general methods that could be formalized indicate that this process would then lead to the development of intelligence analysis as a scientific discipline (Johnson 2005, p. 20). Polanyi challenges this idea that the master-apprentice process of learning expertise within a tradition can be substituted by formalised

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education and propositional knowledge. Polanyi refers to the medical profession as an example of the importance of the master-apprentice model of learning expertise within the educational process of the medical student (Polanyi 1962a, pp. 56-73). Whilst some intelligence scholars have observed that the field of intelligence analysis can be characterised as being a ‘craft’ (Gentry 2016; Marrin 2005, p. 1; Marrin & Clemente 2005; Modern Intelligence Analysis 2011) ‘operating within an apprenticeship system’ (Cooper 2005, p. 6), Polanyi’s views stand in contrast to those who argue for the need to move away from master- apprentice relations for learning expertise within intelligence analysis. Agrell is another who argued for the need of the intelligence field to evolve from a tradecraft to a profession. As Agrell observes, ‘If a modern profession is characterized by the transformation from improvisation and master-apprentice relations to formalized education and training programs, then intelligence analysis has come a long way’ (Agrell 2002, p. 3). Agrell further observes,

The work of a profession is not the successful miracles of the gifted amateurs or the skilled craftsmen but a systematic employment of knowledge, where methods are visible and verifiable, their employment can be tested, and the results can be predicted (Agrell 2002, p. 3). Polanyi disagrees with this position, observing that

it is of the essence of a tradition to remain unformulated, and traditions can therefore be transmitted only by the personal apprenticeship of succeeding generations to the example of their elders (Polanyi 1962a, p. 32). These discussions regarding the pedagogical foundations of the discipline of intelligence analysis have implications regarding the set of skills the ‘expert’ analyst should possess. For example, Richard Betts noted that there are two kinds of analysts, those of the linear-thinking kind, and those who generate ‘exceptional thinking’ – who are more capable of anticipating the unusual (Betts 2007, pp. 64-65). Betts observed that even though it may be useful to encourage ‘out of the box’ analysis, the problem remains as to when to listen to these thinkers and when to stick with the conventional analysts (Betts 2007, p. 134). Polanyi contributes to this observation with his arguments regarding similar challenges present within

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science, specifically in relation to the decisions facing the scientist as to whether they have struck upon an original theory or line of inquiry which may bear fruit in the future, or whether they are piecing together parts of a puzzle which are not related (Polanyi 1957). Polanyi further adds to this dilemma, arguing that the way through such problems is for the tradition from which the practitioner operates to be encouraged to respect the truth (or at least an attempt to discover it), and intellectual values and virtues that can create new knowledge and understanding of the world (Polanyi 1962a, p. 216). Such a view has some bearing on the intelligence field with respect to pedagogical considerations of the tradecraft.

The Popperian way of understanding knowledge claims within science, of moving ‘from observations and evidence to explanation’ (Waltz 2014, p. 2), is a view which finds some support within the intelligence literature (Agrell & Treverton 2015; Hicks 2013; Phythian 2017; Prunckun 2014, p. 68; Tang 2017). For example, the ideal of falsificationism as the principle underlying science is championed by other intelligence scholars (Marrin 2017; Pherson & Beebe 2015; Phythian 2017; Heuer, Pherson & Beebe 2009; Smith 2017). Polanyi emphasised that the personal commitment of the practitioner is key to understanding science, which more completely acknowledges the personal dimension of knowledge more generally. The world ‘out there’ becomes intelligible, not by following some ‘scientific method’, or falsificationism as such, but by virtue of the relationship between observations and linking that to the practitioner’s inner personal knowledge (Polanyi 1962a; Scott 1985, pp. 64-8). This relationship is key to understanding science and provides a framework for how the analyst could view themselves with respect to solving problems and understanding their knowledge as a product.

As outlined in the 9/11 Commission and government reports in the alleged WMD program in Iraq under Saddam Hussein, there has been a clear requirement for improved objectivity within analytical assessments (Butler 2004; Parliamentary Joint Committee on Law Enforcement 2013; Flood 2004). With respect to this aim within the intelligence field, Polanyi’s concept of personal knowledge presents a way for understanding

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knowledge claims and the development of expertise that re-casts the classic distinction between knowledge, which is either objective or subjective. According to Polanyi’s concept of personal knowledge, a more accurate representation of knowledge claims is that they involve, to varying degrees, personal affirmations. Without sufficiently acknowledging the ‘knower’ within knowledge claims and the refinement of expertise within a tradition amounts to an impoverished or limited epistemological understanding of what it means to ‘know’ something, according to Polanyi’s outlook.

Conclusion Intelligence analysis can be characterised as an epistemological enterprise, which aims to develop a clear understanding of knowledge products. Polanyi’s perspective of what it means to ‘know’ something serves to recognise some fundamental epistemological considerations regarding knowledge as a product. Specifically, Polanyi’s concept of personal knowledge contributes towards a deeper epistemological understanding of recognising knowledge as a product within the discipline of intelligence analysis. Polanyi emphasised that there should be sufficient acknowledgement within knowledge claims of the ‘personal coefficient’ or the knower within the knowledge as a product. Polanyi’s treatment of the concept of personal knowledge provides an expanded range of ways for exploring what it means to ‘know’ something regarding intelligence analysis products. Polanyi’s perspective inverts the traditional epistemological hierarchy, which typically values propositional knowledge over more ‘tacit’ or ‘personal’ ways of understanding knowledge claims. This epistemological position contributes to the intelligence discipline by providing a firmer outline of the personal dimension of knowledge as a product. Closely related to this acknowledgement of the personal dimension of knowledge is the manner by which the practitioner develops their own personal knowledge and expertise. The refinement of the practitioner’s expertise, according to Polanyi, may often require a master- apprentice relationship. This way of understanding the development of expertise, which emphasises the ‘tacit’ transference and refinement of the

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individual’s personal knowledge from the master to the apprentice via emulation, offers a novel intellectual landscape for considering pedegogical aspects of the discipline of intelligence analysis. Polanyi’s concept of personal knowledge challenged the view that knowledge that defies complete clear articulation is necessarily the product of ‘…shabby thinking and imperfect knowledge’ (Kent 1967, p. 47). Whilst one’s personal knowledge and expertise may be elusive and defy complete articulation in propositional form, it is still a valid form of understanding something, according to Polanyi (Blum 2010; Fennell 2016; Polanyi 1962a; Wright 2016). Taken together, Polanyi’s discussions regarding what it means to ‘know’ something offers the intelligence analysis discipline a nuanced and detailed epistemological understanding of the personal dimension of expertise and knowledge as a product.

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Conclusion Chapter 1 detailed the different views around the ‘knowledge-building’ activities of intelligence analysis. These views draw attention to epistemological considerations regarding what is known, the reliability of knowledge claims, and what these judgments signify in relation to more clearly understanding the security environment. Intelligence literature, from the national security field to law enforcement, reveals discernible efforts towards aligning intelligence analysis with scientific principles and practices. Discussions regarding the intelligence cycle, along with broader debates in relation to whether intelligence analysis is an ‘art’ or a ‘science’, or some combination of both, is indicative of the sustained interest within the study and practice of intelligence analysis regarding how to broadly understand the nature of intelligence analysis as a discipline. As outlined in Chapter 2, Polanyi’s insights into the practice of science, drawn from his own experience in the profession, provides his own authentic account of the nature of science. Polanyi’s interrelated and dynamic concepts of tacit knowing and personal knowledge provide a novel way for exploring the discipline of science and importantly offers a way for the intelligence field to surmount the tensions between perceiving the tradecraft of analysis as either a ‘science’ or an ‘art’. Polanyi’s perspective of science offers a series of epistemological arguments for overcoming such notional divides between the ‘art’ or ‘science’ of intelligence analysis. Polanyi contributes towards a better understanding of the discipline of intelligence analysis by offering a more in-depth and nuanced understanding of both the ‘process’ of skilfully solving problems and the ‘product’, or what it means to epistemologically ‘know’ knowledge claims.

Whilst broadly speaking, it can be observed that there are two forms of knowledge, being ‘explicit’ and ‘tacit’, Polanyi argued that there is no strict distinction between explicit and tacit knowledge. This is because, according to Polanyi, the tacit structure of knowing characterises all types of knowing including both propositional and non-propositional (Polanyi 1962a, p. 56). For example, perception, observation, acquisition of skills, use of tools, craftsmanship, theorising and verification of theories all

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contain some ‘tacit’ structure (Polanyi 1969, p. 128). Polanyi posited that both explicit and tacit knowledge are valid types of knowledge, since they can be relied upon to solve problems and guide research. This has a bearing on the field of intelligence analysis by sufficiently acknowledging and elevating the role of tacit knowing within the process of the analyst solving problems, along with the centrality of the analyst as a ‘knower’ in understanding knowledge claims.

As examined in Chapter 3, Polanyi’s concept of tacit knowing offers a nuanced way for more completely understanding epistemologically the process of solving problems, which illuminates similar epistemological considerations within the process of intelligence analysis. Sufficient acknowledgment of the primacy of tacit knowing is fundamental to more accurately appreciate the process of solving problems within intelligence analysis. Since tacit knowing as a type of knowledge is elusive, defying complete articulation linguistically or graphically, Polanyi claimed we can only understand it in relation to how it operates within a performance. In this sense, Polanyi offers a robust epistemological account of the process of solving problems, which serves to articulate the generally acknowledged aspect of ‘know how’ for solving problems within the intelligence field. Polanyi’s discussions regarding tacit knowing with respect to a ‘skilful performance’ emphasises the ‘practical skills’ involved in this process. This is because, according to Polanyi, the ‘skilful performance’ partly depends on the ‘tacit powers’ of the practitioner, a form of knowing which escapes complete articulation, but does not undermine the capacity of the practitioner to perform the task. Polanyi’s technical and detailed examination of the scientist skilfully using tools to assist with solving problems develops a rich understanding of how to perceive epistemological considerations regarding the use of SATs within the process of intelligence analysis. Importantly, Polanyi’s framework for understanding the process of a skilful practice, involving the practitioner’s tacit knowing, highlights the centrality of these epistemological issues facing the intelligence analyst and expands the discourse in this area.

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The intelligence failures of the mistaken assessment of Saddam Hussein’s alleged stockpiling of WMDs or the attacks of 9/11 have driven some responses within some Intelligence Communities (noticeably in the US) towards institutionalising standards of analysis, including the use of SATs, in order to improve the accountability, transparency and reliability of intelligence assessments. Such institutional aims are motivated out of avoiding intelligence failures in the future, by requiring the process of intelligence analysis to become more ‘rigorous’ and transparent. The potential problem with applying ‘rigour’ to the analysis of a problem is whether it leads to over-compartmentalisation, which could then follow into the negative aspects of ‘destructive analysis’. Whilst there could be benefits to the use of SATs for managing some issues within the process of analysis, particularly from a cognitive point of view, Polanyi’s concept of ‘destructive analysis’ details some of the epistemological perils regarding analytical tools or methods which may promote or lead to over- compartmentalisation within the process. By the standards of intelligence analysis, a key goal is to ‘connect the dots’, or skilfully integrate the information and data together to develop a coherent picture of the security environment. With respect to Polanyi’s ideas around a ‘skilful performance’ and tacit knowledge, it is questionable whether SATs may assist in the process of solving problems for intelligence analysts. Polanyi’s arguments regarding the potential perils of ‘destructive analysis’ provide a detailed account of the dangers of adding undue ‘rigour’ to the process of analysis, which may lead to pointing out the ‘trees’ without sufficiently (and tacitly) integrating them to perceive the ‘forest’ overall in context. These arguments around ‘destructive analysis’ further offers a more general way of understanding the personal features of the intelligence practitioner performing their analysis.

Polanyi’s concept of ‘indwelling’ provides a detailed epistemic account of what is involved in the ‘know how’ within the process of solving problems. As discussed in Chapter 3 in relation to the concept of tacit knowing, one of the fundamental aspects of this concept is not just that we know things but cannot completely articulate them, but the relationship

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between tacit and explicit knowledge. According to Polanyi, the scientist ‘dwells’ in their problem (1962, p. 11) in the process of solving problems. Within this process, the practitioner switches their attention between ‘focal’ and ‘subsidiary’ awareness – just as when reading, the reader becomes ‘focally’ aware of the words on the page, but this process involves transitions to interpreting the meaning of those words that partially involves ‘subsidiary’ awareness. In this regard, according to Polanyi, the process of knowing involves an epistemological orientation, which is characteristically a ‘from-to’ relationship. In the process of reading text, we move ‘from’ the words ‘to’ their intended meaning. According to Polanyi, we cannot be simultaneously aware both ‘focally’ and ‘subsidiarily’, since these ways of knowing are mutually exclusive (Polanyi 1962a, p. 56). A ‘skilful performance’ involves a ‘from-to’ epistemological orientation, integrating our ‘focal’ with our ‘subsidiary’ awareness, a process that is itself ‘unspecifiable’ since it is largely ‘tacit’ (Polanyi 1969, p. 126). The concept of indwelling outlines how the scientist, in the process of solving problems, moves from their focal attention to their subsidiary awareness. This is how tacit knowing, specifically the practitioner’s ‘subsidiary’ awareness, relates to their explicit knowledge, or more precisely their ‘focal’ attention. The concept of indwelling captures the way tacit knowing relates to explicit knowledge, according to Polanyi, by the scientist transitioning their attention from focal to subsidiary awareness. Polanyi’s treatment of this process (of the scientist switching between their ‘focal’ and ‘subsidiary’ awareness) provides a more epistemologically enriched understanding of the process of knowing whilst solving problems. With respect to the acknowledgement in intelligence analysis that the process involves ‘know how’, Polanyi contributes by articulating more precisely how we know. This insight of Polanyi to the field of intelligence analysis highlights, in a detailed and nuanced way, how to better understand the process of knowing as involved in the practice of analysis.

Polanyi’s views of science contribute towards discussions regarding how to understand the nature of intelligence analysis, specifically in relation to the process of solving problems, involving tacit knowing within

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the skilful performance. Transitioning from these discussions regarding how we know as a process within solving problems, Chapter 4 examined the way Polanyi’s concept of personal knowledge provides an enriched view for the intelligence analysis profession to understand what it means to ‘know’ something as a knowledge product. Polanyi’s re-casting of what it means to ‘know’ something can be understood with a reference to another lesson of the cholera outbreak in London in the nineteenth century.

John Snow was a central figure with respect to developing an understanding of cholera and how to reduce the infections from spreading. Like Polanyi, John Snow was a keen observer, possessing a prodigious intellectual talent that favoured a ‘consilience’ approach to understanding some issues – or the capacity to develop an understanding of one field, from taking insights into other seemingly unrelated fields. John Snow came to recognise that a new perspective was needed to more completely understand and address cholera. Specifically, the new way of seeing would require moving beyond the medical understanding of the symptoms of the individual sufferer, towards a bird’s eye view of how humans behaved within segments of London (Johnson 2006b, p. 98). This switching of attention, from the individual to the group, revealed to John Snow how cholera was spreading to new human hosts through the contamination of certain water supplies. In a similar manner to John Snow’s re-orientation for understanding the nature of the problem of cholera, Polanyi’s concept of personal knowledge presents a re-orientation towards understanding the nature of what it means to ‘know’ something. This has an important bearing on the intelligence analysis discipline. Polanyi’s standpoint posits that in order to fully appreciate propositional knowledge claims (externalised claims) it is necessary to sufficiently acknowledge the ‘tacit’ (internal) and ‘personal’ affirmation of such knowledge products. For there to be ‘knowledge’ there must be a ‘knower’. This personal dimension within how something is known, as Polanyi discussed, should be sufficiently acknowledged within knowledge claims made by intelligence analysts.

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With respect to the fact that the pursuit of knowledge is the key concern of the intelligence analyst (Tang 2017, p. 664; Vandepeer 2014, p. 27), Polanyi’s insights into the practice of science and the nature of knowledge offers a framework for advancing the epistemoligical discourse within intelligence analysis. Necessarily this involves theoretical considerations regarding the process of knowing, as explored in Chapter 3, and the product of knowledge as examined Chapter 4. As outlined in Chapter 4, Polanyi’s concept of personal knowledge provides detailed technical account for discussing what it means to ‘know’ something as a knowledge product within intelligence analysis. Since Polanyi argued that there are no set ‘rules’ or ‘scientific methods’ within science, the practitioner must draw on their personal knowledge for deciding what evidence or clues to accept and those to discard. With respect to the development of the scientist’s skill, Polanyi emphasised that the ‘rules of the art’, or the maxims within the field, do not determine the correct practice of a task. Such ‘rules of thumb’ must be learnt and integrated within the individual’s personal knowledge. Polanyi argued that towards this end, of integrating the ‘rules of the art’ of science within the individual’s personal knowledge, requires both the individual to practice the tasks and learn by submitting to the master in the field. This apprentice-master relationship allows for the transmission and learning of tacit skilful practices, the ‘rules’ for which the master themselves are sometimes not completely aware of. This is because, according to Polanyi, the skilful performance of a task draws on the tacit knowing of the practitioner, a form of knowledge that defies complete articulation into propositional knowledge. In this broad sense, Polanyi’s discussions regarding how the scientist develops their personal knowledge, including the importance of the master apprentice relationship in this process of learning the craft, sheds light on the importance of this relationship, which is applicable to the field of intelligence analysis.

Polanyi’s model of learning and teaching can be characterised as being bottom-up, by attributing the development of skills primarily to individuals adequately practicing tasks, along with submitting and learning

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from masters within the tradition. This model of learning emphasises the necessity to acknowledge the development of the practitioner’s personal knowledge within a tradition, which importantly includes their ‘tacit’ knowledge. This contribution to the field of intelligence analysis is by sufficiently acknowledging in a more detailed epistemological sense the centrality of the development of tacit knowing within this discipline.

Polanyi’s unpacking of the fundamental issues around the process and the product of skilfully solving problems advances the discourse within the intelligence analysis discipline on such epistemological matters. Specifically, Polanyi offers a way for more completely representing and explaining the process of skilfully solving problems, involving the practitioner’s tacit knowing. Polanyi situated the individual ‘knower’ as being central to knowledge claims, which adds to the discourse on what it means to ‘know’ something as an intelligence product. He also emphasised that the ‘personal coefficient’ in relation to knowledge claims is no mere imperfection of knowledge, but a ‘vital component’ of what it means to ‘know’ something (Polanyi 1962a). It is not the intention within this thesis to claim Polanyi offers the definitive answer as to how to understand science and related epistemological topics. Polanyi’s arguments are to be understood as a perspective, to provoke discussion, not an offering of the ‘truth’ on these matters. Polanyi’s arguments around the personal and tacit aspects of knowledge should also not be understood as the basis to only trust one’s own judgment and not sufficiently recognise the knowledge of others within a field. Rather, to appreciate that the intelligence analyst’s personal knowledge is an important feature of knowledge-claims and knowledge more generally. Polanyi’s perspective on science contributes a more nuanced and technical way to explain and comprehend a series of key epistemological issues in relation to the discipline of intelligence analysis. Polanyi’s arguments regarding the personal and tacit features involved in the practice of science, which are furthermore key considerations in relation to knowledge more generally beyond science, answers the thesis question. Polanyi offers a perspective for more completely understanding and

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discussing some of the most enduring epistemological issues within the discourse of intelligence analysis, principally by elevating the centrality of the practitioner’s tacit and personal knowledge.

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Reference list

Agler, DW 2011, 'Polanyi and Peirce on the Critical Method', Tradition and Discovery: The Polanyi Society Periodical, vol. 38, no. 3, pp. 13-30.

Agrell, W 2002, ‘When everything is intelligence, nothing is intelligence’, The Sherman Kent Center for Intelligence Analysis Occasional Papers, vol. 1, no. 4, retrieved 9 January 2018, https://www.cia.gov/library/kent-center-occasional- papers/vol1no4.htm

Agrell, W 2012, 'The Next 100 Years? Reflections on the Future of Intelligence', Intelligence and National Security, vol. 27, no. 1, pp. 118-32.

Agrell, W & Treverton, GF 2015, National Intelligence and Science: Beyond the Great Divide in Analysis and Policy, Oxford University Press, USA.

Ahmadi, M, Dileepan, P & Wheatley, KK 2016, 'A SWOT analysis of big data', Journal of Education for Business, vol. 91, no. 5, pp. 289-94.

Aligica, PD & Tarko, V 2012, 'Polycentricity: From Polanyi to Ostrom, and Beyond', Governance, vol. 25, no. 2, pp. 237-62.

Allen, C 2000, Comments of the Associate Director of Central Intelligence” for Collection at a Public Seminar on Intelligence at Harvard University, Spring 2000, .

Allison, G 1969, 'Conceptual models and the Cuban Missile Crisis', American Political Science Review, vol. 63, no. 3, pp. 689-718.

Alschuler, AW 2007, 'The Upside and Downside of Police Hunches and Expertise', Journal of Law, Economics & Policy, vol. 4, no. 115, pp. 118-119.

Apczynski, JV 2014, 'The Projects of Michael Polanyi and Charles Taylor', Tradition and Discovery: The Polanyi Society Periodical, vol. 41, no. 1, pp. 21-32.

Artner, S, Girven, RS & Bruce, J 2016, 'Assessing the Value of Structured Analytic Techniques in the US Intelligence Community', RAND Corporation, retrieved 9 January 2018, https://www.rand.org/pubs/research_reports/RR1408.html

Audi, R 2010, Epistemology: A contemporary introduction to the theory of knowledge, Routledge, London.

Baartz, D 2005, 'Information management processes and the intelligence cycle', Journal of the Australian Institute of Professional Intelligence Officers, vol. 14, no. 1, p. 45.

Bammer, G 2010, Dealing with Uncertainties in Policing Serious Crime, Australian National University ePress, Canberra.

Bang, M 2017, 'A Shared Epistemological View Within Military Intelligence Institutions', International Journal of Intelligence and CounterIntelligence, vol. 30, no. 1, pp. 102-16.

133

Bar-Joseph, U 2010, 'The Professional Ethics of Intelligence Analysis', International Journal of Intelligence and CounterIntelligence, vol. 24, no. 1, pp. 22-43.

Barbour, R 2013, Introducing qualitative research: a student's guide, Sage, Thousand Oaks, CA.

Barnes, A 2016, 'Making intelligence analysis more intelligent: Using numeric probabilities', Intelligence and National Security, vol. 31, no. 3, pp. 327-44.

Beckett, D 2000, 'Making Workplace Learning Explicit: an epistemology of practice for the whole person', Westminster Studies in Education, vol. 23, no. 1, pp. 41-53.

Betts, R 2007, Enemies of Intelligence: Knowledge and Power in American National Security, Columbia University Press, New York.

Betts, RK 1987, 'Surprise, Scholasticism, and Strategy: A Review of Ariel Levite’s Intelligence and Strategic Surprises ', International Studies Quarterly, vol. 33, no. 3, pp. 329-343.

Betts, RK 2009, 'Analysis, war, and decision: Why Intelligence Failures are Inevitable', in P Gill, S Marrin & M Phythian (eds), Intelligence Theory: Key questions and debates, Routledge, London, pp. 87-111.

Beveridge, WIB 2017, The art of scientific investigation, Edizioni Savine, Ancarano, Italy.

Bhaskar, R & Zhang, Y 2007, 'Knowledge Sharing in Law Enforcement: A Case Study', Journal of Information Privacy and Security, vol. 3, no. 3, pp. 45-68.

Black, DR 2016, A decade of human security: Global governance and new multilateralisms, Routledge, London.

Blum, PR 2010, 'Michael Polanyi: the anthropology of intellectual history', Studies in East European Thought, vol. 62, no. 2, pp. 197-216.

Borg, LC 2017, 'Improving Intelligence Analysis: Harnessing Intuition and Reducing Biases by Means of Structured Methodology', The International Journal of Intelligence, Security, and Public Affairs, vol. 19, no. 1, pp. 2-22.

Bradford, B, Loader, I, Jauregui, B & Steinberg, J 2016, The SAGE Handbook of Global Policing, Sage, Thousand Oaks, CA.

Bradley, T & Sedgwick, C 2009, 'Policing beyond the police: a ‘first cut’ study of private security in New Zealand', Policing and Society, vol. 19, no. 4, pp. 468-92.

Bramly, S 1994, Leonardo: the Artist and the Man, trans. S Reynolds, Penguin, London.

Breckenridge, JG 2010, 'Designing Effective Teaching and Learning Environments for a New Generation of Analysts', International Journal of Intelligence and CounterIntelligence, vol. 23, no. 2, pp. 307-23.

134

Brock, R 2015, 'Intuition and insight: two concepts that illuminate the tacit in science education', Studies in Science Education, vol. 51, no. 2, pp. 127-67.

Brodeur, J-P & Dupont, B 2006, 'Knowledge Workers or “Knowledge” Workers? Jean-Paul Brodeur and Benoit Dupont are both researchers at the International Centre for Comparative Criminology', Policing and Society, vol. 16, no. 1, pp. 7- 26.

Broll, R 2016, 'Collaborative responses to cyberbullying: preventing and responding to cyberbullying through nodes and clusters', Policing and Society, vol. 26, no. 7, pp. 735-52.

Brown, SD 2007, 'The meaning of criminal intelligence', International Journal of Police Science & Management, vol. 9, no. 4, pp. 336-40.

Bruce, JB 2008, 'Making Analysis More Reliable: Why Epistemology Matters to Intelligence Analysis', in RZ George & JB Bruce (eds), Analyzing Intelligence: Origins, obstacles, and innovations, Georgetown University Press, Washington DC, pp. 171-190.

Bruce, JB & George, RZ 2008, ‘Intelligence Analysis: The Emergence of a Discipline’, in RZ George & JB Bruce (eds), Analyzing Intelligence: Origins, obstacles, and innovations, Georgetown University Press, Washington DC, pp. 1- 18.

Brunger, M, Tong, S & Martin, D 2015, Introduction to policing research: taking lessons from practice, Routledge, London.

Bullock, K 2013, 'Community, intelligence-led policing and crime control', Policing and Society, vol. 23, no. 2, pp. 125-44.

Burley, T & Freier, MC 2004, 'Character structure: A Gestalt-cognitive theory', Psychotherapy: Theory, Research, Practice, Training, vol. 41, no. 3, pp. 321-31.

Butler, R 2004, Review of intelligence on weapons of mass destruction, The Stationery Office, London, retrieved 9 January 2018, http://news.bbc.co.uk/nol/shared/bsp/hi/pdfs/14_07_04_butler.pdf

Cain, A 2012, ‘Intelligence Analysis and Professional Journal Publishing’, paper presented at the BCISS Conference 2012, Brunel University, UK.

Cambrosio, A & Keating, P 1988, '“Going monoclonal”: Art, science, and magic in the day-to-day use of hybridoma technology', Social Problems, vol. 35, no. 3, pp. 244-60.

Campbell, S 2011, 'A Survey of the U.S. Market for Intelligence Education', International Journal of Intelligence and CounterIntelligence, vol. 24, no. 2, pp. 307-37.

Carson, D 2009, 'Detecting, developing and disseminating detectives' ‘creative’ skills', Policing and Society, vol. 19, no. 3, pp. 216-25.

Carson, D, Gilmore, A, Perry, C & Gronhaug, K 2001, Qualitative marketing research, Sage, London.

135

Carter, SM & Little, M 2007, 'Justifying knowledge, justifying method, taking action: Epistemologies, methodologies, and methods in qualitative research', Qualitative health research, vol. 17, no. 10, pp. 1316-28.

Chang, W, Berdini, E, Mandel, DR & Tetlock, PE 2017, 'Restructuring structured analytic techniques in intelligence', Intelligence and National Security, pp. 1-20.

Chang, W & Tetlock, PE 2016, 'Rethinking the training of intelligence analysts', Intelligence and National Security, vol. 31, no. 6, pp. 903-920.

Chase, WG & Simon, HA 1973, 'Perception in chess', Cognitive psychology, vol. 4, no. 1, pp. 55-81.

Chauvin, C & Fischhoff, B 2011, Intelligence analysis : behavioral and social scientific foundations, National Academies Press, Washington, DC.

Chi, MT, Glaser R & Farr MJ 2014, The nature of expertise, Psychology Press, New York.

CIA 2009, 'Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis', Center for the Study of Intelligence, retrieved 09 January 2018, https://www.cia.gov/library/publications/publications-rss-updates/tradecraft- primer-may-4-2009.html.

Clare, J 2012, ‘Better intelligence leads to record seizures’, media release, http://www.ministerhomeaffairs.gov.au/Mediareleases/Pages/2012/Second%20Q uarter/17- May-2012---Better-intelligence-lead-to-record-seizures.aspx.

Clarke, R 2013, Intelligence Analysis: A Target-Centric Approach, CQ Press, London.

Colapietro, V 2011, 'Intellectual Passions, Heuristic Virtues, and Shared Practices: Charles Peirce and Michael Polanyi on Experimental Inquiry', Tradition and Discovery: The Polanyi Society Periodical, vol. 38, no. 3, pp. 51-66.

Colby, W 1992, 'Retooling the Intelligence Industry', Foreign Service Journal, vol. 69, no. 1, pp. 21-25.

Collins, H 2001, 'Tacit knowledge, trust and the Q of sapphire', Social Studies of Science, vol. 31, no. 1, pp. 71-85.

Collins, H., 2010. Tacit and explicit knowledge. University of Chicago Press

Collins, H. and Evans, R., 2008. Rethinking expertise. University of Chicago Press

Collins, S & McCombie, S 2012, 'Stuxnet: the emergence of a new cyber weapon and its implications', Journal of Policing, Intelligence and Counter Terrorism, vol. 7, no. 1, pp. 80-91.

United States Department of State, ‘Commission on the Intelligence Capabilities of the United States Regarding Weapons of Mass Destruction 2005’, Report to the President of the United States, retrieved 09 January 2018, https://fas.org/irp/offdocs/wmd_report.pdf

136

The Concise Macquarie Dictionary, 1982, Doubleday, South Australia.

Converse, R & Pherson, R 2008, 'Intelligence and Medicine: Parallel Cognitive Traps', Pherson Associates LLC, retrieved 9 January 2018, http://www.pherson.org/wp-content/uploads/2013/11/03.-Intelligence-and- Medicine-Parallel-Cognitive-Traps_FINAL.pdf

Cook, MB & Smallman, HS 2008, 'Human Factors of the Confirmation Bias in Intelligence Analysis: Decision Support From Graphical Evidence Landscapes', Human Factors: The Journal of the Human Factors and Ergonomics Society, vol. 50, no. 5, pp. 745-54.

Cooper, JR 2005, Curing Analytic Pathologies: Pathways to Improved Intelligence Analysis, Center for the Study of Intelligence, Washington, DC, retrieved 9 January 2018, https://www.cia.gov/library/center-for-the-study-of- intelligence/csi-publications/books-and-monographs/curing-analytic-pathologies- pathways-to-improved-intelligence-analysis-1/analytic_pathologies_report.pdf

Cope, N 2004, 'Intelligence Led Policing or Policing Led Intelligence?', British Journal of Criminology, vol. 44, pp. 188-203.

Corkill, J 2011, 'Not art, not science, but artistry: Why Professional Artistry should matter to the intelligence community', Journal of the Australian Institute of Professional Intelligence Officers, vol. 19, no. 1, p. 3.

Cornall, R & Black, R 2011, 2011 Independent Review of the Intelligence Community report, Dept. of the Prime Minister and Cabinet, Canberra, retrieved 9 January 2018, https://www.pmc.gov.au/sites/default/files/publications/2011-iric- report.pdf

Coulthart, S 2016, 'Why do analysts use structured analytic techniques? An in- depth study of an American intelligence agency', Intelligence and National Security, vol. 31, no. 7, pp. 933-948.

Coulthart, S 2017a, 'What’s the problem? Frameworks and methods from policy analysis for analyzing complex problems', Intelligence and National Security, vol. 32, no. 5, pp. 636-48.

Coulthart, SJ 2017b, 'An Evidence-Based Evaluation of 12 Core Structured Analytic Techniques', International Journal of Intelligence and CounterIntelligence, vol. 30, no. 2, pp. 368-91.

Coyne, JW 2014, 'Strategic intelligence in law enforcement: anticipating transnational organised crime', PhD thesis, Queensland University of Technology, QuT ePrints.

Crawford, A 2005, Plural policing: The mixed economy of visible patrols in England and Wales, Policy Press, London.

Creswell, JW & Poth, CN 2017, Qualitative inquiry and research design: Choosing among five approaches, Sage Publications, London.

Criminal Intelligence: Manual for Analysts, 2011, UN Office on Drugs and Crime, New York.

137

Czerwinski, T 1998, Coping with the Bounds: Speculations in Nonlinearity in Military Affairs, National Defense University, Washington.

Dahl, EJ 2012, 'Pinball Wizards and Professors: Competing Models of Intelligence Analysis', paper presented to International Studies Association annual convention, San Diego, California.

Dahl, EJ 2014, 'Finding Bin Laden: Lessons for a new American way of intelligence', Political Science Quarterly, vol. 129, no. 2, pp. 179-210.

Dahl, EJ 2017, 'Getting beyond analysis by anecdote: improving intelligence analysis through the use of case studies', Intelligence and National Security, vol. 32, no. 5, pp. 563-78.

Davis, J 1995, 'A Policymakers Perspective on Intelligence Analysis', Studies in Intelligence, vol. 38, no. 5, pp. 7-15.

Davis, J 2002a, 'Leadership in Intelligence Analysis', retrieved 9 January 2018, http://www.oss.net/dynamaster/file_archive/100304/e2866ddc5841ad4aa50d1ce 9ac76415e/Jack%20Davis%20Leadership%20in%20Intelligence%20Analysis%2 0August%202002.pdf

Davis, J 2002b, ‘Sherman Kent and the profession of intelligence analysis’, The Sherman Kent Center for Intelligence Analysis Occasional Papers, vol. 1, no. 5, retrieved 9 January 2018, https://www.cia.gov/library/kent-center-occasional- papers/vol1no5.htm.

Davis, PK, Manheim, D, Perry, WL & Hollywood, J 2015, 'Using causal models in heterogeneous information fusion to detect terrorists', 2015 Winter Simulation Conference (WSC), pp. 2586-97. doi: 10.1109/WSC.2015.7408367

De Franco, C & Meyer, C 2011, Forecasting, Warning and Responding to Transnational Risks, Springer, Berlin. de Maillard, J & Zagrodzki, M 2017, 'Plural policing in Paris: variations and pitfalls of cooperation between national and municipal police forces', Policing and Society, vol. 27, no. 1, pp. 54-67.

Décary-Hétu, D & Dupont, B 2012, 'The social network of hackers', Global Crime, vol. 13, no. 3, pp. 160-75.

Dennis, MA 2013, 'Tacit Knowledge as a Factor in the Proliferation of WMD: The Example of Nuclear Weapons', Studies in Intelligence, vol. 57, no. 3.

Denzin, NK & Lincoln, YS 2011, The Sage handbook of qualitative research, Sage, Thousand Oaks, CA.

Devine, J & Loeb, V 2014, Good Hunting: An American Spymaster's Story, Macmillan, Basingstoke, UK.

Devlin, K 2017, Finding Fibonacci: The Quest to Rediscover the Forgotten Mathematical Genius Who Changed the World, Princeton University Press, Princeton, NJ.

138

Dexter, H, Phythian, M & Strachan-Morris, D 2017, 'The what, why, who, and how of teaching intelligence: the Leicester approach', Intelligence and National Security, vol. 32, no. 7, pp. 920-934.

Doing, P 2011, 'Review essay: Tacit knowledge: Discovery by or topic for science studies?: Michael Polanyi, The Tacit Dimension', Social Studies of Science, vol. 41, no. 2, pp. 301-6.

Dreisbach, C 2011, 'The Challenges Facing an IC Epistemologist-in-Residence', International Journal of Intelligence and CounterIntelligence, vol. 24, no. 4, pp. 757-92.

Dunn, W 2012, Public Policy Analysis, Pearson, London.

Dupont, B 2017, 'Bots, cops, and corporations: on the limits of enforcement and the promise of polycentric regulation as a way to control large-scale cybercrime', Crime, Law and Social Change, vol. 67, no. 1, pp. 97-116.

Duvenage, MA 2010, 'Intelligence analysis in the knowledge age: An analysis of the challenges facing the practice of intelligence analysis', Masters thesis, University of Stellenbosch, Stellenbosch, South Africa.

Eck, JE, Clarke, RV & Petrossian, G 2013, Intelligence Analysis for Problem Solvers, Center for Problem-Oriented Policing, Washington, DC.

Einstein, A 1934, 'On the method of theoretical physics', Philosophy of science, vol. 1, no. 2, pp. 163-9.

Einstein, A 1949, Albert Einstein: philosopher-scientist, Open Court, La Salle, IL.

Ekman, P 2009, Telling lies: Clues to deceit in the marketplace, politics, and marriage (revised edition), WW Norton & Company, New York.

Ellis-Smith, J 2016, 'Analysis and influence in combat intelligence', Journal of the Australian Institute of Professional Intelligence Officers, vol. 24, no. 1, p. 34.

Elstein, AS & Sprafka, SA 1978, Medical Problem Solving: An Analysis of Clinical Reasoning, Harvard University Press, Cambridge, MA.

Ericson, R & Haggerty, K 1997, Policing Risk and Society, Oxford University Press, Oxford.

Ericson, R & Shearing, C 1986, ‘The Scientification of Police Work’, in G Bohme & N Stehr (eds) The Knowledge Society: The Growing Impact of Scientific Knowledge on Social Relations, D. Reidel, Dordrecht, pp. 129-159.

Evans, G 2009, 'Rethinking Military Intelligence Failure – Putting the Wheels Back on the Intelligence Cycle', Defence Studies, vol. 9, no. 1, pp. 22-46.

Evans, JM & Kebbell, MR 2012, 'The effective analyst: a study of what makes an effective crime and intelligence analyst', Policing and Society, vol. 22, no. 2, pp. 204-19.

Feltz, A 2008, 'Problems with the appeal to intuition in epistemology', Philosophical Explorations, vol. 11, no. 2, pp. 131-41.

139

Fennell, J 2016, 'Polanyi’s “Illumination:” Aristotelian Induction or Peircean Abduction?', Tradition and Discovery: The Polanyi Society Periodical, vol. 42, no. 3, pp. 42-54.

Ferguson, AG 2012, 'Predictive Policing and Reasonable Suspicion', Emory Law Journal, vol. 62, no. 2, pp. 261-325.

Feyerabend, PK 1970, ‘How to be a good empiricist-A plea for tolerance in matters epistemological’ in B Brody (ed) Readings in the Philosophy of Science, Prentice Hall, Englewood Cliffs, NJ, pp. 119-142.

Fingar, T 2011a, Reducing uncertainty: Intelligence analysis and national security, Stanford University Press, Stanford, CA.

Fingar, T 2011b, 'Analysis in the US intelligence community: Missions, masters, and methods', in B Fischoff & C Chauvin (eds) Intelligence analysis: Behavioral and social scientific foundations, The National Academies Press, Washington DC, pp. 3-27.

Fini, G 2004, 'New Frontiers of Intelligence Analysis', paper presented to Papers presented at the conference on “New Frontiers of Intelligence Analysis: Shared Threats, Diverse Perspectives, New Communities”, Rome, Italy.

Fiore, SM, Hoffman, RR & Salas, E 2008, 'Learning and performance across disciplines: An epilogue for moving multidisciplinary research toward an interdisciplinary science of expertise', Military Psychology, vol. 20, no. S1, p. S155.

Fischer, F & Mandell, A 2009, 'Michael Polanyi's Republic of Science: The Tacit Dimension', Science as Culture, vol. 18, no. 1, pp. 23-46.

Fishbein, W & Traverton, G 2004, Making sense of transnational threats, Central Intelligence Agency, Washington.

Flick, U 2014, An introduction to qualitative research, Sage, Thousand Oaks, CA.

Flood, P 2004, Report of the inquiry into Australian Intelligence agencies, Australian Government, Canberra.

Folker, R 2000, 'Intelligence Analysis in theater Joint Intelligence Centers - An Experiment in Applying Structured Methods - Robert Folker', Joint Military Intelligence College, Occasional Paper no. 7.

Fothe, S 2012, 'Review of Tihamér Margitay (ed.): Knowing and Being. Perspectives on the philosophy of Michael Polanyi', Studies in East European Thought, vol. 64, no. 1-2, pp. 153-7.

Fournie, DA 2004, 'Harsh Lessons: Roman Intelligence in the Hannibalic War', International Journal of Intelligence and CounterIntelligence, vol. 17, no. 3, pp. 502-38.

Frank, AB 2012, 'Agent-Based Modeling in Intelligence Analysis', PhD thesis, George Mason University, Fairfax, VA.

140

Friedman, RS 1998, 'Open source intelligence', Parameters, vol. 28, no. 2, p. 159.

Gainor, R & Bouthillier, F 2014, 'Competitive Intelligence Insights for Intelligence Measurement', International Journal of Intelligence and CounterIntelligence, vol. 27, no. 3, pp. 590-603.

Galotti, KM 2017, Cognitive psychology in and out of the laboratory, Sage Publications, Thousand Oaks, CA.

Garfinkel, H, Lynch, M & Livingstone, E 1981, 'The Work of Discovering Science Construed with Materials from the Optically Discovered Pulsar', Philosophy of the Social Sciences, vol. 11, pp. 131-58.

Garrick, J & Chan, A 2017, 'Knowledge management and professional experience: the uneasy dynamics between tacit knowledge and performativity in organizations', Journal of Knowledge Management, vol. 21, no. 4, pp. 872-884.

Gaudet, J 2013, 'It takes two to tango: knowledge mobilization and ignorance mobilization in science research and innovation', Prometheus, vol. 31, no. 3, pp. 169-87.

Gelwick, R 2005, 'Michael Polanyi’s Search for Truth: Michael Polanyi's daring epistemology and the hunger for teleology', Zygon, vol. 40, no. 1.

Gendron, A 2012, 'Improving the IC's Analytical Performance', International Journal of Intelligence and CounterIntelligence, vol. 25, no. 2, pp. 420-6.

Gentry, JA 2014, 'Managers of Analysts: The Other Half of Intelligence Analysis', Intelligence and National Security, vol. 31, no. 2, pp.154-177.

Gentry, JA 2015, 'Has the ODNI Improved U.S. Intelligence Analysis?', International Journal of Intelligence and CounterIntelligence, vol. 28, no. 4, pp. 637-61.

Gentry, JA 2016, 'The “Professionalization” of Intelligence Analysis: A Skeptical Perspective', International Journal of Intelligence and CounterIntelligence, vol. 29, no. 4, pp. 643-76.

George, RZ 2004, 'Fixing the Problem of Analytical Mind-Sets: Alternative Analysis', International Journal of Intelligence and CounterIntelligence, vol. 17, no. 3, pp. 385-404.

George, RZ & Bruce, JB (eds) 2008, Analyzing Intelligence: Origins, Obstacles, and Innovations, Georgetown University Press, Washington D.C.

George, RZ 2010, 'Beyond Analytic Tradecraft', International Journal of Intelligence and CounterIntelligence, vol. 23, no. 2, pp. 296-306.

George, RZ 2011, 'Reflections on CIA analysis: is it finished?', Intelligence and National Security, vol. 26, no. 1, pp. 72-81.

Gigerenzer, G & Brighton, H 2007, 'Can Hunches Be Rational?', Journal of Law, Economics & Policy, vol. 4, no. 1, pp. 155-176.

141

Gilbert, MRJ 2000, 'Epistemological resources for thought experimentation in science learning', International Journal of Science Education, vol. 22, no. 5, pp. 489- 506.

Gilbert, N 2008, Researching social life, Sage, London.

Gill, P 1998, 'Police intelligence processes: A study of criminal intelligence units in Canada', Policing and Society, vol. 8, no. 4, pp. 339-65.

Gill, P 2000, Rounding Up the Usual Suspects, Ashgate, Aldershot.

Gill, P 2009, 'Theories of intelligence: Where are we, where should we go and how might we proceed?', in P Gill, S Marrin & M Phythian (eds), Intelligence Theory: Key questions and debates, Routledge, USA, pp. 208-226.

Gillispie, CC 2016, The edge of objectivity: An essay in the history of scientific ideas, Princeton University Press, Princeton, NJ.

Gilovich, T, Griffin, D & Kahneman, D 2002, Heuristics and biases: The psychology of intuitive judgment, Cambridge University Press, Cambridge.

Giurgiu, L, Barsan, G & Mosteanu, D 2015, 'The Technical Dimension Of Knowledge Management In The Context Of Learning And Training', in Proceedings of the International Management Conference, vol. 9, pp. 506-12.

Gladwell, M 2011, Blink, Brown and Company, New York.

Glomseth, R & Gottschalk, P 2009, 'Police personnel cultures: a comparative study of counter terrorist and criminal investigation units', Criminal Justice Studies, vol. 22, no. 1, pp. 3-15.

Godson, R & Wirtz, JJ 2000, 'Strategic Denial and Deception', International Journal of Intelligence and CounterIntelligence, vol. 13, no. 4, pp. 424-37.

Goodman, MS 2009, 'Jones' Paradigm: The How, Why, and Wherefore of Scientific Intelligence', Intelligence and National Security, vol. 24, no. 2, pp. 236- 56.

Goodman, MS, & Omand, D (eds) 2008, ‘Intelligence Today and Tomorrow’, Studies in Intelligence, vol. 52, no. 4, December 2008, DTIC Document.

Gormley, DM 2004, 'The limits of intelligence: Iraq's lessons', Survival, vol. 46, no. 3, pp. 7-28.

Gottschalk, P 2007, 'Organizational culture as determinant of enterprise information systems use in police investigations', Enterprise Information Systems, vol. 1, no. 4, pp. 443-55.

Grant, BM & Giddings, LS 2002, 'Making sense of methodologies: A paradigm framework for the novice researcher', Contemporary Nurse, vol. 13, no. 1, pp. 10- 28.

Grant, KA 2007, 'Tacit knowledge revisited–we can still learn from Polanyi', The Electronic Journal of Knowledge Management, vol. 5, no. 2, pp. 173-80.

142

Greco, J & Sosa, E 2002, The Blackwell guide to epistemology, Wiley-Blackwell, Hoboken, NJ.

Grene, M 1977, 'Tacit knowing: grounds for a revolution in philosophy', Journal of the British society for phenomenology, vol. 8, no. 3, pp. 164-71.

Groarke, L 2009, An Aristotelian account of induction: Creating something from nothing, vol. 49, McGill-Queen's Press-MQUP, Kingston, Canada.

Groopman, J 2007, How Doctors Think, Houghton Mifflin, Boston.

Gulick, W 2016, 'Relating Polanyi’s Tacit Dimension to Social Epistemology: Three Recent Interpretations', Social Epistemology, vol. 30, no. 3, pp. 297-325.

Gyngell, A 2011, 'The Challenges of Intelligence, speech to the Lowy Institute for International Policy', Canberra, 30 March 2011.

Hadamard, J 1945, The psychology of the invention in the mathematical field, Dover Publications, Mineola, New York.

Hall, JA 1982, 'Polanyi And Jungian Psychology Dream-Ego and Waking-Ego', Journal of Analytical Psychology, vol. 27, pp. 239-54.

Hall, WM & Citrenbaum, G 2010, Intelligence Analysis : How to Think in Complex Environments, AUSA Book, Praeger Security International, Santa Barbara, CA.

Hall, WM & Citrenbaum, G 2013, 'Intelligence Collection: How to Plan and Execute Intelligence Collection in Complex Environments', Journal of Strategic Security, vol. 6, no. 3, pp. 129-131.

Hamlyn, DW 2017, The psychology of perception: a philosophical examination of Gestalt theory and derivative theories of perception, vol. 13, Routledge, London.

Hare, N & Coghill, P 2016, 'The future of the intelligence analysis task', Intelligence and National Security, vol. 31, no. 6, pp. 858-870.

Hart, D & Simon, S 2006, 'Thinking straight and talking straight: problems of intelligence analysis', Survival, vol. 48, no. 1, pp. 35-60.

Hatch, SJ 2013, 'Managing the “Reliability Cycle”: An Alternative Approach to Thinking About Intelligence Failure', Studies in Intelligence, vol. 57, no. 2, pp. 29- 37.

Hawley, M 2008, 'Measuring Intelligence Successes and Failures', in Intelligence Performance Conference Proceedings, Canberra.

Hegarty, S 2000, 'Teaching as a Knowledge-based Activity', Oxford Review of Education, vol. 26, no. 3-4, pp. 451-65.

Henry, SG 2010, 'Polanyi's tacit knowing and the relevance of epistemology to clinical medicine', Journal of Evaluation in Clinical Practices, vol. 16, no. 2, pp. 292-7.

Herbert, M 2006, 'The Intelligence Analyst as Epistemologist', International Journal of Intelligence and CounterIntelligence, vol. 19, no. 4, pp. 666-84.

143

Herbert, M 2013, 'The Motley of Intelligence Analysis: Getting over the Idea of a Professional Model', International Journal of Intelligence and CounterIntelligence, vol. 26, no. 4, pp. 652-65.

Herman, M 1996, Intelligence Power in Peace and War, Cambridge University Press, Cambridge.

Heuer, RJ Jr. 1999, 'Psychology of Intelligence Analysis', Center for the Study of Intelligence.

Heuer, RJ Jr. 2005, 'How Does ACH Improve Analysis?', retrieved 09 January 2018, http://www.pherson.org/wp-content/uploads/2013/06/06.-How-Does-ACH- Improve-Analysis_FINAL.pdf

Heuer, RJ Jr. 2008a, 'Small Group Processes', retrieved 09 January 2018, http://www.pherson.org/wp-content/uploads/2013/06/02.-Small-Group- Processes.pdf

Heuer, RJ Jr. 2008b, 'Taxonomy of Structured Analytic Techniques', paper presented to International Studies Association 2008 Annual Convention, San Francisco, CA, retrieved 09 January 2018, http://www.pherson.org/wp- content/uploads/2013/06/03.-Taxonomy-of-Structured-Analytic- Techniques_FINAL.pdf

Heuer, RJ Jr. 2009, 'The evolution of structured analytic techniques', presentation to the National Academy of Science, National Research Council Committee on Behavioral and Social Science Research to Improve Intelligence Analysis for National Security, Washington, DC.

Heuer, RJ Jr., Pherson, RH & Beebe, SM 2009, 'Analytic Teams, Social Networks and Collaborative Behavior', retrieved 09 January 2018, http://www.pherson.org/wp- content/uploads/2013/11/10.AnalyticTeamsSocialNetworksCollaborativeBehavior _FINAL.pdf

Hicks, DC 2013, ‘Review of The art and science of intelligence analysis by Julian Richards’, Policing and Society, vol. 23, no. 2, pp. 279-284.

Hodgkin, RA 1992, 'Michael Polanyi on the Activity of Knowing—the bearing of his ideas on the theory of multiple intelligences', Oxford Review of Education, vol. 18, no. 3, pp. 253-67.

Hoffman, R, Henderson, S, Moon, B, Moore, DT & Litman, JA 2011, 'Reasoning difficulty in analytical activity', Theoretical Issues in Ergonomics Science, vol. 12, no. 3, pp. 225-40.

Holmes, JR 1992, 'Logic, inference and statistics in the analysis phase of the intelligence cycle', Journal of the Australian Institute of Professional Intelligence Officers, vol. 1, no. 1, p. 21.

Horan, D 2007, 'Hunch, or the Whispered Voice of Experience', Journal of Law, Economics & Policy, vol. 4, no. 1, pp. 13-24.

144

Horelick, AL 1964, 'The Cuban Missile Crisis: an analysis of Soviet calculations and behavior', World Politics, vol. 16, no. 3, pp. 363-89.

Howis, D 2015, 'A systems thinking approach to threat assessments', Journal of the Australian Institute of Professional Intelligence Officers, vol. 23, no. 1, p. 3.

Hudson, CG 2009, 'Decision Making in Evidence Based Practice: Science and Art', Smith College Studies in Social Work, vol. 79, no. 2, pp. 155-74.

Hulnick, AS 2006, 'What's wrong with the Intelligence Cycle', Intelligence and National Security, vol. 21, no. 6, pp. 959-79.

Hunter, D & MacDonald, MN 2017, 'The emergence of a security discipline in the post 9-11 discourse of US security organisations', Critical Discourse Studies, vol. 14, no. 2, pp. 206-22.

Immerman, RH 2011, 'Transforming analysis: The intelligence community's best kept secret', Intelligence and National Security, vol. 26, no. 2-3, pp. 159-81.

Ingold, T 2000, The perception of the environment: essays on livelihood, dwelling and skill, Psychology Press, New York.

Innes, M, Fielding, N & Cope, N 2005, ''The Appliance of Science?': The Theory and Practice of Crime Intelligence Analysis', British Journal of Criminology, vol. 45, no. 1, pp. 39-57.

Intelligence Analysis in the 21st Century: An Interview with Dr. Mark Lowenthal, 2016, video, Spycast Museum, Washington DC, 15 March, retrieved 9 January 2018, https://www.spymuseum.org/multimedia/spycast/episode/intelligence- analysis-in-the-21st-century-an-interview-with-dr-mark-lowenthal/.

Jacobs, S 2001, 'Michael Polanyi on Tacit Cognitive Relativist', Heythrop Journal, vol. 42, no. 4, pp. 463–79.

Jacobs, S 2009, 'Ideas of Knowledge in Practice', in Struan Jacobs, Heather D’Cruz & A Schoo (eds), Knowledge in Practice in the Caring Professions, Ashgate, Farnham, England.

James, A 2017, 'Intelligence-led policing: comparing national approaches to its regulation and control', in Comparative policing, Edward Elgar Publishing, UK.

James, A, Phythian, M, Wadie, F & Richards, J 2017, 'The road not taken: understanding barriers to the development of police intelligence practice', The International Journal of Intelligence, Security, and Public Affairs, vol. 19, no. 2, pp. 77-91.

Jensen, CJ 2011, 'The Intelligence Officer Training Corps: An ROTC-Style Program for the IC', International Journal of Intelligence and CounterIntelligence, vol. 24, no. 4, pp. 733-46.

Jensen, KF 2018, 'Why Spy? The art of intelligence', Intelligence and National Security, vol. 33, no. 2, 296-298.

Jervis, R 2006, 'Reports, politics, and intelligence failures: The case of Iraq', The Journal of Strategic Studies, vol. 29, no. 1, pp. 3-52.

145

Jervis, R 2010, Why intelligence fails: lessons from the Iranian Revolution and the Iraq War, Cornell University Press.

Jha, S 2011, 'Wigner’s “Polanyian” Epistemology and the Measurement Problem: The Wigner–Polanyi Dialog on Tacit Knowledge', Physics in Perspective, vol. 13, no. 3, pp. 329-58.

Johnson, J 2006a, 'Consequences of positivism: A pragmatist assessment', Comparative Political Studies, vol. 39, no. 2, pp. 224-52.

Johnson, L 1996, 'Analysis For a New Age', Intelligence and National Security, vol. 11, no. 4, pp. 657-671.

Johnson, L 2000, Policing Britain: Risk, Security and Governance, Longman, London.

Johnson, LK 1986, 'Making the intelligence “Cycle” work', International Journal of Intelligence and CounterIntelligence, vol. 1, no. 4, pp. 1-23.

Johnson, LK 2009, 'Sketches for a theory of strategic intelligence', in P Gill, S Marrin & M Phythian (eds), Intelligence Theory: Key questions and debates, Routledge, USA, pp. 33-52.

Johnson, LK 2010, The Oxford Handbook of National Security Intelligence, Oxford University Press, Oxford.

Johnson, LK 2017, National security intelligence, John Wiley & Sons, Hoboken, NJ.

Johnson, R 2003, 'Integrating Methodologists into Teams of Substantive Experts', Studies in Intelligence, vol. 47, no. 1, pp. 57-65.

Johnson, R 2005, Analytic Culture in the US Intelligence Community- An Ethnographic Study, Central Intelligence Agency, Washington, DC.

Johnson, R 2007, 'Developing a Taxonomy of Intelligence Analysis Variables', Studies in Intelligence, vol. 47, no. 3. Retrieved 9 January 2018, https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/csi- studies/studies/vol47no3/article05.html

Johnson, S 2006b, The ghost map: The story of London's most terrifying epidemic--and how it changed science, cities, and the modern world, Penguin, New York.

Johnson, S 2016, 'Tacit Knowledge: An Assessment of Michael Polanyi's Epistemology', PhD thesis, Regent University, VA.

Jones, C 2007, 'Intelligence reform: The logic of information sharing', Intelligence and National Security, vol. 22, no. 3, pp. 384-401.

Jones, M 2009, The Thinker’s Toolkit: 14 Powerful Techniques for Problem Solving, Crown Publishing Group, New York.

Jones, RV 1993, 'The Scientific Intelligencer', Studies in Intelligence, vol. 6, no. 4.

146

Judge, TA, Colbert, AE & Ilies, R 2004, 'Intelligence and leadership: a quantitative review and test of theoretical propositions', Journal of Applied Psychology, vol. 89, no. 3, p. 542.

Kahn, D 2009, 'An Historical Theory of Intelligence', in P Gill, S Marrin & M Phythian (eds), Intelligence Theory: Key questions and debates, Routledge, USA, pp. 4-15.

Kahneman, D 1973, Attention and effort, vol. 1063, Prentice-Hall Englewood Cliffs, NJ.

Kahneman, D 2003, 'Maps of bounded rationality: Psychology for behavioral economics', The American economic review, vol. 93, no. 5, pp. 1449-75.

Kahneman, D 2011, Thinking, fast and slow, Macmillan, Basingstoke, UK.

Kahneman, D & Tversky, A 1982, 'The psychology of preferences', Scientific American, vol. 246, no. 1, pp. 160-173.

Kaiser, D 2005, 'Making tools travel: Pedagogy and the transfer of skills in postwar theoretical physics', in D Kaiser (ed), Pedagogy and the practice of science: Historical and contemporary perspectives, MIT Press, Cambridge, MA, pp. 41-74.

Kałamała, P, Sadowska, A, Ordziniak, W & Chuderski, A 2017, 'Gestalt effects in visual working memory: Whole-part similarity works, symmetry does not', Experimental Psychology, vol. 64, no. 1, pp. 5-13.

Kamarck, EC 2005, ‘Transforming the intelligence community: Improving the collection and management of information’, Belfer Center for Science and International Affairs, Harvard University, retrieved 9 January 2018, https://www.belfercenter.org/publication/transforming-intelligence-community- improving-collection-and-management-information

Kan, KJ, Wicherts, JM, Dolan, CV & van der Maas, HL 2013, 'On the nature and nurture of intelligence and specific cognitive abilities: the more heritable, the more culture dependent', Psychological Science, vol. 24, no. 12, pp. 2420-8.

Kane, J 1982, 'A Philosophical Analysis of Michael Polanyi's Concepts of Indwelling and Heuristic Vision in the Process of Scientific Inquiry and Discovery', PhD thesis, New York University.

Kang, S-M & Lee, H 2014, 'Detecting Deception by Analyzing Written Statements in Korean', Linguistic Evidence in Security, Law and Intelligence, vol. 2, no. 2, pp. 1-10.

Karpenko, LP & Claggett, S 2017, Strange Science: Investigating the Limits of Knowledge in the Victorian Age, University of Michigan Press, Ann Arbor, MI.

Kay, DA 1995, 'Denial and deception practices of WMD proliferators: Iraq and beyond', The Washington Quarterly, vol. 18, no. 1, pp. 83-105.

Kean, TH & Hamilton, LH 2004, The 9/11 Report: The national commission on terrorist attacks upon the United States, St. Martin's Paperbacks, New York.

147

Keil, FC & Wilson, RA 2000, Explanation and cognition, MIT Press, Cambridge, MA.

William Kelleher, Respect and Empathy in the Social Science Writings of Michael Polanyi, Tradition and Discovery: The Polanyi Society Periodical, Vol 35, Issue 1, 2008/2009

Kelling, GL & Coles, CM 1997, Fixing broken windows: Restoring order and reducing crime in our communities, Simon and Schuster, New York.

Kent, S 1949, Strategic intelligence for American World Policy, Princeton University Press, Princeton, NJ.

Kent, S 1964, 'A Crucial Estimate Relived', Studies in Intelligence, vol. 8, no. 2, pp. 1-18.

Kent, S 1967, Writing history, Appleton-Century-Crofts, New York.

Kerbel, J 2008, 'Lost for words: The intelligence community's struggle to find its voice', Parameters, vol. 38, no. 2, p. 102.

Kerr, R, Wolfe, T, Donegan, R & Pappas, A 2005, 'Issues for the US Intelligence Community', Studies in Intelligence, vol. 49, no. 3, pp. 47-54.

Kessel, FS 1969, 'The philosophy of science as proclaimed and science as practiced: 'Identity' or 'dualism?'', American Psychologist, vol. 24, no. 11, pp. 999- 1005.

Khalsa, S 2009, 'The Intelligence Community Debate over Intuition versus Structured Technique: Implications for Improving Intelligence Warning', The Journal of Conflict Studies, vol. 29, https://journals.lib.unb.ca/index.php/jcs/article/view/15234/20838.

Kingstone, A, Smilek, D & Eastwood, JD 2008, 'Cognitive ethology: A new approach for studying human cognition', British Journal of Psychology, vol. 99, no. 3, pp. 317-40.

Kiyimba, K 2009, 'Michael Polanyi's theory of tacit knowledge: An epistemology of skill in science', PhD thesis, London School of Economics and Political Science, UK.

Knorr-Cetina, K 1999, Epistemic Cultures, Harvard University Press, Boston, MA.

Knutsson, J 2017, 'Advancing policing by using, producing and diffusing evidence', in J Knutsson & L Tompson (eds), Advances in Evidence-Based Policing, Routledge, London, p. 157.

Kreuzer, MP 2016, 'Professionalizing Intelligence Analysis: An Expertise and Responsibility Centered Approach', Intelligence and National Security, vol. 31, no. 4, pp. 579-97.

148

Kristiansen, S, Jensen, SM & Trabjerg, MC 2014, 'Youth gambling as risky business: An examination of risk perception and perception of skill and luck among Danish adolescents', Journal of Gambling Issues, no. 29, pp. 1-22.

Kuhn, T 1962, The Structure of Scientific Revolutions, University of Chicago Press, Chicago.

Lahneman, WJ 2010a, 'Knowledge-Sharing in the Intelligence Community After 9/11', International Journal of Intelligence and CounterIntelligence, vol. 17, no. 4, pp. 614-33.

Lahneman, WJ 2010b, 'The Need for a New Intelligence Paradigm', International Journal of Intelligence and CounterIntelligence, vol. 23, no. 2, pp. 201-25.

Lahneman, WJ & Arcos, R 2017, 'Experiencing the art of intelligence: using simulations/gaming for teaching intelligence and developing analysis and production skills', Intelligence and National Security, vol. 32, no. 7, pp. 972-985.

Lanier, MM & Cooper, AT 2016, 'From papyrus to cyber: how technology has directed law enforcement policy and practice', Criminal Justice Studies, vol. 29, no. 2, pp. 92-104.

Laqueur, W 1983, 'The Question of Judgment Intelligence and Medicine', The Journal of Contemporary History, vol. 18, pp. 533–48.

Lathrop, CE 2008, The literary spy: The ultimate source for quotations on espionage & intelligence, Yale University Press, New Haven, CT.

Latour, B 1990, 'Postmodern? No, simply amodern! Steps towards an anthropology of science', Studies in History and Philosophy of Science Part A, vol. 21, no. 1, pp. 145-71.

Lawson, S 2003, International Relations, Polity Press, Cambridge.

Lawson, S 2008, 'Political studies and the contextual turn: A methodological/normative critique', Political Studies, vol. 56, no. 3, pp. 584-603.

Lefebvre, S 2004, 'A Look at Intelligence Analysis', International Journal of Intelligence and CounterIntelligence, vol. 17, no. 2, pp. 231-64.

Legrand, T & Vogel, L 2012, ‘Forensic Intelligence’, Briefing paper, issue 9, ARC Centre of Excellence in Policing and Security, Queensland.

Lemaitre, B 2017, 'Science, narcissism and the quest for visibility', The FEBS Journal, vol. 284, no. 6, pp. 875-82.

Leppänen, A & Kankaanranta, T 2017, 'Cybercrime investigation in Finland', Journal of Scandinavian Studies in Criminology and Crime Prevention, vol. 18, no. 2, pp. 157-175.

Lerner, CS 2007, 'Introduction to Police Hunches', Journal of Law, Economics & Policy, vol. 4, no. 1, pp. 4-12.

149

Lewandowski, C, Carter, JG & Campbell, WL 2017, 'The Utility of Fusion Centres to Enhance Intelligence-Led Policing: An Exploration of End-Users', Policing: a journal of policy and practice, p. pax005, https://doi.org/10.1093/police/pax005.

Li, S & Seale, C 2007, 'Managing criticism in Ph. D. supervision: A qualitative case study', Studies in Higher Education, vol. 32, no. 4, pp. 511-26.

Lillbacka, RGV 2013, 'Realism, Constructivism, and Intelligence Analysis', International Journal of Intelligence and CounterIntelligence, vol. 26, no. 2, pp. 304-31.

Lowenthal, M 2011, 'Intelligence Analysis. Guide to its Study', The Intelligencer, Journal of US Intelligence Studies, Association of Former Intelligence Officers, vol. 18, no. 3, p. 61.

Lowenthal, MM 2008, 'Intelligence in transition: Analysis after September 11 and Iraq', Analyzing Intelligence: Origins, obstacles, and innovations, pp. 226-37.

Lowenthal, MM 2013, 'A Disputation on Intelligence Reform and Analysis: My 18 Theses', International Journal of Intelligence and CounterIntelligence, vol. 26, no. 1, pp. 31-7.

Lowenthal, MM 2014, Intelligence: From secrets to policy, CQ Press, Washington DC.

Lowenthal, MM & Marks, RA 2015, 'Intelligence Analysis: Is It As Good As It Gets?', International Journal of Intelligence and CounterIntelligence, vol. 28, no. 4, pp. 662-5.

Lowney, C 2011, 'Ineffable, tacit, explicable and explicit: qualifying knowledge in the age of “intelligent” machines', Tradition and Discovery: The Polanyi Society Periodical, vol. 38, no. 1, pp. 18-37.

Lyda, R & Hamrock, J 2006, 'Exploring Investigative Methods for Identifying and Profiling Serial Bots', Journal of Digital Forensic Practice, vol. 1, no. 3, pp. 165- 77.

Lynch, M 1993, The Scientific Practice and Ordinary Action, Cambridge University Press, Cambridge.

MacKenzie, D & Spinardi, G 1995, 'Tacit knowledge, weapons design, and the uninvention of nuclear weapons', American Journal of Sociology, vol. 101, no. 1, pp. 44-99.

Mackenzie, JIM 2011, 'Positivism and Constructivism, Truth and ‘Truth’', Educational Philosophy and Theory, vol. 43, no. 5, pp. 534-46.

Maguire, M 2000, 'Policing by Risks and Targets: Some Dimensions and Implications of Intelligence-Led Crime and Control', Policing and Society, vol. 9, pp. 315-36.

Manjikian, M 2008, 'Diagnosis, Intervention, and Cure: The Illness Narrative in the Discourse of the Failed State', Alternatives: Global, Local, Political, vol. 33, no. 3, pp. 335-57.

150

Manjikian, M 2013, 'Positivism, Post-Positivism, and Intelligence Analysis', International Journal of Intelligence and CounterIntelligence, vol. 26, no. 3, pp. 563-82.

Manjikian, M 2014, 'Reading Lolita in Langley: The Unreliable Narrator as a Device to Evaluate Intelligence Credibility', Intelligence and National Security, vol. 30, no. 5, pp. 704-722.

Manning, P 2001, 'Technology's Ways: Information Technology, Crime Analysis and the Rationalizing of Policing', Criminal Justice, vol. 1, p. 83-103.

Manningham-Buller, E 2005, 'The International Terrorist Threat and the dilemmas in countering it', MI5 Security Service, retrieved 9 January 2018, https://www.mi5.gov.uk/news/the-international-terrorist-threat-and-the-dilemmas- in-countering-it.

Marchio, J 2014, 'Analytic Tradecraft and the Intelligence Community: Enduring Value, Intermittent Emphasis', Intelligence and National Security, vol. 29, no. 2, pp. 159-83.

Margolis, J, Robarge, D, Olcott, A, Mercado, SC, Bradford, M & Peake, H 2012, ‘Probing the Implications of Changing the Outputs of Intelligence: A Report of the 2011 Analyst-IC Associate Teams Program’, Studies in Intelligence, Vol. 56, no. 1, retrieved 19 January 2018 https://www.cia.gov/library/center-for-the-study-of- intelligence/csi-publications/csi-studies/studies/vol.-56-no.-1/index.html

Marrin, S 2003a, 'CIA’s Kent School Improving Training For New Analysts', International Journal of Intelligence and CounterIntelligence, vol. 16, no. 4, pp. 609-37.

Marrin, S 2003b, ‘Improving CIA Analysis by Overcoming Institutional Obstacles’, in RG Swenson (ed), Bringing Intelligence About: Practitioners Reflect on Best Practices Joint Military Intelligence College, Washington, DC, pp. 41-60.

Marrin, S 2004, 'Preventing Intelligence Failures by Learning from the Past', International Journal of Intelligence and CounterIntelligence, vol. 17, no. 4, pp. 655–72.

Marrin, S 2005, 'Intelligence Analysis: Turning a Craft into a Profession’, paper presented at the International Conference on Intelligence Analysis, retrieved 9 January 2018, http://www.ialeia.org/docs/40168353-Marrin-Intelligence-Analysis- Turning-a-Craft-Into-a-Profession.pdf

Marrin, S 2007a, 'Intelligence Analysis Structured Methods or Intuition', American Intelligence Journal, vol. 25, no. 1, pp. 7-16.

Marrin, S 2007b, 'Intelligence Analysis Theory Explaining and Predicting Analytic Responsibilities', Intelligence and National Security, vol. 22, no. 6, pp. pp. 821 – 46.

Marrin, S 2008, 'Training and Educating U.S. Intelligence Analysts', International Journal of Intelligence and CounterIntelligence, vol. 22, no. 1, pp. 131-46.

151

Marrin, S 2011a, 'The 9/11 Terrorist Attacks: A Failure of Policy Not Strategic Intelligence Analysis', Intelligence and National Security, vol. 26, no. 2-3, pp. 182- 202.

Marrin, S 2011b, Improving intelligence analysis: bridging the gap between scholarship and practice, Routledge, London.

Marrin, S 2012a, 'Evaluating the Quality of Intelligence Analysis: By What (Mis) Measure?', Intelligence and National Security, vol. 27, no. 6, pp. 896-912.

Marrin, S 2012b, 'Intelligence Studies Centers: Making Scholarship on Intelligence Analysis Useful', Intelligence and National Security, vol. 27, no. 3, pp. 398-422.

Marrin, S 2012c, 'Is Intelligence Analysis an Art or a Science?', International Journal of Intelligence and CounterIntelligence, vol. 25, no. 3, pp. 529-45.

Marrin, S 2013, 'Rethinking Analytic Politicization', Intelligence and National Security, vol. 28, no. 1, pp. 32-54.

Marrin, S 2016, 'Improving intelligence studies as an academic discipline', Intelligence and National Security, vol. 31, no. 2, pp. 266-79.

Marrin, S 2017, 'Understanding and improving intelligence analysis by learning from other disciplines', Intelligence and National Security, vol. 32, no. 5, pp. 539- 47.

Marrin, S & Clemente, JD 2005, 'Improving Intelligence Analysis by Looking to the Medical Profession', International Journal of Intelligence and CounterIntelligence, vol. 18, no. 4, pp. 707-29.

Marrin, S & Clemente, JD 2006, 'Modeling an Intelligence Analysis Profession on Medicine', International Journal of Intelligence and CounterIntelligence, vol. 19, no. 4, pp. 642-65.

Marrin, S & Torres, E 2017, 'Improving how to think in intelligence analysis and medicine', Intelligence and National Security, vol. 32, no. 5, pp. 649-62.

Martin, B 2012, 'Kahneman in practice', Prometheus, vol. 30, no. 4, pp. 457-60.

Martin, CP, Philp, W & Hall, WP 2009, 'Temporal convergence for knowledge management', Australasian Journal of Information Systems, vol. 15, no. 2, pp. 133-148.

Martin, K, Kebbell, M, Porter, DL & Townsley, DM 2011a, 'A Qualitative Examination of Analyst Decision Making in Criminal Intelligence', ARC Centre of Excellence in Policing and Security Briefing Papers, no. 4.

Martin, K, Kibbell, M, Porter, L & Townsley, M 2011b, 'The Paradox of Intuitive Analysis and the Implicaitons for the Professionalism', Journal of the Australian Institute of Professional Intelligence Officers, vol. 19, no. 1.

Martinez-Conesa, I, Soto-Acosta, P, Carayannis, EG & Carayannis, EG 2017, 'On the path towards open innovation: Assessing the role of knowledge

152

management capability and environmental dynamism in SMEs', Journal of Knowledge Management, vol. 21, no. 3, pp. 553-70.

Massingham, P 2004, 'Linking business level strategy with activities and knowledge resources', Journal of Knowledge Management, vol. 8, no. 6, pp. 50- 62.

Massingham, P 2016, 'Knowledge accounts', Long Range Planning, vol. 49, no. 3, pp. 409-25.

Mathams, RH 1982, Sub Rosa: Memoirs of an Australian Intelligence Analyst, Allen & Unwin.

Mathams, RH 1988, ‘The intelligence analyst's notebook’, Working Paper, Research School of Pacific Studies, Australian National University, Canberra.

Maxwell, J 2005, 'Conceptual framework: what do you think is going on', in J Maxwell (ed), Qualitative research design: An interactive approach, Sage, Thousand Oaks, pp. 33-63.

McNamara, LA 2001, 'Ways of Knowing About Weapons: The Cold War's End at the Los Alamos National Laboratory', PhD thesis, University of New Mexico.

Meadows, LM & Morse, JM 2001, 'Constructing evidence within the qualitative project', in LM Meadows, JM Swanson & AJ Kuzel (eds), The nature of qualitative evidence, Sage, Thousand Oaks, pp. 187-200.

Medina, C 2002, 'The Coming Revolution in Intelligence Analysis - What to do when traditional models fail', Studies in Intelligence, vol. 46, no. 3.

Meek, EL 2011, Loving to Know: Covenant Epistemology, Cascade Books, Oregon.

Mehlberg, H 1955, The Method of Science, its range and limits. Science and Freedom, Martin Seeker & Warburg, London.

Merriam-Webster n.d., This is the Difference Between a Hypothesis and a Theory: In scientific reasoning, they're two completely different things, retrieved 2.08.17, .

Miller, D 2011, 'Objective Knowledge', updated foreword to K Popper 2003, Ezagutza objektiboa. Ikusmolde ebolutibo bat [Objective Knowledge], Euskaran trans. A Gabikagojeaskoa, Donostia, San Sebastián, Klasikoak. Retrieved 19 January 2018, https://warwick.ac.uk/fac/soc/philosophy/people/miller/foreword2009.pdf

Miller, GA 1956, 'The magical number seven, plus or minus two: some limits on our capacity for processing information', Psychological review, vol. 63, no. 2, p. 81.

Ministry of Defence 2010, Understanding and decision making, Joint Doctrine Publication 04, United Kingdom Ministry of Defence, retrieved 09 January 2018, https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/58 4177/doctrine_uk_understanding_jdp_04.pdf

153

Mitchell, B 2005, 'The intelligence cycle and an information management process model: A comparative analysis', Journal of the Australian Institute of Professional Intelligence Officers, vol. 14, no. 2, p. 14.

Modern Intelligence Analysis: From Art to Science? 2011, video, Spycast Museum, Washington DC, 1 July, retrieved 9 January 2018, https://www.spymuseum.org/multimedia/spycast/episode/modern-intelligence- analysis-from-art-to-science/.

Mohajan, HK 2016, 'Sharing of Tacit Knowledge in Organizations: A Review', American Journal of Computer Science and Engineering American Journal of Computer Science and Engineering, vol. 3, no. 2, pp. 6-19.

Moini, G 2011, 'How participation has become a hegemonic discursive resource: towards an interpretivist research agenda', Critical Policy Studies, vol. 5, no. 2, pp. 149-68.

Mol, A 2002, The body multiple: Ontology in medical practice, Duke University Press, Durham, NC.

Mole, C 2012, 'Three Philosophical Lessons for the Analysis of Criminal and Military Intelligence', Intelligence and National Security, vol. 27, no. 4, pp. 441-58.

Moore, DT 2007, Critical Thinking and Intelligence Analysis, National Defence Intelligence College, Washington, DC.

Moore, DT, Krizan, L & Moore, EJ 2005, 'Evaluating Intelligence: A Competency- Based Model', International Journal of Intelligence and CounterIntelligence, vol. 18, no. 2, pp. 204-20.

Moran, C 2016, 'Note on teaching intelligence', Intelligence and National Security, vol. 31, no. 1, pp. 118-30.

Mudd, P 2015, The HEAD Game: High-Efficiency Analytic Decision Making and the Art of Solving Complex Problems Quickly, WW Norton & Company, New York.

Muller, DG 2007, 'Intelligence Analysis in Red and Blue', International Journal of Intelligence and CounterIntelligence, vol. 21, no. 1, pp. 1-12.

Mullins, P 2002, 'Peirce's abduction and Polanyi's tacit knowing', The Journal of Speculative Philosophy, vol. 16, no. 3, pp. 198-224.

Mullins, P 2013, 'Michael Polanyi's Thought and Political Philosophy: An Introduction', Perspectives on Political Science, vol. 42, no. 3, pp. 152-3.

Narvaez, D 2012, 'Moral rationality', Tradition and Discovery, vol. 38, no. 2, pp. 25-33.

Newburn, T 2003, Handbook of Policing, Willan Publishing, Devon, UK.

Nicholls, D 2009, 'Qualitative research: Part three--Methods', International Journal of Therapy & Rehabilitation, vol. 16, no. 12.

154

Nolan, P 2012, 'A Curator Approach to Intelligence Analysis', International Journal of Intelligence and CounterIntelligence, vol. 25, no. 4, pp. 786-94.

Nolte, WM 2012, 'Intelligence in Context', International Journal of Intelligence and CounterIntelligence, vol. 25, no. 3, pp. 631-4.

Nonaka, I & Takeuchi, H 1995, The knowledge creation company: how Japanese companies create the dynamics of innovation, Oxford University Press, New York.

Nye, MJ 2011, Michael Polanyi and his generation: Origins of the social construction of science, University of Chicago Press, Chicago, IL.

O'Malley, P 2016, '‘Policing the Risk Society’ in the 21st Century', Sydney Law School Research Paper 16/11, retrieved 9 January 2018, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2729631.

O’Connor, J & McDermott, I 1997a, The Art of Systems Thinking: Essential Skills for Creativity and Problem Solving, Harper Collins Publishers, London, UK.

O’Connor, J & McDermott, I 1997b, The art of systems thinking: essential skills for creativity and problem solving, Thorsons, Harper Collins Publisher, London.

O’Neill, M & Fyfe, NR 2017, ‘Plural policing in Europe: relationships and governance in contemporary security systems’, Policing and Society, vol. 27, no. 1, pp. 1-5.

Odom, WE 2008, 'Intelligence Analysis', Intelligence and National Security, vol. 23, no. 3, pp. 316-32.

Office of National Assessments 2006, The Australian Intelligence Community: Agencies, functions, accountability and oversight, Office of National Assessments, Canberra.

Oliver, WR 2007, 'Medicine Looks Back at Intelligence', International Journal of Intelligence and CounterIntelligence, vol. 20, no. 3, pp. 564-6.

Omand, D 2014, Securing the state, Oxford University Press, Oxford.

Ormston, R, Spencer, L, Barnard, M & Snape, D 2014, 'The foundations of qualitative research', in J Ritchie, J Lewis, McNaughton Nicholls, C & Ormston, R (eds) Qualitative research practice: A guide for social science students and researchers, Sage, Los Angeles, pp. 1-25.

Ouagrham-Gormley, SB 2014, Barriers to Bioweapons: The Challenges of Expertise and Organization for Weapons Development, Cornell University Press, Ithaca, NY.

Ouagrham-Gormley, SB & Vogel, KM 2010, 'The social context shaping bioweapons (non) proliferation', Biosecurity and bioterrorism: biodefense strategy, practice, and science, vol. 8, no. 1, pp. 9-24.

Parliamentary Joint Committee on Law Enforcement 2013, Inquiry into the gathering and use of criminal intelligence, Parliamentary paper (Australia, Parliament); no. 119 (2013), 0727-4181, JCoL Enforcement, Canberra.

155

Trent, SA, Patterson, E & Woods, DD 2007, 'Challenges for Cognition in Intelligence Analysis', Journal of Cognitive Engineering and Decision Making, vol. 1, no. 1, pp. 75-97.

Patton, MQ 2005, Qualitative research, Wiley Online Library, Hoboken, NJ.

Peak, KJ & Glensor, RW 1999, Community policing and problem solving: Strategies and practices, Prentice Hall, Upper Saddle River, NJ.

Peck, D 2006, 'Tacit knowledge and practical action: Polanyi, hacking, heidegger and the tacit dimension', Master of Arts thesis, University of Guelph.

Perry, WL 2013, Predictive policing: The role of crime forecasting in law enforcement operations, Rand Corporation, Santa Monica, CA.

Peters, AM & Cohen, IM 2017, 'The mandate and activities of a specialized crime reduction policing unit in Canada', Police Practice and Research, vol. 18, no. 6, pp. 570-583.

Petranker, J 2005, 'The When of Knowing', The Journal of Applied Behavioral Science, vol. 41, no. 2, pp. 241-59.

Pfautz, J, Fichtl, T, Guarino, S, Carlson, E, Powell, G & Roth, E 2006, 'Cognitive Complexities Impacting Army Intelligence Analysis', Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 50, no. 3, pp. 452-6.

Pherson, KH & Pherson, RH 2012, Critical thinking for strategic intelligence, CQ Press, Washington DC.

Pherson, RH 2005, 'Overcoming Analytic Mindsets: Five Simple Techniques', Pherson Associates, LLC, retrieved 9 January 2018, http://www.pherson.org/wp- content/uploads/2013/11/01.-Overcoming-Analytic-Mindsets.-Five-Simple- Techniques_FINAL.pdf

Pherson, RH 2009, 'Breaking the Mold in Developing Training Courses on- Collaboration', Pherson Associates LLC, retrieved 9 January 2018, http://www.pherson.org/wp-content/uploads/2013/11/08.Breaking-the-Mold-in- Developing-Training-Courses-on-Collaboration_FINAL.pdf

Pherson, RH 2013, 'The Five Habits of the Master Thinker', Journal of Strategic Security, vol. 6, no. 3, pp. 54-60.

Pherson, RH & Beebe, SM 2015, Cases in Intelligence Analysis: Structured Analytic Techniques, second edition, CQ Press, Washington D.C.

Phythian, M 2009, 'Intelligence Analysis Today and Tomorrow', Security Challenges, vol. 5, no. 1, pp. 67-83.

Phythian, M 2012, 'Policing Uncertainty: Intelligence, Security and Risk', Intelligence and National Security, vol. 27, no. 2, pp. 187-205.

Phythian, M 2013, Understanding the intelligence cycle, Routledge, London.

156

Phythian, M 2017, 'Intelligence analysis and social science methods: exploring the potential for and possible limits of mutual learning', Intelligence and National Security, vol. 32, no. 5, pp. 600-12.

Piccini, A & Kershaw, B 2004, 'Practice as research in performance: from epistemology to evaluation', Digital Creativity, vol. 15, no. 2, pp. 86-92.

Picciotto, R 2017, Global development and human security, Routledge, London.

Picornell, I 2013, 'Analysing Deception in Written Witness Statements', Linguistic Evidence in Security, Law and Intelligence, vol. 1, no. 1, pp. 41-50.

Pirolli, P 2006, ‘Assisting people to become independent learners in the analysis of intelligence’, Final Technical Report, retrieved 9 January 2018, https://fas.org/irp/eprint/pirolli.pdf.

Platt, W 1957, Strategic Intelligence Production: Basic Principles, Frederick A. Praeger, New York.

Polanyi, J 2002, ‘Free Discovery from Outside Ties’, Ottawa Citizen, retrieved 23 August 2016, https://www.cautbulletin.ca/en_article.asp?articleid=1442.

Polanyi, M 1951, The logic of liberty, Routledge & Kegan Paul, London.

Polanyi, M 1952, 'The Stability Of Beliefs', The British Journal for the Philosophy of Science, vol. 3, no. 11, pp. pp. 217-32.

Polanyi, M 1957, 'Problem solving', The British Journal for the Philosophy of Science, vol. 8, no. 30, pp. 89-103.

Polanyi, M 1961, 'Faith and reason', The Journal of Religion, vol. 41, no. 4, pp. 237-47.

Polanyi, M 1962a, Personal Knowledge: Towards a Post-Critical Philosophy, Martino Publishing, USA.

Polanyi, M 1962b, 'The republic of science: Its political and economic theory', Minerva, vol. 38, no. 1, pp. pp. 1-21.

Polanyi, M 1962c, 'Tacit Knowing Its Bearing on Some Problems in Philosophy', Reviews of Modern Physics, vol. 34, no. 4, pp. 601-16.

Polanyi, M 1964, Science, faith and society, University of Chicago Press, Chicago, IL.

Polanyi, M 1966a, 'The logic of tacit inference', Philosophy, vol. 41, no. 155, pp. 1-18.

Polanyi, M 1966b, The Tacit Dimension, University of Chicago Press, Chicago, IL.

Polanyi, M 1969, Knowing and Being: Essays by Michael Polanyi, University of Chicago Press, Chicago, IL.

Popper, K 1959, The logic of scientific discovery, Routledge, London.

157

Popper, K 1966, Objective Knowledge A Realist View of Logic, Physics, and History, Clarendon Press, Oxford.

Posner, RA 2006, Uncertain Shield: The US Intelligence System in the Throes of Reform, Rowman & Littlefield, Lanham, MD.

Pringle Jr, RW 2015, 'The Cryptology Expert's Tales: Review of How I Discovered World War II's Greatest Spy, and Other Stories of Intelligence and Code by David Kahn’, International Journal of Intelligence and CounterIntelligence, vol. 28, no. 4, pp. 841-3.

Pritchard, MC & Goodman, MS 2008, 'Intelligence: The Loss of Innocence', International Journal of Intelligence and CounterIntelligence, vol. 22, no. 1, pp. 147-64.

Prosch, H & Polanyi, M 1975, Meaning, University of Chicago Press, Chicago.

Prunckun, H 2013, 'Intelligence and intelligence analysis', Police Practice and Research, vol. 14, no. 5, pp. 443-5.

Prunckun, H 2014, Scientific Methods of Inquiry for Intelligence Analysis, Rowman & Littlefield Publishers, Lanham, MD.

Prunckun, H 2015, Scientific methods of inquiry for intelligence analysis (Second Edition), Rowman & Littlefield Publishers, Lanham, MD.

Puvathingal, BJ & Hantula, DA 2012, 'Revisiting the psychology of intelligence analysis: from rational actors to adaptive thinkers', American Psychology, vol. 67, no. 3, pp. 199-210.

Quarmby, N & Young, LJ 2010, Managing Intelligence: The Art of Influence, Federation Press, Annandale, NSW.

Random, R 1958, 'Intelligence as a Science', Studies in Intelligence, vol. 2, no. 2, pp. 75-9.

Rasanen, M & Nyce, JM 2013, 'The Raw is Cooked: Data in Intelligence Practice', Science, Technology & Human Values, vol. 38, no. 5, pp. 655-77.

Ratcliffe J & McCullagh, M 2001, 'Chasing Ghosts? Police Perception of High Crime Areas', The British Journal of Criminology, vol. 41, pp. 330-41.

Ratcliffe, JH 2004, 'The structure of strategic thinking', in J Ratcliffe (ed), Strategic Thinking in Criminal Intelligence, Federation Press, Annandale, NSW, pp. 1-10.

Ratcliffe, JH 2014, 'Intelligence-led policing', in G Bruisma & D Weisburd (eds), Encyclopedia of criminology and criminal justice, Springer, Berlin, pp. 2573-81.

Ratcliffe, JH 2016, Intelligence-led policing, Routledge, London.

Ray, T 2008, 'Rethinking Michael Polanyi's Realism: From Personal Knowledge to Intersubjectively Viable Communication', Prometheus, vol. 26, no. 3, pp. 241- 57.

158

Reiner, R 2010, The politics of the police, Oxford University Press, Oxford.

Richards, HJ & Pherson, RH 2010, Structured analytic techniques for intelligence analysis, CQ Press, Washington DC.

Richards, J 2010, The art and science of intelligence analysis, Oxford University Press, Oxford.

Van Riper, PK 2006, Planning For and Applying Military Force: An Examination of Terms, Strategic Studies Institute, Carlisle, PA.

Ritchie, J, Lewis, J, Nicholls, CM & Ormston, R 2013, Qualitative research practice: A guide for social science students and researchers, Sage, London.

Rockefeller Commission, 1975, Report to the President by the Commission on CIA Activities Within the United States, Manor Books, New York.

Rodgers, RS 2006, 'Improving Analysis: Dealing with Information Processing Errors', International Journal of Intelligence and CounterIntelligence, vol. 19, no. 4, pp. 622-41.

Rosenbaum, JM 2007, 'Hunches: Too Much Discretion Not Enough Control Rational Hunches and Policing', Journal of Law, Economics & Policy, vol. 4, no. 1, pp. 107-114.

Rothberg, HN, Rothberg, HN, Erickson, GS & Erickson, GS 2017, 'Big data systems: knowledge transfer or intelligence insights?', Journal of Knowledge Management, vol. 21, no. 1, pp. 92-112.

Rubenstein, RE 2017, 'State Security, Human Security, and the Problem of Complementarity', in ED Jacob (ed), Rethinking Security in the Twenty-First Century, Springer, Berlin, pp. 225-43.

Rutledge, DW 2015, 'Who Was Michael Polanyi?: A Primer for Poteat Scholars', Tradition and Discovery: The Polanyi Society Periodical, vol. 42, no. 1, pp. 10-7.

Ryan, M 2006, 'Filling in the ‘unknowns’: Hypothesis-based intelligence and the Rumsfeld commission', Intelligence and National Security, vol. 21, no. 2, pp. 286- 315.

Ryan, P 2015, 'Positivism: paradigm or culture?', Policy Studies, vol. 36, no. 4, pp. 417-33.

Schaler, JA, Lothane, HZ & Vatz, RE 2017, Thomas S. Szasz: The Man and His Ideas, Routledge, London.

Scheffer, M, Baas, M & Bjordam, T 2017, 'Teaching originality? Common habits behind creative production in science and arts', Ecology and Society, vol. 22, no. 2, p. 29.

Schmitt, RH 2011-2012, 'The Personal and the Paradigmatic', Tradition & Discovery: The Polanyi Society Periodical, vol. XXXVIII, no. 2, pp. 15-20.

159

Schön, DA 1995, 'Knowing-In-Action: The New Scholarship Requires a New Epistemology', Change: The Magazine of Higher Learning, vol. 27, no. 6, pp. 27- 34.

Schum, D 1987, Evidence and Inference for the Intelligence Analyst, University Press of America, Lanham, MD.

Scott, LD 1985, Everyman revived: The common sense of Michael Polanyi, William B. Eerdmans Publishing, Grand Rapids, MI.

Scott, MS 2017, 'Reconciling problem-oriented policing and evidence-based policing', in J Knutsson & L Tompson, Advances in Evidence-Based Policing, Routledge, London, p. 27.

Seale, C 1999, 'Quality in qualitative research', Qualitative inquiry, vol. 5, no. 4, pp. 465-78.

Sennett, R., 2008.The craftsman. Yale University Press

Shaw, D 2007, 'Managing Analysis', paper presented to 8th Australian Information Warfare and Security Conference, Perth, Australia.

Shearing, C & Wood, J 2007, Imagining security, Willan, London.

Shelton, C 2011, 'The Roots of Analytic Failures in the U.S. Intelligence Community', International Journal of Intelligence and CounterIntelligence, vol. 24, no. 4, pp. 637-55.

Sheptycki, J 2009, 'Policing, intelligence theory and the new human security paradigm: Some lessons from the field', in P Gill, S Marrin & M Phythian (eds), Intelligence Theory: Key questions and debates, Routledge, USA, pp. 166-185.

Silver, N 2012, The signal and the noise: why so many predictions fail--but some don't, Penguin, New York.

Silverman, D 2011, Interpreting qualitative data: A guide to the principles of qualitative research, SAGE Publications Limited, London.

Silverman, EB 2007, 'With a Hunch and a Punch', Journal Of Law, Economics & Policy, vol. 4, no. 1, pp. 133-154.

Sinclair, RS 2010, 'Thinking and Writing: Cognitive Science and Intelligence Analysis', Center for the Study of Intelligence, retrieved 9 January 2018, https://www.cia.gov/library/center-for-the-study-of-intelligence/csi- publications/books-and-monographs/Thinking-and-Writing-Feb2010-web.pdf

Smedslund, J 2016, 'Practicing psychology without an empirical evidence-base: The bricoleur model', New Ideas in Psychology, vol. 43, pp. 50-6.

Smith, F & Ingram, G 2017, 'Organising cyber security in Australia and beyond', Australian Journal of International Affairs, vol. 71, no. 6, pp. 642-60.

Smith, MD 2017, 'A Good Intelligence Analyst', International Journal of Intelligence and CounterIntelligence, vol. 30, no. 1, pp. 181-5.

160

Smith, TJ 2014, 'Overlord/Bodyguard: Intelligence Failure through Adversary Deception', International Journal of Intelligence and CounterIntelligence, vol. 27, no. 3, pp. 550-68.

Sokal, AD 2001, 'Review of: Who Rules in Science?' retrieved 9 January 2018, http://www.physics.nyu.edu/sokal/review_of_brown_doublespace.pdf

Spielmann, K 2014, 'Using Enhanced Analytic Techniques for Threat Analysis: A Case Study Illustration', International Journal of Intelligence and CounterIntelligence, vol. 27, no. 1, pp. 132-55.

Steen, J & Kastelle, T 2012, 'Thinking, Fast and Slow: great for practitioners but not so great for academics', Prometheus, vol. 30, no. 4, pp. 465-7.

Stohl, M 2012, 'Don't confuse me with the facts: knowledge claims and terrorism', Critical Studies on Terrorism, vol. 5, no. 1, pp. 31-49.

Strang, KD & Sun, Z 2017, 'Analyzing Relationships in Terrorism Big Data Using Hadoop and Statistics', Journal of Computer Information Systems, vol. 57, no. 1, pp. 67-75.

Styhre, A 2008, Science-Based Innovation: From Modest Witnessing to Pipeline Thinking, Palgrave Macmillan, New York.

Tan, C 2014, 'Reflective thinking for intelligence analysis using a case study', Reflective Practice, vol. 15, no. 2, pp. 218-31.

Tang, J 2017, 'How do we know? What intelligence analysis can learn from the sociology of science', Intelligence and National Security, vol. 32, no. 5, pp. 663- 74.

Taylor, TZ, Elison-Bowers, P, Werth, E, Bell, E, Carbajal, J, Lamm, KB & Velazquez, E 2013, 'A Police Officer’s Tacit Knowledge Inventory (POTKI): establishing construct validity and exploring applications', Police Practice and Research, vol. 14, no. 6, pp. 478-90.

Tebble, AJ 2016, Epistemic liberalism: a defence, Routledge, London.

Tenet, G 2000, Tenet Dedicates New School for Intelligence Analysis, media release, 4 May, CIA, retrieved 9 Janaury 2018, https://www.cia.gov/news- information/press-releases-statements/press-release-archive- 2000/pr050400.html .

Terpstra, J 2017, '‘Not just one node among many’ – plural policing in a state- dominated context: the case of Austria', Policing and Society, vol. 27, no. 1, pp. 68-81.

Townsley, M 2017, 'Crime mapping and spatial analysis', in B LeClerc & E Savona (eds) Crime Prevention in the 21st Century, Springer, Berlin, pp. 101-12.

Townsley, M, Mann, M & Garrett, K 2011, 'The Missing Link of Crime Analysis: A Systematic Approach to Testing Competing Hypotheses', Policing, vol. 5, no. 2, pp. 158-71.

161

Treverton, GF 2009, Intelligence for an Age of Terror, Cambridge University Press, New York.

Treverton, GF & Gabbard, CB 2008, Assessing the tradecraft of intelligence analysis, RAND National Security Research Division, Santa Monica, CA.

Treverton, GF, Jones, SG, Boraz, S & Lipscy, P 2006, Toward a Theory of Intelligence. Workshop Report, RAND National Security Research Division, Santa Monica, CA.

Turner, S 2012, 'Making the Tacit Explicit', Journal for the Theory of Social Behaviour, vol. 42, no. 4, pp. 385-402.

Turner, S 2014, 'Tacit Knowledge Meets Analytic Kantianism', Tradition and Discovery: The Polanyi Society Periodical, vol. 41, no. 1, pp. 33-47.

Turpin, T, Garrett Jone, S & Rankin, N 1996, 'Bricoleurs and boundary riders: managing basic research and innovation knowledge networks', R&D Management, vol. 26, no. 3, pp. 267-82.

Tversky, A & Kahneman, D 1971, 'Belief in the law of small numbers', Psychological Bulletin, vol. 76, no. 2, p. 105.

Tversky, A & Kahneman, D 1973, 'Availability: A heuristic for judging frequency and probability', Cognitive psychology, vol. 5, no. 2, pp. 207-32.

Tversky, A & Kahneman, D 1975, 'Judgment under uncertainty: Heuristics and biases', in D Wendt & CA Vlek (eds) Utility, probability, and human decision making, Springer, Berlin, pp. 141-62.

United Nations Office on Drugs and Crime 2011, Criminal Intelligence Training: Manual for Analysts, United Nations Office on Drugs and Crime, Vienna.

United States Marine Corp, 2011 ‘21st‐Century Marine Expeditionary Intelligence Analysis: Modernizing Tactical Military Intelligence Analysis’, retrieved 19 January 2018, https://marinecorpsconceptsandprograms.com/programs/intelligence- surveillance-and-reconnaissance/21st-century-marine-expeditionary-intelligence Van Puyvelde, D, Coulthart, S & Hossain, MS 2017a, 'Beyond the buzzword: big data and national security decision-making', International Affairs, vol. 93, no. 6, pp. 1397-416.

Van Puyvelde, D, Coulthart, S & Hossain, S 2017b, 'National security relies more and more on dig data. Here's why', The Washington Post, September 27, retrieved 9 January 2018, https://www.washingtonpost.com/news/monkey- cage/wp/2017/09/27/national-security-relies-more-and-more-on-big-data-heres- why/?utm_term=.bcde78a4880e van Steden, R 2017, 'Municipal Law Enforcement Officers: towards a new system of local policing in the Netherlands?', Policing and Society, vol. 27, no. 1, pp. 40- 53.

162

Vandepeer, C 2011a, 'Intelligence analysis and threat assessment: towards a more comprehensive model of threat', Proceedings of the 4th Australian Security and Intelligence Conference, Edith Cowan University, Perth, WA, 5-7 December.

Vandepeer, C 2011b, 'Rethinking Threat: Intelligence Analysis, Intentions, Capabilities and the Challenge of Non-State Actors', PhD thesis, University of Adelaide.

Vandepeer, C 2014, Applied Thinking for Intelligence Analysis: a guide for practitioners, Air Power Development Centre, Department of Defence, Canberra.

Vandepeer, C 2016, 'Question-Asking in Intelligence Analysis: Competitive Advantage or Lost Opportunity?', ASPJ Africa & Francophonie, vol. 8, no. 1, pp. 24-43.

Varela, FJ, Thompson, E & Rosch, E 2017, The embodied mind: Cognitive science and human experience, MIT Press, Cambridge, MA.

Vogel, KM 2013a, 'Expert knowledge in intelligence assessments: bird flu and bioterrorism', International Security, vol. 38, no. 3, pp. 39-71.

Vogel, KM 2013b, 'The Need for Greater Multidisciplinary, Sociotechnical Analysis: The Bioweapons Case', Studies in Intelligence, vol. 57, no. 3, pp. 1-10.

Vogel, KM & Dennis, M 2012, The Role of Tacit Knowledge in WMD Proliferation: Past and Present, Wilson Center, Washington, https://www.wilsoncenter.org/event/the-role-tacit-knowledge-wmd-proliferation- past-and-present.

Von Krogh, G, Ichijo, K & Nonaka, I 2000, Enabling knowledge creation: How to unlock the mystery of tacit knowledge and release the power of innovation, Oxford University Press, Oxford.

Vrist Rønn, K 2014, '(Mis-) Informed Decisions? on Epistemic Reasonability of Intelligence Claims', International Journal of Intelligence and CounterIntelligence, vol. 27, no. 2, pp. 351-67.

Vrist Rønn, K & Høffding, S 2012, 'The Epistemic Status of Intelligence: An Epistemological Contribution to the Understanding of Intelligence', Intelligence and National Security, vol. 28, no. 5, pp. 694-716.

Wagemans, J 2013, 'Historical and conceptual background: Gestalt theory', in J Wagemans (ed), The Oxford Handbook of Perceptual Organization, Oxford University Press, Oxford, pp. 1-20.

Wagemans, J, Feldman, J, Gepshtein, S, Kimchi, R, Pomerantz, JR, van der Helm, PA & van Leeuwen, C 2012, 'A century of Gestalt psychology in visual perception: II. Conceptual and theoretical foundations', Psychological Bulletin, vol. 138, no. 6, pp. 1218-52.

Wahlert, MH 2012, 'The “motivated bias” dilemma in warfare and intelligence', Defense & Security Analysis, vol. 28, no. 3, pp. 247-59.

163

Wall, DS 2007, 'Policing Cybercrimes: Situating the Public Police in Networks of Security within Cyberspace', Police Practice and Research, vol. 8, no. 2, pp. 183- 205.

Walsh, P 2014, 'Building Better Intelligence Frameworks Through Effective Governance', International Journal of Intelligence and CounterIntelligence, no. 28, pp. 123–42.

Walsh, PF 2011, Intelligence and intelligence analysis, Routledge, London.

Walsh, PF 2012, 'Intelligence and Intelligence Analysis Exploring the State of Intelligence Since 9/11', PhD thesis, Charles Sturt University.

Walsh, PF 2017, 'Improving strategic intelligence analytical practice through qualitative social research', Intelligence and National Security, vol. 32, no. 5, pp. 548-62.

Walsh, WF 2001, 'Compstat: An analysis of an emerging police managerial paradigm', Policing: An International Journal of Police Strategies & Management, vol. 24, no. 3, pp. 347-62.

Waltz, E 2003, Knowledge Management in the Intelligence Enterprise, Artech House, Inc., Norwood, MA.

Waltz, E 2014, Quantitative Intelligence Analysis: Applied Analytic Models, Simulations, and Games, Rowman & Littlefield, Lanham, MD.

Warner, M 2009, 'Intelligence as risk shifting', in P Gill, S Marrin & M Phythian (eds), Intelligence Theory: Key questions and debates, Routledge, USA, pp. 16- 32.

Warner, M 2017, 'Analysis as history, and history as analysis: a search for common goals and standards', Intelligence and National Security, vol. 32, no. 5, pp. 613-24.

Wastell, C, Clark, G & Duncan, P 2006, 'Effective Intelligence Analysis: The Human Dimension', Journal of Policing, Intelligence and Counter Terrorism, vol. 1, no. 1, pp. 36-52.

Wastell, CA 2010, 'Cognitive Predispositions and Intelligence Analyst Reasoning', International Journal of Intelligence and CounterIntelligence, vol. 23, no. 3, pp. 449-60.

Weber, L, Fishwick, E & Marmo, M 2016, The Routledge International Handbook of Criminology and Human Rights, Routledge, London.

Weber, M 2017, Methodology of social sciences, Routledge, London.

Weiss, C 2007, 'Communicating Uncertainty in Intelligence and Other Professions', International Journal of Intelligence and CounterIntelligence, vol. 21, no. 1, pp. 57-85.

Westera, N, Kebbell, M, Milne, B & Green, T 2013, 'Developing the “effective detective” of the future', CEPS briefing paper, no. 21, vol. 2200-4149, Australian Research Council, Nathan, Queensland.

164

Wheaton, K 2011, 'Teaching Strategic Intelligence Through Games', International Journal of Intelligence and CounterIntelligence, vol. 24, no. 2, pp. 367-82.

Wheaton, KJ & Richey, m 2015, 'Wilhelm Agrell, Essence of Assessment: Methods and Problems of Intelligence Analysis', Intelligence and National Security, vol. 30, no. 4, pp. 609-11.

Wheeler, DL 2015, 'The Literature of Intelligence', Intelligencer: Journal of US Intelligence Studies, vol. 21, no. 1, pp. 67-73.

Whitney, CR 2005, The WMD Mirage: Iraq's Decade of Deception and America's False Premise for War: Featuring the Report to the President from the Commission on the Intelligence Capabilities of the United States Regarding Weapons of Mass Destruction, PublicAffairs, New York.

Williamson, K 2002, Research methods for students, academics and professionals: Information management and systems, Elsevier, Amsterdam.

Williamson, K, Schauder, D, Wright, S & Stockfeld, L 2002, 'Oxymoron or a successful mixed marriage? A discussion of the use of action research within an interpretivist framework', Qualitative Research Journal, vol. 2, no.1, pp. 7-17.

Willis, JW & Jost, M 2007, Foundations of qualitative research: Interpretive and critical approaches, Sage, London.

Wing, I 2000, 'The characteristic of successful and unsuccessful intelligence analysts', Journal of the Australian Institute of Professional Intelligence Officers, vol. 9, no. 2, p. 4.

Winner, M 2012, 'Raphael's School of Athens', The Power of Images in Early Modern Science, p. 289.

Wippl, JW 2014, 'The Future of American Espionage', International Journal of Intelligence and CounterIntelligence, vol. 27, no. 2, pp. 387-96.

Wirtz, JJ 2012, 'The Science of Artful Analysis', International Journal of Intelligence and CounterIntelligence, vol. 25, no. 2, pp. 418-20.

Wirtz, JJ 2014a, 'The Art of the Intelligence Autopsy', Intelligence and National Security, vol. 29, no. 1, pp. 1-18.

Wirtz, JJ 2014b, 'Doing Intelligence in the Classroom', International Journal of Intelligence and CounterIntelligence, vol. 27, no. 2, pp. 425-7.

Wolhstetter, R 1962, Pearl Harbor: Warning and Decision, Stanford University Press, Stanford.

Wood, D, Cockcroft, T, Tong, S & Bryant, R 2017, 'The importance of context and cognitive agency in developing police knowledge: going beyond the police science discourse', The Police Journal. https://doi.org/10.1177/0032258X17696101

Wordsworth, W., 2000. The major works. Oxford University Press, USA.

165

Wright, D 2016, 'Personal Knowledge Transformed: James Loder’s neo- Chalcedonian science of practical theology’, Tradition & Discovery: The journal of the Polanyi society, vol. 42, no. 2, pp. 34-51.

Xinping, Z 2002, 'Interpretivist Research, Positivist Research, and Field Research', Chinese Education & Society, vol. 35, no. 2, pp. 39-46.

Ylikoski, P 2017, 'The illusion of depth of understanding in science', in H de Regt, S Leonelli & K Egner (eds), Scientific Understanding: Philosophical Perspectives. University of Pittsburgh Press, Pittsburgh, PA, pp. 100-119.

Zafeirakopoulos, M 2014, 'Institutionalising imagination in the intelligence process', Journal of the Australian Institute of Professional Intelligence Officers, vol. 22, no. 3, p. 23.

Zammito, JH 2004, A Nice Derangement of Epistemes: Post-positivism in the Study of Science from Quine to Latour, University of Chicago Press, Chicago.

Zegart, AB 2009, Spying Blind: The CIA, the FBI, and the Origins of 9/11, Princeton University Press, USA.

Zohar, E 2013, 'Intelligence Analysis as a Manifestation of a Grounded Theory', International Journal of Intelligence and CounterIntelligence, vol. 26, no. 1, pp. 130-60.

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Appendix 1: Email from Glenn Carle

Date: 22.06.16

Dear Owen,

Thanks for your note. I am happy that you have read, and seem to have appreciated, my book. How did it come to your attention? Why read it (although you are right to have done so!) in particular, given your focus of study? It is a factual story, one of relative and limiting and skewed perceptions, of paradigms, of leadership and its pitfalls, of terrorism, of organizational “enclosed realities…” But, there is a lot there, that few perceive, frankly, or care about. That is okay, in a way (sort of): the tale of torture, error, terrorism, and moral, public, and personal duty is enough for people to wrestle with, and each of those issues is important in itself.

Polanyi? I know of him only through his relatively minor involvement, as I recall, in the Manhattan Project; or, more accurately, in Britain’s nuclear efforts prior to the US taking over the principle effort. But, his influence on Kuhn and Hayek is key! Kuhn is in the pantheon of seminal influences on my life; along with Montesquieu, Thucydides, Machiavelli, Locke, Tocqueville, and Aron.

As you may have noted, I sought to write on a number of levels: Of course, there is the straightforward narrative, the derring do tale of torture, terrorism, and error. Most will remain there, and that is important and okay. But I also sought to convey how perceptions become a paradigm, that the paradigm is held in many ways unconsciously, and that it defines "truth" and bounds perception, so that reality is actually external to the paradigm. I do hold that there is an objective reality, or truth; but also that the truth can be multiple, as it depends for many aspects of existence on obligatory choice. If one does not choose, all knowledge is meaningless and unintelligible - inchoate series of facts. There must be a narrative thread to perception, lest society and individual life be impossible, and completely nihilistic. Yet, our minds and perceptions, too, are subject to the Heisenberg Principle: by analyzing, by becoming aware, by observing and defining through senses and thought, anything, we limit it. In so doing, reality and life become real and not just an inchoate, undefined potential reality, but in defining they are limited – certainly the perceptions and beliefs we have are thereby limited. And so, we are trapped in a partial reality; but only by, in a sense, becoming conscious of ever more partial realities, all based on choice and therefore perspective, can one approach – surely never achieve – comprehensive understanding and objective truth.

Most people don’t care about this and find it high-falutin, pretentious, and meaningless over-intellectualization of life, and our daily tasks. In most instances, most people are right; and I am a dreamer; and we all know dreams

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are not reality…whereas we must live here on earth, in reality. But, that obliges us, if we do not surrender, to seek reality. We know it is multiple.

More precisely to your questions: It was not files full of SIGINT that said CAPTUS was a danger. The issue has nothing to do with HUMINT, SIGINT, or other INT. It was all-source intelligence: years of human, technical, analytical assessments. The issue was one of consciousness, perception, paradigms of thought, institutional orthodoxy. Recall what I wrote in the paragraph above: “perceptions become a paradigm, that the paradigm is held in many ways unconsciously, and that it defines "truth" and bounds perception, so that reality is actually external to the paradigm.” There was a mountain of reporting about CAPTUS, years of work by many officers. The counter-terrorism center’s assessment was that he was a bad guy, and a critically important and senior one. That was the expert, reasoned view; therefore it was the reference point of truth, to be disproven, perhaps, but the truth. In general, facts that did not confirm the conviction were taken to be evidence of error in thinking or fact. Think of me as Galileo, and the Counterterrorism Center as the Church. Think of all of Kuhn’s work, as described in The Structure of Scientific Revolutions. What happens when someone challenges orthodoxy? I am explicit about this point in my book: one is branded an apostate. One burns apostates at the stake. Facts are not even secondary. Facts are dangerous to “truth,” as it has been defined. People believe the paradigms and are threatened by any challenge to orthodoxy. Challenging beliefs is challenging truth. Challenging truth is to err. Ergo, anything not orthodox is in error. What? Radar reports of incoming aircraft, at 6:00 AM on December 7, 1941? Well, no planes should be flying at that hour, so therefore the report must be erroneous. What? Multiple reports of German troops crossing the border of the Soviet Union, the morning of June 25, 1941? But we have a treaty with Germany. The reports cannot therefore be accurate. What? Reports of German armor in the Ardennes the morning of May 10, 1940? But we know the Ardennes are impassable to massed armor. Our experts say so. The reports, therefore, are ignorant and wrong…

I challenged orthodoxy. I was not part of the inside expert clergy, one of the guild of terrorism experts. What I reported, assessed, and argued, would undo years of work, the lives and reputations of dozens, even hundreds of officers, of the entire institution – of one of the touted signal successes and triumphs in the War on Terror. That simply cannot be accepted; for the entire edifice would be revealed as fraudulent and wrong. Deluded. In particular, my assessments challenged the priesthood, and in particular one priestess, the high priestess of our counterterrorism paradigm; the woman known to the world as “Maya,” from Zero Dark Thirty. I did not describe this element adequately in my book, for a number of reasons. She alone, more than Wilmington, vetoed my actions and assessments. Because they undid all her work, and the entire edifice of the War on Terror. Voilà. That’s it. That’s why my assessments – which I knew then and we all know now are 100%, without exception, accurate. Except…even still, the clergy, Maya, the CIA counterterrorism guild, refuse, sincerely refuse, to accept

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my perspective. They do not believe me; but they see themselves as arguing facts. It is as Keynes said: theory progresses, funeral by funeral….

What use will you make of this? What is the title of your thesis? When will you finish it? What will you do with it? What do you intend to do with yourself? How did I – my book – come to your attention? What other sources, resources, avenues are you pursuing for your thesis?

Best,

Glenn Carle

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Appendix 2: Email from Philip Mudd

Date: 7.06.17

Hi Owen,

I am not a huge fan of structured techniques, with some basic exceptions. One would be ensuring a structured conversation about questions -- something I outlined in the book. The other is ensuring analysts are systematic about what they believe is important to assess before they study a problem. For example, what do you need to know to assess a terror group? I see lots of analysis that focuses on sets of variables that are too small. "XYZ terror group staged two attacks last week; they're on the rise." That's bad analysis. I would also push for structure on adding simple statistical measures to some analysis. A lot of analysis will look at a bit of data and draw sweeping conclusions -- "Assad's military lost two towns this week; they're slipping." Analysts should ensure they ask a simple question before they go down this road: what sample size of military activity do we need to see before we can make an assessment of how the military is doing? Surely, that sample size has got to be more than one week's worth of losses. Philip

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