Integrating Musiasem and the Ecosystem Approach to Health

Integrating MuSIASEM and The Ecosystem Approach to Health; Systems Theory, Quantitative Storytelling, and Participatory Methods for Promoting Human and Ecosystem Health

David Mallery

Date Submitted: July 25th, 2016

A Major Paper submitted to the Faculty of Environmental Studies in partial fulfillment of the requirements for the degree of Master in Environmental Studies

York University, Toronto, Ontario, Canada

______David Mallery Martin Bunch MES Candidate MES Major Paper Supervisor Abstract

Increasingly, Issues relating to coupled human and ecosystem health manifest in complex problems without simple solutions. Systems oriented methodologies, such as The Ecohealth Approach and Multi-scale integrated analysis of societal and ecosystem metabolism, are required to grapple with the uncertainty and non-linear behavior of complex systems relevant to sustainability and human wellbeing. This paper proposes that these methodologies should be theoretically and methodologically integrated for the purpose of collaboratively developing interventions for promoting human and ecosystem health within a complex decision space. Section 1 presents a thorough disucssion of the concept of health, within the discourse of complex systems theory and ecological economics, and proposes a general framework for understanding the health of autopoietic systems. Section 2 provides a review of the methods and principles employed by MuSIASEM and The Ecohealth Approach while discussing how and why these methodologies should be integrated.

Foreword

Prior to entering the MES program, I became interested in the work of theoretical biologists, Ray Peat, Gilbert Ling and Mae Wan Ho. Peat's view, that energy manifests structure and structure, in turn, determines the path of energy, along with Ling's observation, that life is fundamentally a "high energy-low entropy state" (Ling 1992), stood out in my mind at the start of my masters studies. I began to wonder if these concepts could be extended to higher levels of organisation and whether they could provide some insights into sustainability. With a bit of research, I was happy to discover that these ideas were directly related to the discourse on societal and ecological metabolism. The germinal concept for this paper was, therefore, formulated early in the course of my MES studies, and my plan of study was specifically designed so that I would acquire the knowledge and expertise necessary for writing it.

From the beginning, I set out with the goal of gaining an equal competence in both theory and practical application of concepts from systems science and ecological economics, the fields in which the metabolism discourse primarily takes place. These subjects would become the components of my area of concentration and I enrolled in the relevant courses that FES offered. While these courses were highly informative and helpful in contextualizing my understanding, the theorists and researchers who interested me the most were those who operated at the intersection between the two epistemological traditions. Thus, a great deal of my time in MES was spent in independent directed studies and reading courses concerning more specialized areas of research. Each of the papers I wrote for these courses contributed to sections of the paper I have written here.

I was immediately impressed by the work of Mario Giampietro and Kozo Mayumi, and familiarizing myself with their MuSIASEM methodology was the purpose of my first reading course. I was intrigued by the theoretical narrative they adopted, which, for me, represented the ideal synthesis of bioeconomics and systems theory. I determined then that I MuSIASEM was a methodology I intended to practice and the work of Giampietro and Mayumi would provide a roadmap for the subjects I would cover later.

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In researching MuSIASEM, I was also delighted to discover that Giampietro’s theoretical framework is largely grounded the work of the relational biologist and complexity theorist, Robert Rosen, who happens (in seemingly cosmic coincidence) to have been the grandfather of a dear childhood friend of mine. With the help of Rosen’s daughter, Judith, I designed another reading course around the field of relational theory. As part of the requirements to fulfil these studies, I attended the 2015 meeting of the International Society for the Systems Sciences in Berlin, Germany. In yet another coincidence, it was there that I met my soon-to-be MES supervisor, Martin Bunch, who introduced me to the work of his mentor, the theoretical ecologist, James Kay. In the summer and fall terms of 2015, I undertook two more independent courses, focusing on Kay’s work in systems ecology and resilience thinking. I started to understand how all of the theoretical concepts I had studied were interconnected and I began to see a very elegant pattern “emerge.” A working understanding of this pattern was what I had hoped to gain in MES and it is, in part, the subject of this paper.

Through research as part of a graduate assistanceship with the Credit Valley Conservation Authority, I also began to take an interest in Martin’s own work and the methodology that he practices: The Ecohealth Approach. Recognizing that MuSIASEM and Ecohealth were grounded in similar theoretical frameworks, I reasoned that the biophysical accounting scheme of MuSIASEM could be complementary to the participatory methods of Ecohealth and vice versa. In fulfillment of one of my learning objectives, I attended the 2016, MuSIASEM summer school at the Institut de Ciència i Tecnologia Ambientals (ICTA), a department of the Autonomous University of Barcelona, Spain, where I was encouraged to discover many other researchers who shared my views.

Originally, I had intended this paper to be a case study of the Credit Valley Watershed using the two methodologies I had been studying. Once I realized how overly ambitious this would be within the timeframe of MES, I decided instead to present a synthesis of the methodologies and theories I had researched over the past two years which could be operationalized at a later point in my academic career. This work, therefore, became the start of something rather than an end. It represents not only the fulfilment of the requirements for my MES degree, but also a starting basis for future research.

Acknowledgements

My time in MES has been a journey of intellectual and personal growth which never could have been attempted without the support of many people. The beginning of my studies coincided with the onset of a sometimes debilitating, chronic illness that continues to disrupt the normal routines of my life. The course has been difficult and I owe a great debt to the friends, colleagues and loved ones who supported me during this time. This paper, in part, will argue that the future of human society will require that we learn to do more with much less. If the past two years have taught me anything, it is that mutualism, conviviality, and compassion make attainable those goals which would be otherwise impossible due to adversity and limited resources. I am forever grateful to the people who have shown me that.

To my parents, Dominique Lepoutre, Marshall Smith and Bob Mallery, for their infinite love and kindness. They are responsible for instilling in me a love of knowledge and a respect for the natural world. I’ll always remember where that comes from.

To my partner, my best friend and my love, Meaghan McElwain, for encouraging and inspiring me. I could never have done this without her.

To my uncle, Dennis Baldocchi, who in many ways started me on this path.

To my second family, the Rosens, who are nothing less than an endless font of brilliance and fun. To Rachel, my dear friend, and quite possibly the world’s foremost conversationalist, and Judith, who opened my eyes to the world of complexity while guiding me toward an understanding of her father’s work.

I am very grateful to all of the members of FES who have provided an engaging, supportive and inclusive intellectual community:

Thanks to my supervisor, Martin Bunch, who is the most patient, insightful and generous mentor I could have hoped for.

To Eric Miller, who I found to be an outstanding professor of ecological economics.

To my advisor, Peter Mulvihill, for encouraging these ideas early on.

Also, thanks to my classmate, Alvero Palazuelos, with whom I shared many engaging conversations.

Special thanks to the many academics and researchers who have shared their own insights as this work progressed:

To Mario Giampietro and his team at the ICTA, including Violetta Cabello, Zora Kovacic, Alevgül Sorman and Samuele Lo Piano. Their week-long seminar in MuSIASEM was an enjoyable and informative experience that helped me to work out any misconceptions I had about their methodology.

Thanks to the folks at Systems Thinking Ontario, whose meetings I always look forward to. To David Ing, for sharing an earlier version of this paper on social media, and Allenna Leonard, for sharing her incomparable knowledge of cybernetics.

To the members of the Relational Science SIG at the ISSS: Janet and Michael Singer, John Vodonick, John Kineman, Ariel Leonard and Amber Elkins, who warmly welcomed me to their group. Also thanks to Katharine Farrell, for sharing her considerable insights while showing us around Berlin.

To Arran Gare, who reached out to express an interest in my research, while offering some helpful resources I may never have encountered on my own.

Finally, thanks to Raymond Peat, whose correspondence has given me a great deal to think about.

Table of Contents

Abstract ...... ii Foreword ...... iii Acknowledgements ...... v Table of Contents ...... vii Table of Figures ...... ix List of Abbreviations ...... xi Introduction ...... 1 Section 1: Theory ...... 5 1.1: Self-organization ...... 6 1.2: Open systems and Non-Equilibrium Thermodynamics...... 10 1.3: Living Systems ...... 17 1.4: Autocatalysis ...... 24 1.5: Panarchy and Complex Adaptive Systems ...... 30 1.6: Societal Metabolism ...... 35 1.7: Energy Gain and Energy Return on Investment ...... 44 1.7: Health Within the Systems Narrative...... 49 1.8 The Modelling Relation and the M-R system ...... 54 1.9: Monocultures of Mind and Models ...... 61 Section 2: Methodologies ...... 67 2.1: The Ecosystem Approach to Health ...... 70 2.2: Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) ..... 75 2.2.1 Fund-Flow Analysis in MuSIASEM ...... 75 2.2.2: Hierarchies of Societal Compartments ...... 78 2.2.3: Multi-Purpose Grammars ...... 81 2.2.4: Intensive and Extensive Variables ...... 84 2.2.5: Impredicative Loop Analysis ...... 86 2.2.6: The Sudoku Effect ...... 89 2.2.7: Bioeconomic Pressure as an Indicator of Societal Desirability ...... 90 2.2.8: Feasibility and Ecological Indicators of Desirability ...... 92

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2.3: Land systems, ecosystem services and coupled human and ecosystem health ...... 96 3: Conclusions ...... 103 References ...... 105

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Table of Figures

Figure 1: Order, mixing, and dispersal of material objects as measures of material entropy...... 9 Figure 2: Bénard cells as an example of self-organization in open systems...... 12 Figure 3: An illustration of a catastrophe cusp in three dimensions...... 21 Figure 4: Development along a thermodynamic path (upper left); retreat to an earlier optimum operating point along the same thermodynamic path (lower left); retreat, bifurcation, and development along a different thermodynamic path (upper right); catastrophic “flip” to another thermodynamic branch (lower right)...... 22 Figure 5: Example of a schematic network of energy exchanges between trophic compartments in the Cone Spring ecosystem...... 25 Figure 6: Autocatalytic selection causing element B to be replaced by the more effective element, D...... 27 Figure 7: Cartoon depicting the effects of autocatalysis as an inchoate set of connections is replaced by a dominant autocatalytic loop...... 28 Figure 8: Adaptive renewal cycle (Panarchy) in two dimensions...... 31 Figure 9: Panarchy in three dimensions, including resilience...... 32 Figure 10: the circular flow of income model from neoclassical economics ...... 41 Figure 11: The ecological economic model, in which the circular flow of neoclassical economics is situated within a finite, biophysical context...... 42 Figure 12: A cartoon illustrating the concept of energy return on investment ...... 46 Figure 13: The “Hubbert Curve” with added illustrations of cavemen to illustrate both the concept of Peak Oil as well as the Olduvai Theory ...... 47 Figure 14: Rosen’s Modelling Relation ...... 54 Figure 15: The parable of blind men attempting o describe an elephant...... 57 Figure 16: Category mapping of Rosen's M-R system. Source: Rosen 1985...... 58 Figure 17: The Diamond Heuristic depicting the general process for an Ecosystem Approach based on complex systems thinking and collaborative processes...... 72 Figure 18: Waltner-Toews’ Adaptive Methodology for Ecosystem Sustainability and Health (AMESH)...... 73

Figure 19: Examples of flows and funds – Simple “cow grammar” ...... 77 Figure 20: Hierarchy of societal system components...... 79 Figure 21: Example dendrogram of Societal Funds and Flows...... 81 Figure 22: Example of a food grammar...... 82 Figure 23: Example of an Energy Grammar...... 84 Figure 24: Dendrogram depicting disaggregated societal compartments with extensive fund- shares and intensive flow-rates...... 85 Figure 25: An example of impredicative loop analysis depicting a shortfall in the cereal production necessary to operate a small farm...... 87 Figure 26: Multi-level matrix depicting the Sudoku Effect ...... 90 Figure 27: LU/LC scenario from Mauritius case study. Vector colors represent the cubic water requirements (CWR) of different crops and land uses...... 94 Figure 28: Ecosystem services as providing the constituents of well-being...... 97 Figure 29: Diagram depicting the impredicative relationship between three nested holons: ecosystem metabolism, societal metabolism, and human health...... 100 Figure 30: Example of a multi-objective integrated representation...... 102

List of Abbreviations

AG agricultural sector AMESH Adaptive Methodology for Ecosystem Sustainability and Health BEP bioeconomic pressure BM building and manufacturing sector CATWOE customers, actors, transformation, worldview, owners, environmental constraints CWR cubic water requirement EC energy carrier ELP economic labour productivity EM energy and mining sector EMR exosomatic metabolic rate EROI energy return on investment ET energy throughput GDP gross domestic product GIS geographic information system GPP gross primary productivity HA human activity HEIA high-external-input agriculture HH household sector ILA impredicative loop analysis LEIA low-external-input agriculture LU/LC land use/ land cover MEA Millennium Ecosystem Assessment ML managed land MuSIASEM Multi-Scale Integrated Analysis of Societal and Ecosysystem Metabolism M-R metabolism and repair NEC negentropic cost NPP net primary production NET non-equilibrium thermodynamics PAWF plant active water flow

PC power capacity PES primary energy source PF primary flows sector PS primary and secondary production sectors PW paid work sector SB standing biomass SDM system dynamics modeling SES socio-ecological system SG services and government sector SOHO self-organizing, holarchic, open system SSM soft systems methodology TET total energy throughput TFT total food throughput TWT total water throughput THA total human activity WS whole society

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Introduction

Human health and wellbeing are fundamentally reliant upon the integrity of our ecological contexts (Millenium Ecosystem Assessment 2005). Increasingly, issues relating to the coupled relationship between human and ecological health, such as environmental degradation, economic inequality and resource scarcity, coalesce into non-linear, complex problems characterized by high levels of uncertainty (Bunch and Waltner-toews 2015; Charron 2012). Reductionist disciplinary science has proven limited in addressing these problems, while traditional resource management, disease management, economic and environmental policy have at times yielded perverse, destructive outcomes (Waltner-Toews 2001; Waltner-Toews and Kay 2005). There is a growing recognition that navigating these challenges will require a new breed of practical methodologies informed by complex systems theory (Funtowicz and Ravetz 1993; Kay et al. 1999). The Ecosystem Approach to Health and Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) are two such methodologies. This paper will argue that these approaches should be theoretically and methodologically integrated and together applied for the purpose of collaboratively developing interventions for promoting human and ecosystem health within a complex decision space.

While MuSIASEM and The Ecohealth Approach share common theoretical underpinnings, they each offer a unique and complimentary set of tools for these purposes. In general, the field of EcoHealth is concerned with “research, practice and knowledge integration at the interface of ecology and health” (International Association for Ecology and Health 2013, pg. ii). The Ecohealth Approach refers to the application of a set of systems based principles and heuristics, from an ecosystem approach developed by Kay et al. (1999), to promoting interventions oriented toward realizing desirable and sustainable outcomes in situations of uncertainty and complexity (Bunch and Waltner-toews 2015). Adopting the process depicted in Kay’s “diamond diagram,” The Ecohealth Approach emphasizes a participatory framework for 1) the development of socioecological systems descriptions and issues frameworks; 2) scenario writing, in which ecological possibilities are discerned, and socio-cultural preferences are negotiated in order to discern feasible and desirable futures; 3) promotion of desirable

scenarios through targeted interventions; and 4) ongoing adaptive management through governance, monitoring, and implementation strategies.

MuSIASEM is described as “quantitative storytelling” for resource accounting and sustainability assessment (Chifari et al. 2016). In essence, it is a systems based diagnostic and simulation tool designed to assess scenarios, in primarily biophysical terms, within multiple non-equivalent descriptive domains (Giampietro 2003; Giampietro and Mayumi 2000; Giampietro et al. 2011; 2013; 2014). Like the Ecohealth Approach, MuSIASEM is concerned with the feasibility and desirability of possible futures. However, whereas Ecohealth focuses on describing the dynamics of socioecological systems, MuSIASEM describes separate but interrelated patterns of “metabolism” governing the self-organization of societies and ecosystems respectively. This departure allows for quantitative assessment of societal constraints which further impact the viability space of proposed scenarios. Thus, MuSIASEM is a potentially useful extension to the diamond heuristic as both societal and ecological possibility filter. The purpose of the second half of this paper is to discuss how a methodological partnership between the Ecohealth Approach and MuSIASEM might function.

First, however, Section 1 will review and expand upon the established theory informing these methodologies. In many ways, the theoretical narratives adopted by Kay and Waltner-Toews (2003) and Giampietro (2003) are strikingly similar. This should come as no surprise as these researchers1 were friends and colleagues whose mutual influences are made readily apparent. Their respective methodologies adopt concepts from theories including but not limited to: general systems theory (von Bertalanffy 1968); complexity theory (Rosen 1985; 1991); post- normal science (Funtowicz and Ravetz 1993); hierarchy theory (Allen and Hoekstra 1992; Koestler 1968); non-equilibrium thermodynamics and self-organization (Prigogine and Stengers 1984; O’Connor 1991); ecosystem health and integrity (Kay 1991; Kay and Schneider 1992; Schneider and Kay 1994a; 1994b; Kay et al. 1999); systems ecology (Odum 1971; Odum and Odum 1976; Ulanowicz 1986; 1997; Jørgensen et al. 2007); autopoiesis and cognition

1 The so-called “Dirk Gently Gang” - named for a character in Douglas Adams’ Dirk Gently’s Holistic Detective Agency – was an informal association consisting of James Kay, David Waltner-Toews, Jerry Ravetz, Silvio Funtawitz, Mario Giampietro, Gilberto Gallapin, Bruna De Marchi, Tamsyn Murray, Henry Reiger and Martin O’Connor.

(Maturana and Varela 1980) and; complex adaptive systems and resilience thinking (Gunderson and Holling 2002; Holling 1973; Berkes, Folke, and Colding 2002). MuSIASEM goes further in operationalizing concepts from: bioeconomics (Georgescu-Roegen 1971; 1975); ecological economics (Daly 1973; Boulding 1966); anthropological studies of collapse within complex societies (Tainter 1988; 1995); and systems ecology, including energy return on investment (Hall, Lambert, and Balogh 2014; Lambert et al. 2014).

Section 1 consists of a literature review that will cover these subjects in depth. In doing so, an attempt will be made to reconcile any minor, interpretative differences between these two approaches. For example, one of MuSIASEM’s unique features is the primacy of the concept of metabolism, which Giampietro defines as “the property of systems using biophysical flows to organize themselves” (Madrid-López and Giampietro 2015). Through this lens, metabolism can be viewed as an isomorphism, general to self-organizing systems, rather than a metaphor. In redefining metabolism as such, Giampietro avoids the fraught territory associated with the suggestion that ecosystems and societies are organisms or even “superorganisms” akin to Lovelock’s Gaia Hypothesis (Lovelock 1972). The reconceptualization of metabolism does, however, create an opportunity for the re-interpretation of the very concept of health itself from within a systems framework. Currently, the application of the concepts of “health” or “well-being” to non-human systems (such as societies and ecosystems) is both common and problematic, leading to unnecessary discord between systems thinkers and political ecologists due to its functionalist connotations. By invoking Rosen’s concept of metabolism and repair (M- R systems), this work will attempt to develop a more general and potentially useful systems interpretation of health can be more generally applied to ecosystems and societies as isomorphism rather than metaphor. Following in the epistemological tradition of Rosen, theorists in an emerging field of relational science, such as Don Mikulecky, John Kineman, and Judith Rosen, provide additional instructive insights (Mikulecky and Coffman 2012; J. Rosen 2009; Kineman 2011).

As a final note on the literature review: the depth and implications associated with the discussed concepts are considerable. It will become clear that these methodologies fall within a theoretical discourse that grapples, among other things, with the possible collapse of industrial

society as well as the nature and origins of life itself. In many ways, systems theory occurs at the intersection between science and philosophy. For readers more familiar with one or the other, some concepts may appear unconventional, to say the very least. In reference to the 1999 film, The Matrix, the opening chapter of Giampietro et al.’s, The Metabolic Pattern of Societies (2011), is appropriately entitled, “The red pill”, which symbolizes for them “the possibility of getting a fresh view of something previously perceived in a different way from within a well consolidated framework”. Here it will be argued that given the complexity of the challenges we face, “fresh views,” in the form of alternative or excluded perspectives, are perhaps our most precious resource. In attempting to alter the alternative herein, we are not only entering the rabbit hole, but we are also going to leave Wonderland a bit stranger than it already was. It is hoped that this paper will provide some meaningful contribution in this regard.

Section 1: Theory

A good way to introduce the notion of complexity is to invoke the saying, “the whole is greater than the sum of its parts.” Although occasionally attributed to Aristotle or Hegel, the phrase is more likely an anonymous idiom which reflects a truism that humans have observed for millennia: that many if not most natural phenomena cannot be adequately understood by reducing them to their smallest material constituents and the observing forces which act upon them. In essence, this is what we are doing in the Newtonian-Baconian-Cartesian tradition of western science. While spectacularly effective in describing the complicated interplay of objects, mechanisms, and forces, reductionism is limited when encountering complex systems. Complex problems arise when interactions between complex systems become problematic. The very notion of complexity is, therefore, subsumed within our understanding of “systemhood,” and the theoretical explanations we adopt will necessarily orient approaches designed to navigate the challenges of complexity.

MuSIASEM and the Ecosystem Approach for Health are two operationalized expressions of a similar theoretical narrative rooted in the systems sciences. Both methods conceptualize societal and ecological systems as complex, thermodynamically open, dissipative systems that self-organize through non-linear, autocatalytic feedback loops, operating across multiple, hierarchically nested temporal and spatial scales, the boundaries and behaviors of which are impossible to determine within any single descriptive domain (Kay et al. 1999; Giampietro 2003). The first goal of this paper is to explain what exactly is meant by that. The following section will review the body of relevant literature to provide the reader with a cursory understanding of the systems concepts and theories that inform the methodologies discussed in section 2.

1.1: Self-organization

Self-organization, referring to the spontaneous emergence of organized structure from disorderly conditions, is a characteristic of all complex systems. Although first proposed by V.I. Vernadsky, it is Erwin Schrödinger, in his highly influential work, What is Life? (1947), who is most commonly credited with the idea that the emergence of “order from disorder” is a unique and counter-intuitive thermodynamic phenomenon. The Nobel laureate, Ilya Prigogine, of the Brussels School of Thermodynamics, would later apply this germinal idea to the study of non- equilibrium thermodynamics. To understand the phenomenon of self-organization, it is therefore necessary to first review some of the basic principles of thermodynamics which are relevant to future discussion.

Thermodynamics is the scientific study of heat in relation to energy and work. The word "energy" is a Greek compound, coined in Aristotle’s Metaphysics, joining εν (in) and έργον (work, deed, action), meaning "actuality, identified with movement" (Smil 2006, pg. 1). Following Aristotle, the term came to be associated with "motion, action, work and change." Although, as the great physicist, Richard Feynman, famously declared, "we have no knowledge of what energy is," we can say, with respect to our experience of natural phenomena, that we understand what energy does (Feynman, Leighton, and Sands 1963). In general, energy is understood simply as the capacity of a system to do work, which Glucina and Mayumi discuss as follows:

"In physics, “work” (W) has the precise meaning of “motion against an opposing force.” Thus, lifting a weight against gravity, accelerating a car against air resistance, or forcing a flow of electrons through an electrically resistive material are all examples of “doing work. “Heat” (Q) has the precise meaning of the “transfer of energy caused by a difference in temperature.” Temperature itself is a measure of the average motion of molecules, which includes three kinds of motion; translational, rotational, and vibrational. Heat and work are therefore two different modes of energy transfer" (Glucina and Mayumi 2010, pg. 12).

The quality of energy, as it undergoes transformations from one form to another, and the potential that energy has to "do work" within a system is the specific purview of thermodynamics. The First Law of classical thermodynamics, an adaptation of the law of conservation of energy, implies that the energy of the universe is constant2. Energy "cannot be created or destroyed. Thus, the workings of the universe can be viewed as a series of energy transformations" (Glucina and Mayumi 2010, pg. 13) The Second Law is an extension of two statements made in early research on the economy of steam engines: 1) ‘No cyclic process is possible in which heat is taken from a hot source and converted completely into work’ (the Kelvin statement), and 2) "Heat does not pass from a body at low temperature to one at high temperature without an accompanying change elsewhere’ (the Clausius statement)." Taken together, these two statements establish the impossibility of perpetual motion. It is possible to convert one hundred percent of the energy in a system to heat, but the same does not hold true for work. In the conversion of energy to work, there is always a loss of efficiency to heat which is too "dilute" to perform further work. Thus, the efficiency of a system can never be absolute. Entropy is irreversible and always increasing at any temperature above absolute zero. As work is performed, the "degradation" of energy quality (the dissipation of heat) due to entropy is accelerated; although entropy is always acting on any energy or material at all times. Ultimately, entropic degradation moves thermodynamic systems toward a state of equilibrium, or "maximum entropy", in which all flows of energy and matter cease and the system attains the same temperature as the surrounding environment.

There are three types of thermodynamic systems: open systems, in which matter, heat, and work can traverse the system boundary; closed systems, in which energy can traverse the boundary but matter cannot; and isolated systems, in which neither energy or matter can traverse the boundary. The "internal energy" (U) of a system refers to the amount of energy within a system. Because the first law states that energy is conserved, losses in the capacity to do work are transferred, in the form of heat, to the surroundings in open or closed systems. This is expressed in the equation: U = Q W, meaning that the change in the Universe is

2 The term “universe” in thermodynamicsΔ refers to −the "system" in question and the "surroundings" (i.e. the environment within which the system operates.) The point of demarcation which divides the system and the surroundings is referred to as the "system boundary”.

equal to heat (Q) minus work (W). The “quality of energy”, referring to the potential of energy within a system to do work, is expressed in terms of "entropy" (S). "Low entropy" refers to energy with a high capacity to do work and "high entropy" indicates the reverse. "Exergy", a related term, refers to" the maximum amount of work that can be produced by a stream of matter, heat or work as the medium comes into equilibrium with a reference environment ... or into equilibrium with the surrounding environment" (Stremke, Dobbelsteen, and Koh 2011, pg. 151) The "grade" of an energy type is defined as the ratio of exergy to entropy. Electricity, for example, is considered to be a high-exergy, low-entropy energy source with an energy grade of 1.0, whereas the energy grade of geothermal hot water can be as low as .009.

Entropy itself is an elusive and somewhat controversial concept which is often described in many disputed ways. The convention of referring to entropy as a measure of disorder, for example, is frequently contested in the physical sciences. The “statistical” reading of entropy referred to within the field of statistical mechanics (a branch of quantum mechanics) holds that "atoms can only exist in a discrete number of energy levels, and at any given instant all the atoms in a system must be distributed in some way among these levels… A system with a relatively high number of possible distributions is said to have high entropy, and likewise, a smaller number of possible distributions corresponds to lower entropy” (Glucina & Mayumi, 2010). For many theorists, the higher the number of possible distributions of atoms within a system is an indicator of increased levels of "internal disorder", and therefore higher entropy. The statistical reading of entropy further allows for the extension of the concept to the realm of information theory, whereby ordered information (e.g. letters organized into words on a page) is considered to contain lower entropy than disorderly information (e.g. letters in a word jumble). In some interpretations, the entropy concept has even been applied to the disordering of material objects. As and example, Stremke et al. argue that the material entropy of mixed or dispersed objects (such as objects in a landfill) is necessarily higher than objects which are unmixed and spatially concentrated.

Figure 1: Order, mixing, and dispersal of material objects as measures of material entropy. Source: Stremke, Dobbelsteen, and Koh 2011

The Second Law is one of the most important statements made in the history of science and has profound implications for our understanding of complex systems and self-organizing processes. The next section will discuss how the concept of entropy has evolved over time and how these

concepts have been applied to an understanding of the phenomenon of self-organization.

1.2: Open systems and Non-Equilibrium Thermodynamics

As entropy is irreversibly increasing, the tendency of all things to move toward thermodynamic equilibrium can be interpreted to mean "the natural tendency of things to go over into disorder" (Schrödinger 1947, pg. 68). According to classical thermodynamics, the universe is predicted to ultimately achieve a “heat-death” state in which all motion ceases as a result of total entropic decay. If all things tend towards increasing disorder, how then is it possible that we can observe self-organizing, self-ordering patterns in natural processes? By extension, how is it possible that living systems exist at all? Schrodinger observed that living things “resist entropy,” which is to say that they are able to maintain a “far from equilibrium” state despite the mandate of the second law that the entropy of all closed systems will tend toward maximum.

"What is the characteristic feature of life? When is a piece of matter said to be alive? When it goes on 'doing something', moving, exchanging material with its environment, and so forth, and that for a much longer period than we would expect of an inanimate piece of matter to 'keep going' under similar circumstances. When a system that is not alive is isolated or placed in a uniform environment, all motion usually comes to a standstill very soon as a result of various kinds of friction; differences of electric or chemical potential are equalized, substances which tend to form a chemical compound do so, temperature becomes uniform by heat conduction. After that, the whole system fades away into a dead, inert lump of matter. A permanent state is reached, in which no observable events occur... It is by avoiding the rapid decay into the inert state of 'equilibrium' that an organism appears so enigmatic... How does the living organism avoid decay? The obvious answer is: By eating, drinking, breathing and (in the case of plants) assimilating. The technical term is metabolism" (Schrödinger 1947)

Schrodinger provided an initial explanation for the “order from chaos” phenomenon in keeping with the inviolable second law by suggesting that living things import “negentropy” (i.e.

10 negative entropy) in an ongoing battle to resist entropic decay. The concept of negentropy briefly came to be associated with the theory of “syntropy” proposed by Albert Szent- Györgyi (1974), the Hungarian biochemist who was awarded the Nobel Prize for his discovery of ascorbic acid. Syntropy, Szent-Györgyi claimed, was the symmetrical counterpart to entropy, which allowed for the increasing complexification of ordered structure in natural phenomena; the drive for “living matter to perfect itself”. Invariably, both concepts fell out of favour as Von Bertalanffy and Prigogine soon after came to recognize that the spontaneous appearance of “order from disorder” is unique to open systems.

“However, we find systems which by their very nature and definition are not closed systems. Every living organism is essentially an open system. It maintains itself in a continuous inflow and outflow, a building up and breaking down of components, never being, so long as it is alive, in a state of chemical and thermodynamic equilibrium by maintained in a so-called steady state which is distinct from the latter. This is the very essence of that fundamental phenomenon of life which is called metabolism, the chemical processes within living cells. What now? Obviously, the conventional formulations of physics are, in principle, inapplicable to the living organism qua open systems and steady state, and we may well suspect that many characteristics of living systems which are paradoxical in view of the laws of physics are a consequence of this fact” (von Bertalanffy 1968, pg. 39)

The first meaningful investigation into the nature of thermodynamically open, self-organizing systems was conducted by Ilya Prigogine, a Russian expatriate physicist and chemist, operating in the 1960s and 70s out of the Free University of Brussels. Like Schrodinger, Prigogine was fascinated by the apparent exceptionalism of life and its ability to persist in stable, far-from- equilibrium states. Prigogine sought to understand the physical conditions necessary for non- equilibrium systems to achieve stability. In his early pursuits, Prigogine discovered that far from equilibrium systems can only adequately be described through non-linear equations. Whereas linear equations produce outputs which are consistent with inputs, non-linear equations do not. There is no “solution” to non-linearity in the conventional sense, rather, non-linear equations produce multiple values describing possible equilibria. In contrast to the observations

of classical thermodynamics, Prigogine’s discovery suggested that the behavior of systems in far from equilibrium conditions could not be understood as deterministically tending toward one absolute state. To unravel this mystery, Prigogine investigated a phenomenon known as Bénard instability in which a remarkably uniform pattern of hexagonal cells appear within thin horizontal layers of liquid when an even vertical temperature gradient is applied. As forces (i.e. the temperature gradient) increase, organized networks of hexagonal cells, known as Bénard Cells (figure 2), appear at critical instability thresholds as the flux of heat conduction is replaced with orderly convection currents (Prigogine and Stengers 1984, pg.142). Prigogine and his colleagues also studied oscillating reactions, so-called “chemical clocks,” in which solutions containing two types of differently coloured molecules will spontaneously change colour and back again at regular intervals. In both cases, the surprising effects require millions of molecules to move coherently in such a way that a chemical system behaves as an organized whole.

Figure 2: Bénard cells as an example of self-organization in open systems.

Prigogine theorized that the organized structures, such as Bénard Cells, that emerge in these reactions cause the dissipation of heat flow, and therefore entropy production, of the system to

increase; thus, Prigogine dubbed them “dissipative structures.” Closing the system or removing the thermal gradient, in these experiments, will cause the hexagonal structures to collapse. Likewise, forcing too much exergy and pushing a self-organizing system beyond a certain threshold will overwhelm the system, causing it to become incoherent. There is a “zone of vitality”, explains Robert Ulanowicz, in which self-organizing processes can occur (Ulanowicz 1986). The entropy that is exported into the surrounding environment offsets the motion within the system away from thermodynamic equilibrium. In other words, “the more organized system”, according to Schneider and Sagan, “is also better at producing waste” (Schneider and Sagan 2005, pg. 113). In this way, the Second Law is satisfied, and it is not necessary to conceive of new, mysterious forces, such as syntropy, to account for the appearance of organized structure in random conditions.

Most strikingly, Prigogine noted that the spontaneous formation of organized structures seemed paradoxical in light of Boltzmann’s order principle which suggests that the probability of the emergence of dissipative structures is infinitesimally small. However, these phenomena occur all the same. For Prigogine, the trend of thermodynamic systems, described in classical thermodynamics and statistical mechanics, to “forget” their initial starting conditions (i.e. their histories) seemed to be irrelevant when observing open systems at far from equilibrium conditions.

“The fate of the fluctuations perturbing a chemical system, as well as the kinds of new situations to which it may evolve, thus depend on the detailed mechanism of the chemical reactions. In contrast with close-to-equilibrium situations, the behavior of a far-from-equilibrium system becomes highly specific. There is no longer any universally valid law from which the overall behavior of the system can be deduced. Each system is a separate case; each set of chemical reactions must be investigated and may well produce a qualitatively different behavior” (Prigogine and Stengers 1984, pg. 144-145)

Like a “rolling snowball” (Schneider and Sagan 2005), Bénard cells exhibit tiny random fluctuations of microscopic convection currents which are amplified by autocatalytic positive

feedback cycles. In this way, fluctuations in small macrostates grow exponentially and “invade the system.” Manfred Eigen, observing positive feedback patterns in biochemical reactions, coined the term “hypercycles” to describe them (Eigen 1971). Elsewhere they are described as “catalytic loops”, “autocatalytic loops” or “autocatalytic cycles (Prigogine and Stengers 1984; Odum 1971; Ulanowicz 1986). Generally speaking, positive feedback in closed systems is “pathological” for ordered structures as the exponential growth will cause the system to exit the “zone of vitality” and lose coherence. In the words of Robert Ulanowicz, “they just blow up” (Ulanowicz 1986). Eigen’s hypercycles, however, prove not only remarkably stable but also apparently capable of growth, reproduction, self-correction, and self-instruction (Schneider and Sagan 2005).

Open systems in non-equilibrium conditions achieve this stability through the emergence of coupled negative feedbacks that attenuate autocatalytic cycles and allow for the dissipation of excess energy. In effect, these coupled positive and negative feedbacks are what we are observing in the stability of Bénard Cells. The cyclical interplay between negative and positive feedbacks creates a stable pattern of self-organized behavior which can be mapped to within an area of state space known as an attractor. Once such a semi-stable state is achieved, the system will tend to remain within the vicinity of an attractor until the system is closed or sufficiently perturbed. While the system remains open to the importation of energy, material, and information, the system also becomes organizationally closed as the regime of organization around a particular attractor allows us to discern boundaries within which the system itself can be said to be autonomous and “whole.” Intriguingly, Glansdorff and Prigogine (1973) observed that these “whole” systems are even capable of growing in complexity to point that discrete “whole” subsystems, each with their own boundaries, attractors, and feedbacks, can be seen to interact across various scalar intervals.

Prigogine is therefore often cited as having been a pioneer in what is called “hierarchy theory”, which holds that complex, self-organizing systems appear to be “hierarchically” organized in such a way that higher level systems “constrain and control” subsystems, while subsystem components provide functions that recreate the higher level over time (Allen and Starr 1982). The words “appear to be” are crucial here. Allen and Ahl define hierarchy theory as “a theory of

the observer’s role in any study of complex systems” (Allen and Ahl 1996, pg. 29) and they argue that the task of defining boundaries of systems and subsystems to be an inherently subjective one. Stanley Salthe, another prominent hierarchy theorist, further explains:

“I am not simply asserting that the world is hierarchical any more than I would assert that perfect circles occur in material nature. Circles and hierarchies are structures, that is, organizational principles through which nature can be understood. Even though all actual hierarchies are heterarchic, that is a consequence of the fact that theory is neat, while the world is messy. Because of this, one does not find hierarchies lying about in the world; one constructs nature hierarchically – because it is useful to do so” (Salthe 1993, pg. 35)

Hierarchy theorists use the term, “holon,” in reference to “the Janus-faced holon” coined by Arthur Koestler in his book, The Ghost in the Machine (1968), to describe nested levels of organization. A holon, according to Koestler, is a constituent component of a hierarchy which is itself a whole with its own constituent components. As will be discussed later, hierarchy theory and the holon concept are frequently invoked in discussions within the discourse of socioecoogical systems.

As a final note on self-organization: although syntropy was discarded and negentropy came to be understood essentially analogous to exergy, the process of self-organization remains no less mysterious. Prigogine, while successful in demystifying the role of entropy in self-organizing processes, was unable to determine precisely the threshold points at which a system, moving along a given thermodynamic path, would “bifurcate” (split off) and begin moving along a new thermodynamic branch through the emergence of dissipative structures. Further, the direction of bifurcations appears to be impossible to predict. “There is an irreducibly random element” notes Prigogine, “the macroscopic equation cannot predict the path the system will take. Turning to a microscopic description will not help. There is also no distinction between left and right. We are faced with chance events very similar to the fall of dice” (Prigogine and Stengers 1984, pg. 168). Bifurcation in these events, therefore, appear as emergent phenomena and the future states of self-organizing thermodynamic systems are uncertain. Famously, Einstein once

15 declared, in the spirit of Laplace, that “God does not play dice.” Prigogine claimed to have discovered otherwise. Open systems, it seems, do not behave deterministically. It is for this reason that non-equilibrium thermodynamics and self-organization are considered by many to be the physical basis for irreducible uncertainty and “emergence” in complex systems. As we will see, this mystery manifests in higher levels of organization and has considerable implications for ecosystems and societies alike. The following sections will discuss how observations from non-equilibrium thermodynamics are applied to an understanding of phenomena at higher levels of scale.

1.3: Living Systems

Fritjof Capra, in reference to Prigogine’s investigations of self-organization in nonliving systems, remarks: “the lesson to be learned here seems to be that the roots of life reach down into the realm of nonliving matter” (Capra 1996, pg. 94). Indeed, both Prigogine and Eigen believed that they were observing the prerequisite processes which define living things (Prigogine and Stengers 1984; Eigen 1971). Kay and Schneider, in their influential article, Life as a Manifestation of the Second Law (1994), would later expand upon these observations in an attempt to synthesize a non-equilibrium thermodynamic explanation for the phenomenon of “living systems”, such as organisms and even ecosystems themselves. Pointing to Keenan and Hatsopoulos’ “Law of Stable Equilibrium” as well as Kestin’s “Unified Principle of Thermodynamics”, Kay and Schneider rejected the classical definition of the Second Law as the law of “entropy increase” in which thermodynamic equilibrium is conceived as the sole attractor. Instead, they adopted the position that any number of local semi-stable attractor states are possible in far from equilibrium situations. “When moved away from their local equilibrium state,” they argue “[thermodynamic systems] shift their state in a way which opposes the applied gradients and moves the system back towards its local equilibrium attractor” (Kay and Schneider 1994, pg.33). To illustrate this point, Kay and Schneider proposed a restated Second Law as follows:

“The thermodynamic principle which governs the behavior of systems is that, as they are moved away from equilibrium they will utilize all avenues available to counter applied gradients. As the applied gradients increase, so does the system’s ability to oppose further movement from equilibrium” (Schneider and Kay 1994, pg. 29).

There are a few points that should be made with respect to this restatement. First, the restated second law disposes of entropy as a state variable which is “only defined for equilibrium” and instead focuses on the dissipation of energetic gradients as the central feature of the second law. This allows us to replace the imperative, “entropy must increase,” with the simpler notion that “nature abhors a gradient” (Schneider and Sagan 2005). In other words, energy will

dissipate along the most efficient local pathways, even if that path serves to delay the increase of entropy within the whole system. The distinction serves to establish that self-organizing processes are logically entailed by the Restated Second Law, rather than being problematic as is the case in the classical Second Law. Self-organizing systems, in this view, can be seen as dissipative systems which are characterized by their ability to break down applied gradients on their boundaries. The corollary is that dissipative systems exist only so long as gradients are applied. Because these systems break down the very gradients which animate them, they typically manifest in short-lived vorticial phenomena such as hurricanes, whirlpools or tornadoes (Kay and Schneider 1994; Schneider and Sagan 2005). Life, according to Schneider and Kay, is a type of dissipative system that “exists on earth as another means of dissipating the solar induced gradient and, as such, is a manifestation of the restated second law” (Kay and Schneider 1994, pg. 36). Not unlike Vernadsky’s description of life as a “whirlwind of atoms,” Kay and Schneider suggested that life develops and evolves into increasingly more sophisticated configurations to “oppose” the energetic stress of applied gradients.

Life with its requisite ability to reproduce, ensures that these dissipative pathways continue, and it has evolved strategies to maintain these dissipative structures in the face of a fluctuating physical environment. We suggest that living systems are dynamic dissipative systems with encoded memories, the gene with its DNA, that allow the dissipative processes to continue without having to restart the dissipative process via stochastic events. Living systems are sophisticated mini-tornados, with a memory (its DNA), whose Aristotelian “final cause” may be the second law of thermodynamics. However, one should be clear not to overstate the role of thermodynamics in living processes. The restated second law is a necessary but not a sufficient condition for life (Schneider and Kay 1994, pg. 36, emphasis added).

By invoking the concept of Aristotelian “final cause”, Kay and Schneider radically suggested that the most basic telos (teleological purpose) for life is to dissipate energetic gradients. However, the last sentence contains a significant qualifying statement. The thermodynamic basis is “necessary but not sufficient” for explaining life, and gradient dissipation is not the only imperative driving ecosystem phenomenology. Rather, they suggest that ecosystem

organization “represents a tradeoff between the imperatives of survival and the second law” (Kay and Schneider 1992, pg. 159). This distinction will be elaborated below.

Kay and Schneider were not the first to investigate the association between energy flow and biological growth. As early as 1922, Alfred Lotka proposed the “Law of Maximum Energy” which held that evolutionary survival would favour biological systems with the highest energy output relative to their size (Lotka 1925). The “Maximum Power Principle”, proposed by Howard T. Odum and Richard C. Pinkerton, would later adjust Lotka’s concept, stating: “during self- organization, system designs develop and prevail that maximize power intake, energy transformation, and those uses that reinforce production and efficiency”(Odum and Pinkerton 1955; Odum 1995, pg. 311, emphasis added), i.e., within living systems, the maximum power principle is taken to mean that organisms will evolve to maximize power intake, rather than output. Kay and Schneider offer a third explanation: change in biological systems is driven by the imperative to increase the rate of dissipation and thereby degrade energetic gradients. Thus, the gradient dissipation imperative, which subsumes Lotka and Odum’s “power principles,” is the driving force behind growth, development, and evolution in living systems.

“growth occurs when the system adds more of the same types of pathways for degrading imposed gradients… development occurs when new types of pathways for degrading imposed gradients emerge in the system. The larger the system, i.e., the larger the system flow activity, the more reactions and pathways (both in number and type) are available for gradient destruction. Clearly, the above principle provides a criteria for evaluating growth and development in living systems. All else being equal, the better dissipative pathway is preferred” (Schneider and Kay 1994, pg. 37).

Through this lens, ecosystems themselves can be views as “the result of the biotic, physical and chemical components of nature acting together as a nonequilibrium dissipative process” (Schneider and Kay 1994, pg. 37). As evidence, Kay and Schneider observed that leaf index assemblies in forest canopies arrange in such a way that optimizes energy capture and degradation. High biodiversity increases the possible pathways for energy dissipation. Highly

biodiverse forests, for example, are strongly correlated with higher evapotranspiration rates than agricultural monoculture despite relative levels of standing biomass. Tropic levels and food chains create “higher ordered structures” which further increases dissipation. Finally, Kay and Schneider point out that species diversity and trophic levels are greater at the equator, where solar radiation (i.e. energetic gradients) are significantly higher and that equatorial rainforests are cooler than their surrounding environments, as degraded energy is dissipated outward (Kay and Schneider 1994).

In recognizing that ecosystems exhibit “preferences” towards efficient dissipative pathways, Kay and Schneider proposed that their theory represents an “underlying principle in ecology” which could be potentially instrumental in transforming the field from a descriptive to predictive science (Schneider and Kay 1994). Their hypothesis holds that ecosystems develop in a successional process of increasing dissipative capacity, each stage resulting in 1) increased energy capture; 2) increased energy flow activity; 3) Increased cycling of energy and materials; 4) “higher average trophic structure”, meaning that food chains will be longer, and species on average will occupy higher trophic levels; 5) increased respiration and evapotranspiration; 6) increased biomass; and 7) higher biodiversity (Schneider and Kay 1994, pg. 38). By associating a progressively increasing dissipation rate with these variables, Kay and Schneider claimed that the former could be understood as a suitable indicator of “ecosystem integrity and health” (Kay and Schneider 1994). The higher the gradient imposed on an ecosystem, the more pathways will be grown and developed in response. For Kay and Schneider, the more capable an ecosystem becomes in dissipating energy, the “healthier” it supposedly is.

Kay (1991) explains that the successional developmental process is not gradual or “smooth.” Rather, it is characterized by spurts of growth or sudden precipitous drops. The addition of more (or invention of new) dissipative structures occurs when environmental conditions alter certain key state variables to the point that they cross a “catastrophe cusp”, i.e. an unstable segment of state space along an equilibrium path. Here, state space refers to “a space whose axes are the state variables”. The term, “State variable”, refers to a variable that is important for understanding or describing the system. The path in state space that a system follows as it develops is referred to as the “thermodynamic branch” (Kay 1991, see figure 4). The point in

which disorganizing environmental forces and organizing thermodynamic forces are in balanced is referred to as an “optimum operating point”. Integrity is seen as the ability of a system to maintain its positions around such points, while constantly striving to develop and “attain” optimum operating points at higher levels of the thermodynamic branch.

Figure 3: An illustration of a catastrophe cusp in three dimensions. Source: Kay 1991

Ecosystems, when stressed or perturbed, can “retreat” to configurations which represent earlier successional stages in their development. In thermodynamic terms, this means that ecosystems can reorganize around lower, semi-stable equilibrium attractors (optimum operating points) along the same thermodynamic branch (figure 4, lower left). Alternatively, ecosystems can retreat to an earlier bifurcation point and subsequently begin reorganizing toward another attractor along a different thermodynamic branch (figure 4 upper right). In extreme cases, a system which is forced away from its optimum operating point will catastrophically collapse and reorganize around a new attractor along an entirely different thermodynamic branch (figure 4, lower right). As an example of the latter case, Kay and Reiger point to the “death of Lake Erie” in the 1960s, in which the nutrient loading as a result of fertilizer runoff led to widespread eutrophication. Past a certain threshold, the increased

21 turbidity caused the aquatic ecosystem of Lake Erie to “flip” from a pelagic to a benthic state (Kay 2000).

Figure 4: Development along a thermodynamic path (upper left); retreat to an earlier optimum operating point along the same thermodynamic path (lower left); retreat, bifurcation, and development along a different thermodynamic path (upper right); catastrophic “flip” to another thermodynamic branch (lower right). Source: Kay 1991. Another possibility is that the system will resist change altogether; responding to perturbations by initially moving away from, but later returning to, the original optimum operating attractor. In these cases, the system is said to be exhibiting “resilience,” which refers to a system’s capacity to maintain its position around an attractor. Here, resilience is measured by “the minimum disturbance necessary to disrupt the system and cause it to move to a new equilibrium state” (Kay 1991). Highly resilient systems can withstand larger disturbances and will recover (i.e. return to their original attractor) more quickly. It is interesting to ask; why are some systems more resilient than others and what exactly makes them so. These questions have been considered by Peterson, Holling and Gunderson, who argue that the resilience of a system is a function of the diversity and adaptively of its components. In order to understand

22 how these factors fit into a larger systems narrative, however, it is necessary to examine the thermodynamic perspective more extensively.

1.4: Autocatalysis

If Kay and Schneider provided the “why” (the final cause) of ecosystem growth and development, it could be said that systems ecologists, such as Howard T. Odum, Sven Jorgensen, Robert Ulanowicz and others, provided the “how” with network thermodynamic descriptions of ecosystem flows. Odum (1956, 1971), not unlike Schrodinger, postulated that these network configurations constituted “metabolic networks self-organizing through informed autocatalytic loops” (Odum 1971). That is to say; ecosystems themselves can be considered similar to organisms in that they exhibit patterns which could be considered “metabolic” as they import high-quality energy and materials, while exporting low-quality waste products. As a starting premise, systems ecologists explicitly argue that the flow patterns observable in thermodynamic systems can be generalized in such a way that we can similarly understand any number of complex systems.

“to thermodynamically describe am an ecosystem, it is sufficient to quantify the underlying networks of material and energy flows. A more general form of the postulate would read: the network of flows of energy and materials provide a sufficient description of far from equilibrium systems” (Ulanowicz 1986, pg. 30)

This generalized postulate also forms the theoretical basis for the many approaches to flow- network models of ecosystems and human societies that began with Odum. However, this sub- section will focus on the specific postulate with respect to ecosystems.

As with Prigogine’s Bénard cells and Eigen's hypercycles, the growth and development of ecosystems are driven by the formation of autocatalytic positive feedback loops accompanied by buffering negative feedbacks. Instead of observing the motion of chemical species, systems ecologists use thermodynamic network modeling to quantify and map the cyclical exchange of energy, materials, nutrients and information through trophic levels, food-webs and migratory patterns in ecosystems. In this sense, systems ecologists view specific functional components in ecosystems (e.g. organisms or species) as behaving like catalysts in chemical reactions. In Figure 5, an example provided by Ulanowicz, nodes (numbered boxes) representing different trophic compartments in the Cone Spring ecosystem are connected by linkages (arrows) representing

energy flows measured in kcal/m/y. The “earth ground” ( ) symbol in the diagram represents the dissipation of energy which attenuates the positive feedbacks and allows the system to persist. Within this example alone, a mere four distinct positive feedback loops can be discerned: 2-3-4-5-2, 2-3-4-2, 2-4-5-2, 2-4-2 and 2-3-2 (Ulanowicz 1986, pg. 31). However, network thermodynamic analyses of energy and material flows in ecosystems can be immensely elaborate.

Figure 5: Example of a schematic network of energy exchanges between trophic compartments in the Cone Spring ecosystem. Source: Ulanowicz 1986.

Much like Kay and Schneider, many systems ecologists maintain that the growth and development of ecosystems implies that they are predisposed to directionality due to the propensity of autocatalytic cycles in general to grow. Robert Ulanowicz, in particular, argues that autocatalytic cycles, experiencing perturbations and random events from their contextual environments, select components which will ensure the continual growth of the system. Conceiving first of a three-node (A, B, and C) network linked by positive flows, Jorgensen et al. describe the selective process as follows:

“autocatalysis is capable of exerting selection pressure on its own, ever-changing, malleable constituents. To see this, one considers a small spontaneous change in process B. If that change either makes B more sensitive to A or a more effective

catalyst of C, then the transition will receive enhanced stimulus from A… Conversely, if the change in B makes it either less sensitive to the effects of A or a weaker catalyst of C, then that perturbation will likely receive diminished support from A. That is to say the response of this causal circuit is not entirely symmetric, and out of this asymmetry emerges a direction. This direction is not imparted or cued by any externality; its action resides wholly internal to the system. As one might expect from a causal circuit, the result is to a degree tautologous- autocatalytic systems respond to random events over time in such a way as to increase the degree of autocatalysis… To see how another very important directionality can emerge in living systems, one notes in particular that any change in B is likely to involve a change in the amounts of material and energy that are required to sustain process B. As a corollary to selection pressure we immediately recognize the tendency to reward and support any changes that serve to bring ever more resources into B. Because this circumstance pertains to any and all members of the causal circuit, any autocatalytic cycle becomes the epi-center of a centripetal flow of resources toward which as many resources as possible will converge… That is, an autocatalytic loop defines itself as the focus of centripetal flows. One sees didactic example of such centripetality in coral reef communities, which by their considerable synergistic activities draw a richness of nutrients out of a desert-like and relatively inactive surrounding sea. Centripetality is obviously related to the more commonly recognized attribute of system growth” (Jørgensen et al. 2007, pg. 65, emphasis added).

This “chemical imperialism” exhibited by autocatalytic loops is argued to be a driving force behind Darwinian natural selection as well. As multiple loops existing in the same system “compete” for the same resources, the dominant loop is likely to be that which is a more effective catalyst for other connected system elements. This is illustrated in figure 6:

“Whenever two loops share pathway segments in common the result of this competition is likely to be the exclusion or radical diminution of one of the non- overlapping sections. For example, should a new element D happen to appear and

to connect with A and C in parallel to their connections with B, then if D is more sensitive to A and/or a better catalyst of C, the ensuing dynamics should favor D over B to the extent that B will either fade into the background or disappear altogether” (Jørgensen et al. 2007, pg. 66).

Figure 6: Autocatalytic selection causing element B to be replaced by the more effective element, D. Source: Jørgensen et al. 2007.

Ecosystems, therefore, impose constraints, through autocatalytic selection and centripetality, that “displace more scattered interactions” in favour of processes and components that facilitate “only those pathways that result in greater autocatalytic activities” (Jørgensen et al. 2007, pg. 68). In other words, the trophic feedback loops which most effectively grow the ecosystem will also come to dominate and displace less potent loops. What is interesting about this proposition is that it implies that higher level systems do not passively provide the context within which evolution occurs. Rather, the ecosystem can be considered a more active participant in determining how species evolve. This is illustrated in figure 7, in which a cartoon depicts an immature system with a higher number of effective flows (a) developing into a mature system with fewer flows but “greater overall activity” (b), (Jørgensen et al. 2007, 68), which translates into a higher total system throughput of energy (TST). To quantify these forces in tandem, Ulanowicz has proposed the index, “System Ascendency,” referring to a measure of both autocatalysis and the constraint extant of a system. “It follows as a phenomenological principle,” he argues “in the absence of major perturbations, ecosystems have a propensity to increase in ascendency” (Jørgensen et al. 2007; Ulanowicz 1986). Ascendency, like ecosystem integrity, is therefore considered a measure of ecosystem health.

Figure 7: Cartoon depicting the effects of autocatalysis as an inchoate set of connections is replaced by a dominant autocatalytic loop. Source: Jørgensen et al. 2007.

However, although autocatalysis allows for increases in the overall size and energy throughput of an ecosystem, it also results in the systems becoming increasingly constrained. Successful catalytic loops which survive the selection process will tend to be more differentiated within specialized ecological “niches” that work together to maintain the stability of the ecosystem. Within a competitive environment characterized by increasingly scarce resources, certain “paths” of energy and nutrient flow will be necessarily more developed at the expense of others. This manifests in the elimination of approximate redundancies, the emergence of dominant species, and the loss of functional diversity, i.e.: “the value and range of those species and organismal traits that influence ecosystem functioning” (Tilman 2001). Thus, ecosystems can become more narrowly dependant on a small set of functions and resources despite high biodiversity in general (Ulanowicz 1997; Jørgensen et al. 2007). The gradual loss of functional diversity, in part, explains the fragility of highly developed ecosystems.

Coffman and Mikulecky contend that autocatalytic selection also accounts for the long observed phenomenon of r/k selection in ecosystem development (Mikulecky and Coffman 2012). According to r/K selection theory, immature ecosystems are characterized by a preponderance of versatile species that reproduce rapidly. Examples of r-strategists include what we might call “pest” species, such as rats, cockroaches or dandelions. These species move quickly and will be the first to colonize new territory, or recolonize old territory after a catastrophic disturbance. As the ecosystem matures and becomes more crowded, K-strategist species come to dominate. These species include larger animals with low birthrates and high gestation times. Examples include humans, whales, elephants or redwood trees (Pianka 1970). While K-strategists are more effective in exploiting scarce resources, they are also ironically more prone to extinction due to ecosystem disturbances. Specialization, while advantageous in competitive contexts, is a liability when one is required to be adaptive. The next section will, therefore, discuss the literature on complex adaptive systems.

1.5: Panarchy and Complex Adaptive Systems

The concept of “adaptive resilience” was initially proposed by C.S. “Buzz” Holling (1973) and would later become a central feature of Gunderson and Holling’s theory of Panarchy, which is meant to serve as a general theory of adaptation, change and persistence in “complex adaptive systems” such as ecosystems and socioeconomic systems alike (Gunderson and Holling 2002). Berkes, Folke, and Colding (2002) describe resilience as referring to 1) “the amount of change the system can undergo and still retain the same controls on function and structure, or still be in the same state, within the same domain of attraction”; 2) “the degree to which the system is capable of self-organization”; and, 3) “the ability to build and increase the capacity for learning and adaptation”. Within the theory of Panarchy, resilience is treated as an abstract state variable along with “connectedness” (i.e. the level of interdependence between system components) and “potential” (i.e. stored material and energy “wealth”). The changing relative values of these variables is argued by resilience thinkers to drive a universal pattern of growth, collapse and adaptive renewal within complex adaptive systems. These cycles, referred to as “Panarchies”, consist of four phases: exploitation (r), conservation (K), release (Ω), and reorganization (α) (Gunderson and Holling 2002).

The exploitation (r) phase of the adaptive cycle is named for the fast moving r-strategists that colonize new territories or recolonize ecosystems after catastrophic disturbances. In terms of socioeconomic systems, the r-phase is analogous to new emerging markets and periods of entrepreneurial innovation (e.g., the internet tech bubble). Competition in this phase is necessarily low at first due to the high availability of resources and low population proportional to the environmental carrying capacity. Systems emerging under these conditions will experience high environmental variability and failures will occur regularly. However, the indeterminacy of the environment allows for high adaptivity as contingency pathways are still available. Over time these pathways (e.g. ecological niches or markets) will be increasingly exploited as populations rise and the system becomes more constrained.

The exploitation phase is followed by the conservation (K) phase at which point the growth rate ultimately plateaus due to increasing competition for resources. During this time, the maximum

population is achieved, and the system exhibits high stability as the system begins to mitigate environmental variability. This occurs in ecosystems as vegetation regulates temperature and climate. Markets in the K-phase become less volatile as investors gain confidence and trust are fostered between firms. K-strategists predominate through the formation of tightly coupled, mutually supportive relationships. While high competition and connectedness create less opportunity (and hence less innovation and novelty), the new synergy between system components allows for efficient consolidation of resources along increasingly narrow pathways. In turn, a dominant, exclusive regime emerges that creates vast potential for some while excluding others. As a result, the system becomes more “narrow-minded” and dependant upon specific processes and resources. As potential accumulates, the regime becomes “too big to fail” as well as "an accident waiting to happen" as resilience decreases along with adaptive capacity. To the actors within the system, the future seems certain until an unforeseen disturbance leads to the Ω collapse phase.

Figure 8: Adaptive renewal cycle (Panarchy) in two dimensions. Source: Gunderson and Holling 2002

Figure 9: Panarchy in three dimensions, including resilience. Source: Gunderson and Holling 2002

The release phase (Ω) is marked by an event or a series of events which disrupt the complex web of mutually reinforcing relationships. The potential bound in the resources accumulated in the previous phase are released and the high connectedness unravels. Forest fires or outbreaks of pests are examples in ecosystems; market collapse is an example in economic systems; revolution is an example in social systems. This phase is otherwise termed the "creative destruction phase" in accordance with the theories of Austrian economist, Joseph Schumpeter. The creative destruction phase establishes the conditions for the reorganization (α) or renewal phase, which is marked by a period of high potential but low connectedness. Uncertainty is high in the renewal phase but, most importantly, the weak internal control within the system allows for the introduction of experimentation and novelty. Types of vegetation which were suppressed in the conservation phase are suddenly given the chance opportunity to create new possibilities. In the wake of economic and political upheaval, new groups and ideas are able to take root. With low connectedness, however, capital and resource potential leaks from the system (marked by the x on the diagram in figure 8). Normally, only a small portion of the resources and potential are lost, yet it is possible, argue Gunderson and Holling, that in extreme cases this leakage can be irreparably damaging for particularly irresilient systems. This can occur when certain vital cultural institutions or species, necessary in maintaining the system's structure and function, are lost altogether.

Conversely, it is possible for resources and information to be stored at higher or lower scales. Panarchies are described as being essentially holarchical, meaning that adaptive cycles are nested within larger adaptive cycles that move more slowly. While collapse can “revolt” upwards or “cascade” downwards – precipitating the collapse of other cycles at different scales - it is also possible that useful information can be stored in nested or nesting Panarchies which can be transferred back to a system after reorganization. In the parlance of resilience, this is referred to as “remembrance”. In this way, systems can become increasingly more adaptive in future iterations. Thus, systemic collapse is seen by resilience thinkers to be a necessary process that drives iterative learning in ecosystems and institutions, making them more adaptively resilient over time.

Finally, it should be noted that there is a high correlation between adaptive resilience and functional diversity. The shift from the r to K phase, for example, represents the point of the cycle in which functional diversity peaks and then reduces gradually, along with resilience, as the K phase matures. The presence of a diverse array of species within each group of functional types contributes to the resilience of a system in two ways: (1) redundancy or narrow ranged functional compensation in which the loss of one species due to a disruption or surprise event will not result in the systemic function performed by the species being lost; (2) cross-scale reinforcement: while multiple species of the same functional type can perform the same function in an ecosystem, their behaviors and responses to environmental changes can vary significantly across scales. (Holling, Gunderson, and Peterson 2002, pg.85) Gunderson, Holling and Peterson give an example of this latter phenomena in terms of avian predatory patterns during budworm outbreaks in boreal forests:

"Species in different [body size] lumps forage at different scales, initiating their foraging responses to different sized aggregations of budworms. Small warblers, for example, respond to aggregations on branches, larger ground sparrows to aggregations on trees, and still larger grosbeaks to aggregations in forest patches. Hence, as budworm populations start to jump from one level of the Panarchy to influence larger ones, a strong counteraction develops that brings more and larger avian predator species into play, with larger appetites from larger areas. When the

regulation eventually breaks, it does so suddenly and over large spatial scales of hundreds of kilometers. The creative destruction phase of the forest's adaptive cycle is released." (Holling, Gunderson, and Peterson 2002, pg. 85)

The point at which the regulatory action breaks is the point at which the functional diversity of species brought to bear is insufficient to contain the disruptive event. In many ways it is possible to understand the Theory of Panarchy through the ideas of Kay, Ulanowciz or Jorgensen and vice versa. What Panarchy describes as decreasing adaptive-resilience, systems ecology views as path-dependancy and loss of functional diversity. What Panarchy views as collapse and reorganization, Kay views as a system flipping to another thermodynamic branch altogether. Panarchy, in Kay’s view, therefore constitutes a “dual branch system” (Kay et al. 1999). Small failures experienced by adaptive systems during the growth/exploitation phase can be viewed as the collapse of smaller, nested Panarchies within the adaptive cycle. In the thermodynamic parlance used by Kay, these could be viewed as “retreats” to earlier bifurcation points for the whole system. Because the cost of failure is low in the r-phase, the system’s identity is merely altered rather than changed altogether. There may be many attractors and thermodynamic branches at smaller scales that can be exploited throughout a single Panarchy, but only up to a point. This is the point of departure between Kay’s thought and that of Gunderson and Holling. For the latter, when ecosystems reach maximum population, (i.e. high stability, low resilience, low novelty and low variability) they are so constrained that they become unable to bifurcate. Hence, collapse occurs when there is nowhere left to go but down.

Panarchy is an expression of what is here called the successional development narrative of growth and collapse in complex systems. Although the claims of resilience thinkers can be corroborated, to a certain extent, by systems ecologists, it has been argued (Hornborg 2009) that they overstep in attempting to apply their narrative to social systems and economics. In order to investigate these matters further, it is necessary to turn to the social sciences. The next section will discuss how the successional narrative can be understood within the framework of ecological economics. Later sections will attempt to address Hornborg’s criticisms.

1.6: Societal Metabolism

Theories concerning biophysical flow, metabolism and self-organization in living systems have always been accompanied by parallel counterparts in social theory and economics. As early as the industrial revolution, Sergei Podolinsky, a Ukrainian physician and socialist, solicited Marx and Engels’ counsel in an attempting to reconcile the Marxian labour theory of value with thermodynamic analyses of the economic process (Martinez-Alier and Schlüpmann 1987). In doing so, many credit Podolinksy as having written the first chapter in what was to become the narrative of social metabolism. Despite Engels having rebuffed Podolinsky, Marx himself is sometimes credited with having inaugurated the concept of social metabolism due to his occasional use of the term “stoffwechsel”3 when referring to societal energy or material exchanges with nature. The Marxian concept of “metabolic rift” refers to Marx's critique of capitalist agriculture and related deleterious effects of chemical fertilizers on natural soil fertility during the second agricultural revolution. Although Marx himself never used the term “metabolic rift”, the concept is frequently invoked by modern day Marxists to describe what they consider to be an implicit Marxian theory of unequal ecological exchange (Burkett and Foster 2006; Foster and Holleman 2014).

While the validity of these assertions remains a subject of vigorous debate (Hornborg 2015), the use of the term “metabolism” as a metaphor for biophysical exchanges between society and nature has been adopted by a variety of epistemological traditions. Since the 1960s, social metabolism has become something of a buzz-phrase. Urban planners, starting with Abel Wolman, use the term “urban metabolism” when referring to "the metabolic requirements of a city...defined as all the materials and commodities needed to sustain the city's inhabitants at home, at work and at play" (Wolman 1965). "Over a period of time”, Wolman argued, “these requirements include even the construction materials needed to build and rebuild the city

3 A note concerning etymology: “stoffwechel” is a German compound of “stoff” (material) and “wechel” (exchange). In practical use, the term is synonymous with the English word, “metabolism”, referring to “the chemical processes that occur within a living organism in order to maintain life” (Oxford Dictionary). The word, “metabolism,” is itself derived from the Greek “metabolḗ” (μεταβολή), meaning “a change”. Because the word for metabolism in German literally means “material exchange”, it’s association, by Liebig and Marx, with biophysical exchanges between nature and society is perhaps less of a conceptual leap than it is a turn of phrase.

itself. The metabolic cycle is not completed until the wastes and residues of daily life have been removed and disposed of with a minimum of nuisance and hazard". Following Wolman, industrial ecologists refer to “social metabolism” or “industrial metabolism” when referring to “anthropogenic material and energetic flows” within industrial and socioeconomic processes (Fischer-kowalski and Huttler 1999; Krausmann et al. 2008; Haberl 2001; Schaub and Turek 2010).

While the concept of metabolism continues in general to be applied as a metaphor to describe social processes, Mario Giampietro and others within the MuSIASEM community have distinctly argued that the concept should instead be understood more broadly as “the property of systems using biophysical flows to organize themselves” (Madrid-López and Giampietro 2015). Recently, Giampietro has suggested that the former (metaphorical use) be referred to as “social metabolism” while the latter (non-metaphor) should be referred to as “societal metabolism” in order to avoid confusion. As a non-metaphor, Giampietro is therefore suggesting that the metabolism of human societies is a different expression of the same thermodynamic processes which drive the self-organization of both nonliving and biological systems alike. He argues:

“All natural systems of interest for sustainability (e.g. complex biochemical cycles on this planet, ecological systems and human systems when analyzed at different levels of organization above the molecular one) are dissipative systems. That is, they are self-organizing, open systems away from thermodynamic equilibrium” (Giampietro 2003, pg. 31).

This is the most basic theoretical premise which underlies the MuSIASEM methodology, which will be discussed later.

Circumventing the Marxian discourse on metabolism, Giampietro et al. point to Alfred Lotka (1956) as having first extended the concept of metabolism as a property which is characteristic of both societal and biological systems. Human society, he argued, expresses patterns of both "endosomatic" as well as "exosomatic" metabolism. Endosomatic metabolism refers to physiological conversions of energy (i.e., food, biomass) which occur in the human body to control and generate applied power. Exosomatic metabolism, in contrast, refers to the

“stabilized set of flows of energy and materials inputs transformed under human control within the socio-economic process outside the human body… with the goal of amplifying the output of useful work associated with human activity" (Giampietro et al. 2014, pg. 25, 28). The exigencies of the combined technical capital which constituted “exosomatic metabolism” in Lotka’s view, “bound men together into one body: so very real and material is the bond that society might aptly be described as one huge multiple Siamese twin” (Lotka 1956, pg. 369). Building upon Lotka’s concepts, Odum described societies as “metabolic networks self-organizing through informed autocatalytic loops” in the same fashion as ecosystems. His influential work, Environment, Power and Society, published in 1971, depicts elaborate thermodynamic network descriptions integrating societal and economic processes. Since Odum, many systems ecologists, such as Ulanowicz and Holling et al., make the explicit point that their observations of ecosystem dynamics can be applied to societal and economic systems as well. In recent decades these arguments have led to a natural alliance between systems ecology and ecological economics.

The “phenomenological kinship” (Georgescu-Roegen 1989, quoted in Bonaitu 2011, pg. 159) between societies and ecosystems was similarly recognized by the influential Romanian-born economist, Nicholas Georgescu-Roegen, who is often credited as having been the father of ecological economics. Centrally, Georgescu-Roegen was concerned with the implications of the second law for economic processes. In his famous work, The Entropy Law in the Economic Process (1971), he noted that socioeconomic systems, like ecosystems, are open and must sustain a constant throughput of high-quality (i.e. low entropy) energy and material resources to subsist. Observing that the second law requires that all transformations will result in a net- increase of entropy, Georgescu-Roegen theorized that production processes in the economy (which transform natural resources) result in an irreversible, global loss of energy and material availability.

Further, Georgescu-Roegen observed that the Earth itself is a material system is only open to energy and not material exchange. Due to the unidirectionality of entropy, the economic process, he argued, would increasingly degrade the Earth’s finite biophysical resources until non-renewable resources were exhausted and material recycling would become increasingly

inviable. The issue of recycling even prompted Georgescu-Roegen to propose a fourth law of thermodynamics: "in a closed system, material entropy must in the end reach a maximum" (Georgescu-Roegen 1977) which implies that “it is impossible to recycle matter completely.” Though he was heavily criticized for proposing to amend the laws of thermodynamics, the crux of Georgescu-Roegen’s argument, with respect to the declining availability of useful matter, is difficult to argue against: “Even though we can pick up all the pearls from the floor and reconstitute a broken necklace, no actual process can possibly reassemble all the molecules of a coin after it has been worn out” (Georgescu-Roegen 1975, pg. 356).

For Georgescu-Roegen, the confluence of these problems, when directed by economic models which emphasize growth above all, would necessarily result in the impoverishment of future generations:

“Every time we produce a Cadillac, we irrevocably destroy an amount of low entropy that could otherwise be used for producing a plow or a spade. In other words, every time we produce a Cadillac, we do it at the cost of decreasing the number of human lives in the future. Economic development through industrial abundance may be a blessing for us now and for those who will be able to enjoy it in the near future, but it is definitely against the interest of the human species as a whole, if its interest is to have a lifespan as long as is compatible with its dowry of low entropy. In this paradox of economic development we can see the price man has to pay for the unique privilege of being able to go beyond the biological limits in his struggle for life” (Georgescu-Roegen 1973, pg. 47).

He further argued that the entropic unidirectionality of real processes are nowhere represented in the dominant neoclassical economic paradigm. This omission, he believed, was due to the attempted alignment, by 19th-century neoclassical economic theorists, of economic theory with Newtonian physics:

“By their own proud admission, the greatest ambition of these pioneers was to build an economic science after the model of mechanics—in the words of W. Stanley Jevons—as “the mechanics of utility and self-interest” ... Like almost every

scholar and philosopher of the first half of the nineteenth century, they were fascinated by the spectacular successes of the science of mechanics in astronomy and accepted Laplace’s famous apotheosis of mechanics … as the evangel of ultimate scientific knowledge” (Georgescu-Roegen 1975, pg. 347).

The “apotheosis of mechanics” to which Georgescu-Roegen is alluding, was astronomer Pierre Simon Laplace’s “discovery” of the planet the Neptune, using orbital resonance mathematics, before actually being able to observe the planet through a telescope. The enthusiasm for such discoveries led to similarly mechanistic principles to be adopted by classical economists in the hopes that economics could be transformed from a moral philosophy to a “true science” with irrevocable, scientific authority.

Concurring with Georgescu-Roegen, Philip Mirowski and more recently Robert Nadeau have pointed to the many neoclassical economic models that were adapted directly from misguided and quickly discredited “energetic” models used in 19th-century physics (Mirowski 1988; Mirowski 1989). In struggling to account for the phenomena of heat, light, and electricity, the 19th-century German physicist, Hermann-Ludwig Ferdinand von Helmholtz, had theorized the existence of a ubiquitous, “vague and ill-defined… protean field of amorphous energy” in which to explain them (Nadeau 2015). In brief, von Helmholtz hypothesized that the mysterious energy would cause closed systems to tend towards a state of dynamic equilibrium in which “change tends not occur because forces, influences and reactions cancel each other out” (Nadeau 2015, pg. 102). We know this today to be untrue; closed systems tend towards maximum entropy. However, even the brilliant minds of Jevons, Edgeworth, Walras, and Pareto were so influenced by von Helmholtz’ theory that they quite literally “wrote down the equations from the theory in physics... and substituted economic variables for the physical variables”. In doing so, Naddeau describes that “utility was substituted for energy, the sum of utility for potential energy, and expenditure for kinetic energy. The forces associated with utility-energy were represented as prices and spatial coordinates described quantities of goods” (Nadeau 2015, pg. 102, paraphrasing from Mirowski 1988, pg. 310). In attempting to advance an economic theory “analogous to the science of astronomical forces” (Walras, quoted in

Nadeau 2015, pg. 103), the neoclassicists were not only allowed but also compelled to advance the following suppositions in economics:

“1) market systems are closed and exist in a domain of reality separate and distinct from the external environment; 2) a field of utility-energy operates within closed market systems and forces associated with this field manifest as the dynamics of these systems; 3) these dynamics govern decisions made by economic actors and sustain closed market systems in states of equilibrium if they are not interfered with by external or exogenous agencies like government” (Nadeau 2015, pg. 102)

Because the field of energy in von Helmholtz’ theory was supposed to be unchangeable, due to the conservation law, the neoclassicists reasoned that their equivalent “utility-energy” was conserved as well. The economic process is, therefore, conceptualized as “a physical neutral process” in which a “closed loop from production to consumption with no inlets or outlets” (Nadeau 2015).

In defense of the neoclassicists’ naiveté, Georgescu-Roegen pointed out that mechanics was the order of the day in the 19th century, during a time in which the laws of thermodynamics were not yet well understood. “They thus had some attenuating circumstances,” he wrote, “which cannot, however, be invoked by those who came long after the mechanistic dogma had been banished even from physics” (Georgescu-Roegen 1975, pg. 347). However, even with the abandonment of von Helmholtz’ theory, the spectre of his “protean energy” remains in economics today. The “circular flow” of income (figure 10), a cornerstone of economic thought, conceives of the economic process as a cyclical exchange of goods and services for labor between firms and households. The profit generated for firms by the sale of goods and services allows for economic growth and the expansion of employment. Higher employment drives the purchase of more goods and services and so on. Growth, in this paradigm, is seen as the driving force behind increasing material standards of living and hence human wellbeing.

Figure 10: the circular flow of income model from neoclassical economics. Source: Daly and Farley 2011. Alarmingly, this unexamined episode in economic history suggests that the entirety of modern economic thought, which has grown out of neoclassical economics, is based upon a quickly discredited, pseudoscientific fad in mid-19th-century physics. What has emerged in economics is a belief that the economic process constitutes a closed system without context. Natural resources are subsumed as goods and services, the value of which can only be determined by the price consumers are willing to pay for them. Resource substitution, pricing, and technological innovation are expected to render null issues relating to resource scarcity. This position is best demonstrated in Robert Solow’s famous statement: ‘‘If it is very easy to substitute other factors for natural resources, then there is in principle no ‘problem’. The world can, in effect, get along without natural resources, so exhaustion is just an event, not a catastrophe” (Solow 1974, pg. 11). “Cornicopians” such as Solow and Stiglitz lampooned Georgescu-Roegen, Meadows et al. and Odum, labeling them “Prophets of Doom” and arguing that they did not possess a sufficient grasp on economic theory.

Ecological economists have traditionally delighted in lampooning Solow’s position as well. Daly, in response to the “no problem” declaration, quipped “As an ‘if-then’ statement, this is no less

true than saying, ‘If wishes were horses then beggars would ride’” (Daly 1997, pg. 261). Kenneth Boulding declared before the U.S. Congress: “Anyone who believes exponential growth can go on forever in a finite world is either a madman or an economist” (Energy Reorganization Act of 1973: Hearings, pg.248). Giampietro and Mayumi have even proposed to describe economists as a “Granfalloon,” a term from the fictional religion of Bokonism in Kurt Vonnegut’s, Cat’s Cradle (1963), which is defined as “a group of people… who believe they are helping to bring about a greater plan, but are actually not” (Giampietro, Mayumi, and Sorman 2011, pg. 385; Vonnegut 1963). Clearly, for ecological economists, like Newt in Vonnegut’s book, there is “no damn cat and no damn cradle” in conventional economic models. Instead, the preanalytical vision of ecological economics subsumes the neoclassical circular flow model as an open system nested within a closed, diathermic system representing the Earth’s biosphere (fig. 11).

Figure 11: The ecological economic model, in which the circular flow of neoclassical economics is situated within a finite, biophysical context. Source: Glucina and Mayumi 2010.

Before the emergence of ecological economics, Georgescu-Roegen conceived of a new, dialectical approach to coupled biophysical and economic affairs: bioeconomics, along with a “fund-flow analysis” accounting scheme in which biophysical categories are defined as stocks, flows or funds in a fashion similar to the systems dynamics modelling of Meadows et al. Although it has been noted (Bonaiuti 2007) that Georgescu-Roegen bore uneasiness toward Prigogine’s “order from disorder” thesis in particular, many authors - most notably Mauro Bonaiuti (Bonaiuti 2007) and Georgescu-Roegen’s former pupil, Kozo Mayumi (Mayumi 2001) - have recognized that there is significant resonance between bioeconomics and systems sciences. Indeed, Georgescu-Roegen’s reticence to integrate these views is considered by many to have been a “missed opportunity” (Bonaiuti 2007). In appealing to the dogma of both mechanism and unrestrained growth for growth’s sake, neoclassical economics is positioned in stark contrast to central principles in both bioeconomics and systems science. In combining these views, it becomes clear that the neoclassical paradigm describes not only a machine but also an impossible machine. This realization has led systems theorists and ecological economists alike to adopt the position that the dominant economic paradigm can never adequately address issues of sustainability.

1.7: Energy Gain and Energy Return on Investment

As mentioned earlier, Georgescu-Roegen’s fourth law of thermodynamics was criticized heavily, consistent with many other criticisms across disciplines against the use of entropy as a measure of disorder. Furthermore, his example of a worn-out coin being impossible to reconstruct is also not strictly accurate. All of the molecules worn off of a given penny are likely still on earth, and it is conceivable that even the tiniest materials could somehow be recovered and reconstituted. However, many criticisms of Georgescu-Roegen’s thesis are prone to nitpicking his rhetorical overstatements while overlooking the proverbial bioeconomic forest for the trees (Khalil 1991). The crux of Georgescu-Roegen’s arguments are difficult to argue against, as Kozo Mayumi, his former student, reflects: “his observation that economics needs to incorporate the fact that a unit of energy can only be used once, and that complete recycling of matter is not practical, are correct” (Glucina and Mayumi 2010, pg. 23). What concerned Georgescu-Roegen was not the theoretical impossibility of perfect of even near-perfect recycling, it was the increasing impracticality of economic processes due to the increasing inaccessibility (i.e. decreasing “quality”) of energy and materials due to irreversible qualitative transformations in real processes. This position is summarized succinctly by Daly, another student and longtime proponent of Georgescu-Roegen’s:

“Does the fact that we discovered uses for aluminum imply that we can invent a technology to recycle all the particles of rubber scraped from tires on curbs and interstate highways? The difference is that there is a technology for using aluminum that is economic, but the known technologies for recycling rubber particles on highways are not economic. The main reason for that fact is that aluminum deposits are concentrated, and scraped rubber particles are highly dispersed. One recycling technology for rubber particles requires many people on their hands and knees using magnifying glasses and tweezers. That is not likely to be economic. Whether it is inevitable that more matter will be dissipated in the form of worn-out tweezers and skinned knees than will be recycled in the form of gathered rubber particles is a nice question that I cannot answer. However, it is clear to me that tweezer-based

recycling of specks of rubber (or vacuum cleaning or sand blasting technology) will be ruinously expensive in terms of energy, labor, and time regardless of the exact balance of materials dissipation” (Daly 1992, emphasis added).

The implicit argument in Daly’s statement is that the resources invested in the recovery of any other resource must result in an appropriate net gain. This is as true for materials as it is for energy. Copper is required in the process of mining and refining copper, and the net material gain makes this process worthwhile. While copper may be abundant now, this may not always be the case. Recall figure 1, in which mixed materials are considered to have “higher entropy.” Whether we accept that entropy is a measure of disorder or not, the reality of the situation is that mixed or dispersed materials are more difficult to recover. While mineral deposits tend to be concentrated, minerals in landfills are not. Thus, copper return on copper invested must necessarily reduce over time. These issues become immediately relevant to the viability of industrial society when we begin to consider energy. All processes also require energy, meaning that processes such as copper production “implies a negative net energy.” The issue of gain is particularly important in the generation of energy carriers (i.e. useful forms of energy such as gasoline or electricity) which itself requires an upfront investment of energy. To compound matters, energy, once dissipated, is impossible to recycle. “Whatever we may do,” Georgescu- Roegen writes “we are faced with new snags” (Georgescu-Roegen 1979, pg. 1025).

Net-neutral or negative net gains are described as “uneconomic” by Daly. For Georgescu- Roegen, the distinction was between “viable technologies” and “feasible recipes” (Georgescu- Roegen 1984). While it may be feasible to build a battery out of a potato, potato-battery technology is not now, nor will it ever be, sufficiently advanced to the point where it can be used to power the machinery used to cultivate potatoes. Of course, this is an exaggeration, but it illustrates the basic point. All other technical considerations aside, it would simply require too many potatoes to make sense.

Hall et al. (Hall, Lambert, and Balogh 2014) have labeled the ratio of energy invested to energy gained as “energy return on investment” (EROI) which determines to a large extent the feasibility of any energy production technology. EROI can be calculated as follows:

푘푐푎푙 표푓 푓푢푒푙 푒푥푡푟푎푐푡푒푑 퐸푅푂퐼 , 푘푐푎푙 표푓푑푖푟푒푐푡, 푎푛푑 푖푛푑푖푟푒푐푡 푒푛푒푟푔푦 푟푒푞푢푖푟푒푑 푡표 푙표푐푎푡푒 푒푥푡푟푎푐푡 푎푛푑 푟푒푓푖푛푒 푡ℎ푎푡 푓푢푒푙

Figure 12: A cartoon illustrating the concept of energy return on investment. Source: Hall, Lambert, and Balogh 2014.

One might think of EROI as “energy gained vs. energy required to get that energy” (Murphy and Hall 2010). The concept of EROI is therefore frequently attached to the issue of “Peak-Oil” or, more recently, “Peak Everything” (Heinberg 2005; Heinberg 2007) as easily available deposits of energy or material resources become increasingly scarce. Globally, the EROI for oil and gas production in the year 1999 was 35:1. By 2006, that figure had dropped to 18:1 (Hall, Lambert, and Balogh 2014). Renewable energy is similarly subject to these constraints. Issues relating to EROI and peak oil bring into question the very future of industrial civilization. The anthropologist, Joseph Tainter, notable for his 1988 work, The Collapse of Complex Societies (1988), hypothesizes that changes in resource gain are a determining factor in the organizational change of human and animal societies. Tainter argues that there is a period of rapid growth when societies first begin to exploit finite sources of high-quality resources. When the EROI of these resources begins to diminish, societies then shift into “low-gain” forms of organization which are characterized by increased complexity due to the need to manage resources more carefully (Allen et al. 2001; Tainter et al. 2003). For Tainter, collapse occurs when the level of societal complexity outpaces capacity to manage disturbances. This

Malthusian-like dilemma is similarly recognized by Homer-Dixon, who has coined the term “ingenuity gap” to describe it (Homer-Dixon 2000). The most extreme prediction arising out of the concepts of EROI or “peaking” has been Richard Duncan’s “Olduvai Theory” which posits that over-reliance on fossil fuels will necessarily, and very soon, result in catastrophic collapse of global society accompanied with the reduction of the Earth’s population to 2 billion by 2050 (Duncan 2005). From these arguments, it is clear to see that a significant body of literature suggests that societies, like ecosystems, appear capable of growing themselves to the point of impending collapse.

Figure 13: The “Hubbert Curve” with added illustrations of cavemen to illustrate both the concept of Peak Oil as well as the Olduvai Theory. Source: Giampietro, Mayumi, and Sorman 2011.

Many theorists reject Duncan’s predictions and instead suggest that there is sufficient reason for optimism if the appropriate steps are taken. Daly calls for the adoption of a “Steady State Economy”, describing "an economy with constant stocks of people and artifacts, maintained at some desired, sufficient levels by low rates of maintenance throughput, that is, by the lowest feasible flows of matter and energy from the first stage of production to the last stage of

consumption” (Daly 1991, pg. 17). “Degrowth” has become a rallying call for social and environmental activists around the world calling for an end to extractive capitalism and new forms of governance that emphasize human well-being and sustainability (Demaria et al. 2013). In general, the optimistic consensus seems to be that catastrophe can be averted if the appropriate clean energy transition can be managed in tandem with curtailed economic growth.

Giampietro and Mayumi are cautiously optimistic. While rejecting the over-optimism of renewable energies and sustainable development, they invoke the concept of resilience and cite what they call the “Robinson Crusoe effect” in arguing that “the almost magical ability of adaptation to novelties (i.e. flexibility for dealing with disturbance) is probably what was missing in the analysis of the neo-Malthusian pessimists or prophets of doom in the 1970s” (Giampietro and Mayumi 2009, pg. 259). Instead, they argue that the adaptive resilience of societies is maintained when the dynamic budget of resources favor what they call the “dissipative” components of socioeconomic systems, such as the household sector or services and government. Giampietro et al. theorize that when the productive “hypercyclic” components of a society require a higher proportion of energy to produce a surplus of energy carriers for the whole society, the dissipative components will be under-budgeted, and the society will lose adaptive capacity as a result. MuSIASEM theorists, therefore, maintain that the viability of societal configurations depends on our capacity to maintain a favourable dynamic energy budget in addition to implementing new technologies and economic policies.

1.7: Health Within the Systems Narrative

At this point, a recurring narrative of successional development, growth and collapse should be apparent from the literature from both systems theory and ecological economics. This narrative can be summarized in a series of postulates:

• Organisms, ecosystems, and human societies are all examples of complex, self- organizing, thermodynamically open systems. • These systems form “nested holons” manifesting across spatial and temporal scales • These systems organize around attractors and, through growth or perturbation, can reorganize and “flip” to different thermodynamic branches through bifurcation or catastrophic collapse. • Alternatively, systems can exhibit resilience and maintain their attractor states through adaptation. • Self-organizing systems are driven to grow successively and develop through autocatalytic feedback loops. • When these systems begin to push the resource capacity of their environments, they become selective for autocatalytic processes which most effectively facilitate growth. • In doing so, systems lose functional diversity and adaptive capacity. They become overly complex given the resources available and are “an accident waiting to happen.” • As they become too rigid to withstand unanticipated environmental change, complex systems lose resilience and collapse becomes inevitable.

As noted, these observations have led systems ecologists to associate adaptive resilience and dissipative capacity with the “health” of ecological and social systems. Berkes and Folke, for example, hypothesize that “maintaining resilience may be important for both resources and social institutions – that the well-being of social and ecological systems is thus closely linked” (Berkes, Folke, and Colding 1998, pg. 21). However, critical theorists, such as Alf Hornborg, take particular issue with the application of these concepts to social systems. For Hornborg, the views expressed by Berkes et al. constitutes “the ideological disarmament of disaster,” (Hornborg 2009) arguing that systems ecologists ignore that the trajectories of social systems

“are generally propelled by individuals and groups struggling to maximize their power and affluence” (pg. 254). That there is “no mention of power” within the conceptual framework of resilience thinking is a yet another serious concern for Hornborg. A successional narrative, in his view, ignores “the role of cultural idiosyncrasies (e.g. various versions of fetishism and consumption patterns) as autonomous driving forces propelling the ecological trajectory of a social system.” Hornborg continues:

“In building [their] hypotheses on the naively functionalist notion of ‘the well-being of social and ecological systems’ … they demonstrate once again how their vantage points in fields such as systems ecology have constrained them from seriously engaging the logic of social systems. To any modern social scientist, ‘the wellbeing of social systems’ is simply an impossible phrase” (pg. 254, emphasis added).

Hornborg’s criticisms are not unwarranted. There is an implied and largely unexamined functionalist quality that is common to many of these systems theories. Instead of looking for isomorphisms between organisms, societies, and ecosystems, many theorists instead tend to focus on how the latter two can be considered akin to organisms, in the tradition of Durkheim, or even “superorganisms” in the tradition of Wheeler or Lovelock (Wheeler 1928). In some cases, the claim is inverted, as Salthe suggests instead that ecosystems could more appropriately be viewed as “superecosystems” (Salthe 2003). More often than not, these distinctions serve only as unnecessary rhetorical devices when what they should emphasize is the commonalities shared between different patterns of self-organization across multiple scales. This is likely why Giampietro, as mentioned earlier, has recently and explicitly stated that his use of the term metabolism refers to “the property of systems using biophysical flows to self-organize” (Madrid-López and Giampietro 2015). I believe he is right to do so for a number of reasons.

First of all, the conflation of organisms with societies can potentially have negative ideological impacts. Capra agrees:

“the components of an organism exist for the organism’s functioning, but human social systems exist also for their components, the individual human beings…

Organisms and human societies are therefore very different types of living systems. Totalitarian political regimes have often severely restricted the autonomy of their members and, in doing so, have depersonalized and dehumanized them. Thus fascist societies function more like organisms, and it is not a coincidence that dictatorships have often been fond of using the metaphor of society as a living organism”(Capra 1996, pg. 211).

Further, the concept of health can be equally dangerous. In the 1989 film, The Architecture of Doom, Peter Cohen recounts that doctors in Nazi Germany were more likely than any other profession to be members of the Nazi party. Considering themselves to be social physicians administering to the collective “body of the German Volk,” the SS medical corps operated mobile x-ray trucks for the purposes of cancer screening Germanic peoples in newly conquered territories across Europe. Chillingly, believing that “degenerate peoples” were akin to an infectious disease, the very same doctors perpetrated many of the most terrible atrocities committed during the war (Cohen 1989). Contrary to Hornborg, “the health and wellbeing of social systems” is not an “impossible phrase”. It is an entirely all-too-possible phrase which has been invoked in the past to disastrous effect.

Rather than abandoning the use of these terms with respect to societies and ecosystems, it would be prudent systems scientists and resilience thinkers to, like Giampietro, be more explicit in how they conceptualize their metaphorical use. “Health and wellbeing” should be defined specifically in terms of the systems narrative of self-organization, resilience, and successional development so that the terms can be applied - and not misused - across levels of scale. This, however, is more difficult than one might imagine. One of the reasons the term is used so widely is that it is sufficiently vague that its meaning changes in different contexts.

Let us then begin with some of the basic definitions of health. The Cambridge Dictionary defines health as “the condition of the body and the degree to which it is free from illness … the state of being well.” The World Health Organization defines health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.” Health, therefore, would seem to have something to do with “wellness.” Wellness, however, is

defined by Merriam-Webster as “the quality or state of being healthy”, so we appear to have encountered an impredicative loop. Turning (in frustration) to Wikipedia, health is there defined as “the level of functional or metabolic efficiency of a living organism.” If we accept Schrodinger’s notion of metabolism as the import and export of high and low entropy, then the wiki definition of health provides something akin to Kay’s concept of ecosystem integrity. However, like Kay and Schneider’s restated second law, the association of health with energy dissipation capacity is “necessary but not sufficient” for describing the health of ecosystems, not to mention organisms and, in particular, humans and their societies. Generalizing energy dissipation as health is to ignore that - from the perspective of Panarchy and infodynamics - highly developed ecosystems exhibit senescence, irresilience and path-dependency. If a thing is increasingly prone to collapse, should we call it healthy? Normally we would not, regardless of the services that thing provides. Most importantly, if this concept is extended to suggest that the telos of human society is to dissipate energy, then, according to Salthe, “we must acknowledge that war is the most perfect way for humans to uphold the Second Law” (Salthe 2008, pg. 57). Few nowadays would dispute that war is the antithesis of health and wellbeing.

Thus far, systems theorists have yet to define one conceptualization of health or well-being which can be applied generally to organisms, ecosystems, and societies. This is perhaps why Peterson argues, contrary to Hornborg’s claims, “most resilience researchers do not think that medicine or health are good metaphors for managing, manipulating, or understanding ecosystems” (Peterson 2009). Furthermore, resilience thinkers do in fact grapple with the many ways in which societies and ecosystems differ. Their answer is that societies are distinguished by 1) hierarchies of abstraction; 2) the capacity for reflexivity; 3) “the ability to generate expectations and look forward rather than to react”; and 4) the ability to “externalize… symbolic constructions in technology” (Gunderson and Holling 2002, pg. 119). Again, however, issues of culture, ideology, power, and intersectionality are severely lacking in their description and are therefore unacceptable to Hornborg:

“Rather than try to develop a conspicuously and naively non-political cybernetic etiology of socio-ecological degradation – based on the assumption that such processes, irrespective of capitalist extractivism, are universally patterned,

predictable, and potentially manageable – I challenge resilience theorists to address the operation of the global economic system that is the very obvious source of such processes.”

It is perhaps ironic for systems ecologists, who emphasize uncertainty in complex systems, to suggest that all development is ultimately subject to a universal, deterministic outcome. In response to Hornborg, resilience thinkers have conceded that “‘power’ – however we choose to define it – has been problematic to integrate within the framework of social-ecological systems.” Peterson (2012) has expressed that resilience thinkers are “getting to it,” meaning that these issues are still yet to be fully implemented within the resilience framework. However, even today the issue has not been addressed to the satisfaction of any party. This acrimonious discourse is problematic for both systems science and political ecology. Power and conflict are fundamentally interwoven with issues of health and wellbeing. If systems ecologists seek to grapple with the “well-being” of social systems, then power must be integrated within the resilience framework. Conversely, the systems narrative of successional development is robust and compelling. Ignoring these patterns would do little to serve critical theory. Instead, systems thinkers and political ecologists should strive towards a common understanding.

Coffman and Mikulecky, synthesizing theories from systems ecology, Stanley Salthe and relational theorist, Robert Rosen, offer a way to resolve the current epistemological mess. Rather than arguing that the “universally patterned” cycle of growth and collapse occurs “irrespective of capitalist extractionism,” they instead suggest that the ascension of capitalist extractionism as the dominant paradigm is, in fact, an expression of the successional pattern toward senescence and collapse itself. Exploring their argument provides for a general systems understanding of health as well as a systems framework for understanding power and conflict in human societies. The following section will attempt to expand on their thesis to argue that human societies are not, in opposition to Salthe and the theory of Panarchy, cybernetically programmed to collapse or go marching off to war. Having this discussion, however, requires a brief explanation of Rosen’s ideas.

1.8: The Modelling Relation and the M-R system

Robert Rosen’s work is impossible to summarize in a brief way, but an attempt will be made here nonetheless. Best known for his work in “anticipatory systems theory,” Rosen investigated the processes whereby organisms develop internal semiotic models for understanding themselves and the world around them to generate anticipatory behaviors necessary for survival. He discovered countless examples of plant life and simple organisms (i.e. organisms without brains) exhibiting a capacity – which outmatched their cognitive faculties and could not be explained as reactionary - to “feed forward” and anticipate events (Rosen 1985). Rosen theorized that anticipatory behaviors in any organism are made possible through perceptions of natural phenomena which are then encoded into formalized semiotic models of inferential entailment. In the case of simple organisms, these formal models are somatic, meaning that they are encoded biosemiotically through evolution over time. Humans, while similarly possessing somatic models, also possess cognitive models which can be actively encoded and decoded in real time. The process of forming and applying these models is expressed in Rosen’s “modeling relation” (fig) which constitutes, in a sense, a “model of models.”

Figure 14: Rosen’s Modelling Relation. Source: www.ahlouie.com, adapted to include numbers in place of Greek characters.

The “natural system” in the modeling relation represents any natural phenomena we can observe (1) either through our perceptions or measurement. If we assume that events in nature have some sort of causal order, we can assume that events in the natural system are entailed by natural law whether we understand them or not. For any organism to have the capacity for

anticipation, these events must be encoded (2) into a formal system of inferential entailment (3). Rosen writes:

“We shall understand by a formalism any such “sublanguage” of a natural language, defined by syntactic qualities alone. That is, a formalism is a finite list of production rules, together with a generating family of propositions on which they can act, without any specification or consideration of extralinguistic referents. Thus, a formalism, as a fragment of natural language, could be “about” something (i.e., endowed with extralinguistic referents), but it need not be. A formalism, by its very nature, carries with it no “dictionary” associating its propositions with anything outside itself. It is propelled entirely by its own internal inferential structure, as embodied explicitly in its production rules. These and these alone determine the relations among the propositions of the formalism, which we have called inferential entailment” (Rosen 1991).

In other words, the formal system allows us to make “if-then” predictions because it is based on an internal language of semantic categories and syntactic production rules (i.e. things we deem relevant and our hypotheses regarding how they interact).

Essentially, this is what we are doing in science when we associate abstract numbers and values, derived through measurement, with observed phenomena. The formal system then allows us to “decode” (4) and test our predictions when we interact with the real world (i.e. the natural system). When our interactions with the natural system work out the way we think they will (1= 2+3+4) then we can say that the modeling relation “commutes” and our model is useful (Rosen 1991; 1985). “Bad models” occur when the relation does not commute, and the observer must adjust or update their encodings accordingly. The modeling relation, in effect, describes how organisms form an understanding (by encoding percepts into a formal system of inference) of anything and everything they encounter. According to Rosen, any knowledge system is semiotically encoded in this way.

However, the processes embodied in arrows (2) and (4) are unentailed, i.e. there are no natural laws that require that the natural system and the formal system be consistent with one

another. Further, there are no predetermined, rote methods used to encode the formal model and subsequently decode it back to nature (Rosen 1985). To do this suggests that there must be a story-teller involved, with a specific perspective, who has defined encoding and decoding “dictionaries” of relevant aspects of each system so they can be compared. Steps 2 and 4 are what Rosen, quoting Einstein, called, “free creations of the human mind” (Rosen 2000). In other words, this process “involves art” in the act of choosing which percepts should be considered relevant (Rosen 1991, pg. 54).

Furthermore, the natural system is a subjective abstraction of reality and is thus an “open system”, whereas a formal system, if it is consistent, is closed by definition. As an open system, the natural system exhibits degrees of freedom not present in the formalization because we subjectively define certain features of the natural system that we deem relevant at the expense of others (Mikulecky and Coffman 2012). "The complexity" Rosen argues, "of a natural system is perceived through the number of distinct modeling relations into which it can enter" (Rosen 1985, pg. 400). Within this framework, any observable phenomenon can be considered complex insofar as it can be described in many non-equivalent ways. Rosen’s concept of complexity is, therefore, invocative of the old parable of blind men attempting to describe an elephant (fig 12). Each man, touching a different section of the elephant, believes that he is feeling something entirely different and no one man can grasp the entirety of the thing that he is examining.

Figure 15: The parable of blind men attempting o describe an elephant. Image source: practicalsanskrit.com.

Simple systems (which can still be highly complicated) are systems in which one formalization will suffice. Any single model, however, cannot describe all of reality as any knowledge system is subject to the limitations of mathematical computation. In Rosen’s view, these epistemological boundaries were established long ago by Godel’s Incompleteness Theorem, which essentially holds that mathematics cannot be distilled into one set of axioms (Rosen 1991; Rosen 2000). Mikulecky and Coffman further point out that Turing, in a twist of irony, demonstrated that many real numbers (such as irrational numbers like pi) can not be mechanically computed by a Turing machine. While there are no paradoxes in nature, they do exist in mathematics because it is impossible to subsume all qualitative (semantic) aspects of reality into quantitative (syntactic) algorithms. Complexity is, therefore, a function of our inherent limitations rather than a characteristic of systems themselves. This is why biology cannot be reduced to physics, and Newtonian physics cannot be reduced to quantum mechanics; they are all formalized in non-equivalent models. The Rosennean interpretation of complexity, therefore, challenges our very notion of objectivity. In Box’s words, “all models are wrong but some are useful” (Box 1976). However, any given model is useful only insofar as it is

congruent. Thus, the modeling system (i.e. the observer) must update its formalism in the face of change, or its predictions will no longer apply.

It is generally understated the extent to which Rosen’s ideas have influenced complex systems theory. His thought is so thoroughly “encoded” into the body of systems theory that nowadays it is considered enough to provide a cursory reminder that “Rosen…suggests that complexity cannot be modeled” (Allen 2008, pg. 37) and the issue is considered addressed. Curiously, while Rosen’s modeling relation is frequently invoked within discussions of epistemological complexity (Giampietro 2003; Kovacic and Giampietro 2015), most theorists in the systems sciences overlook the concept’s necessary counterpart - the M-R system – and miss some of Rosen’s most important insights. The M-R system – standing for “metabolism and repair” – is an abstract mathematical model using category theory which illustrates how organisms differ from machines. Although thorough mathematical discussions of the M-R system can be found elsewhere (Rosen 1985; Mikulecky 2000; Mikulecky 2005; Louie 2006), a brief overview in layman’s terms will be provided here.

Figure 16: Category mapping of Rosen's M-R system. Source: Rosen 1985.

To understand the M-R system, it is first necessary to understand Aristotle’s four causes. Without having to resort to Aristotle’s Metaphysics, John Kineman provides us with a very succinct and clever description of Aristotelian causality:

"The material cause for example breakfast, would be the ingredients (things). The efficient cause would be the cooking (process). The formal cause would be the

recipe (design). And the final cause would be one’s hunger and desire to eat (purpose)" (Kineman 2003).

Newtonian science, in reducing all observables to objects and mechanisms, attempts to formalize natural phenomena in terms of only material and efficient cause. For Rosen, this accounted for our general failure to reduce biology to physical mechanism. Life is purposive – teleological – implying behavior which is designed while distinctly not implying an external, omnipotent creator. Rather, Rosen postulated that the most basic telos (final cause) of organisms is, as Schrodinger (1947) suggests, the need to “resist entropy” by “eating, drinking, breathing and (in the case of plants) assimilating.” The energy and materials consumed by an organism constitute material cause whereas “metabolism and repair” constitute a closed circle of efficient cause. Here, Rosen believed he had found Schrodinger’s answer to the question: “what is life?”: life is closed to efficient cause and life “manifests the modelling relation” (Mikulecky and Coffman 2012, pg 55) Even the simplest organism is driven to eat, reproduce (a strategy for repair) and not be eaten, in which case “it pays to anticipate” and it pays to have an anticipatory model (Mikulecky and Coffman 2012).

Because the M-R system depicts a causally closed, self-producing system, Rosen has effectively provided a formalization for what Maturana and Varela refer to as an “autopoietic system”.

“a network of processes of production (transformation and destruction) of components which: (i) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and (ii) constitute… a concrete unity in space in which they (the components) exist by specifying the topological domain of its realization as such a network” (Maturana and Varela 1980, pg. 79).

Giampietro et. al (2011) observe that autopoiesis (and by extension the M-R system) constitutes the “secularization of teleology,” which allows us to separate the concept of telos from theological discussions and its association with intelligent design. Furthermore, Rosen’s proponents claim that the modeling relation drives the evolutionary process itself (Kineman 2003; Kineman 2011). Anticipatory models allow organisms to internalize specific aspects of

59 their environments and realize their models through natural selection and adaptation. Because organisms necessarily come to possess models of themselves (as observable phenomena external to the “self”), they come to possess a model of “optimality” (i.e. health) towards which they are teleologically driven to realize (J. Rosen 2009). For some organisms, optimality may consist of simply eating and not being eaten. For others, it may involve buying new car stereo speakers. It depends on how one’s models are encoded. The following section will discuss just how far this line of reasoning can be extended.

1.9: Monocultures of Mind and Models

Here they are too busy with the material conditions of happiness, as yet they have not addressed themselves directly to happiness. And happiness... what is it? I say it is neither virtue nor pleasure nor this thing or that, but simply growth. We are happy when we are growing. It is the primal law of all nature and the universe, and literature and art are the cosmic movements working in the conscious mind.

-William Butler Yeats Letters to his son (1946)

Relational theorists, such as Kineman and Mikulecky, have recently expanded upon Rosen’s position to claim that modelling relations in aggregate come to manifest at higher levels of organisation (i.e. societies and ecosystems) that exhibit their own patterns of metabolism and repair (Kineman 2007, 2011; Mikulecky and Coffman 2012; Mikulecky 2000). In this sense, it is possible to view ecosystems and societies as autopoietic unities themselves, a claim that Maturana and Varela, it should be noted, never made. Thinking of ecosystems or societies as individuals is not problematic, regardless of the fact that their boundaries can be difficult to determine. “If our observations had the same scale relations to an organism as they have with respect to most ecosystems of biome size,” Salthe argues, “we would not suppose an organism to be an individual either” (Salthe 2003).

Kineman (2012) suggests that autopoietic systems interact with their environmental contexts (i.e. nesting holons) by decoding formal models. Over time, these models will govern how systems “entrain” their contexts into more admissive environments. Moreover, because models are constrained - but not entirely determined - by the nesting holon, the contextual system and its components mutually create one another. Kineman, focusing on the phenomenology of

ecosystems, points to the theory of ecological niche construction as evidence that ecosystem holons enter into an impredicative, reciprocal relationship with its inhabitants. “The ecosystem function of providing tiger habitat,” he writes, “creates a potential that tigers (or some similarly evolved creature) could actualize in some important way. Likewise, functions that the tiger performs have ecological effects, and modify or select aspects of the ecosystem. The ecosystem, in this sense, anticipates tigers; and tigers seek, and, in part, create, suitable conditions” (Kineman 2007, pg. 2437). The recent application of learning theory to evolutionary ecology, by Power et al. (2015), appears to support Kineman’s argument.

While Mikulecky and Coffman concur with Kineman, they go further in suggesting that human societies manifest in a similar way. Although our decodings find semiotic expression in culture, ideology, religion, and art, they are all grounded in formal systems of inference which provide an understanding of how the world works and what, in Rosen’s words, “we ought to do.” Rosen writes:

“The study of anticipatory systems thus involves in an essential way the subjective notions of good and ill, as they manifest themselves in the models which shape our behavior. For in a profound sense, the study of models is the study of man, and if we can agree about our models, we can agree about everything else” (Rosen 1985, pg. 404)

I believe Rosen is correct in this statement, as I also believe he would have understood that historically people tend not to agree. In general, the study of humanity is more often read as the study of “individuals and groups struggling to maximize their power and affluence,” in Hornborg’s words. Through this lens, religious conversion, imperialism, colonization, assimilation, indoctrination, genocide or subjugation through economic means can all be understood as attempts to expunge certain models and their associated “ways of being.” Recall, however, that any given model is only applicable for a time and must be updated. When a system entrains its environment to a sufficient extent, the system then updates its model with observations of a reality that the system itself has created. Over time, the system and its context progressively become more self-referential while excluding perspectives containing

potentially relevant observations in favor of those which are more consistent with the established order. The dominance of growth models to the exclusion of others means that, in time, the system loses touch with reality and cannot account for forces which will ultimately cause its collapse. Ulanowicz has argued that this occurs in ecosystems in the form of autocatalytic selection leading to loss of functional diversity as “inefficient” redundancies are expunged. Mikulecky and Coffman argue that this can occur in societies as dominant, growth based ideologies come to marginalize all alternatives. Their argument, in this regard, is similar to that of Vandana Shiva, who refers to the self-perpetuating hegemony of dominant knowledge as “monocultures of the mind”;

“The fragmented linearity of the dominant knowledge disrupts the integrations between systems. Local knowledge slips through the cracks of fragmentation. It is eclipsed along with the world to which it relates. Dominant scientific knowledge thus breeds a monoculture of the mind by making space for local alternatives disappear, very much like monocultures of introduced plant varieties leading to the displacement and destruction of local diversity. Dominant knowledge also destroys the very conditions for alternatives to exist, very much like the introduction of monocultures destroying the very conditions for diverse species to exist” (Shiva 1993, pg. 12).

The intellectual colonialism of perpetual growth and extractive capitalism is, according to Mikulecky (2013) and Salthe (2003), the epitome of senescence. The fact that our entire global economy is predicated upon theoretical congruence with dubious, pseudoscientific theories from the 19th century – and the fact that nobody in power seems to find that strange - would appear to support that claim. For Mikulecky or Salthe, the culmination of the Olduvai theory appears all but inevitable.

Diamond (2005), however, points out that not all societies grow to the point of collapse, arguing instead that societal collapse occurs due to failures in collective decision making. Further, Shiva reminds us that the world as we know it is the product of western colonialism, imperialism, and militarism, and that these traits are not universal to all cultures. In light of

Rosen, we can say that this is due to different cultures expressing different models and that not all of these models are teleologically driven to realize growth and expansion above all else. Indeed, Shiva’s observation is proof that while societies resemble ecosystems and organisms in some respects, they are not entirely analogous. Rosen himself rejected both the functionalist notion that societies are organisms as well as even the suggestion that the Earth itself, “Gaia”, is an organism. He, not unlike Capra, observed that the components of organisms (e.g. cells, organs) are not independently adaptive in the same way that ecosystem components (e.g. organisms, species) are. In an organism, “health is maintained precisely because individual organs cannot be treated as adaptive individuals in their own right; as soon as they become so, health is disturbed” (Rosen 1975, pg. 50). Cancer is perhaps the most obvious example of this. Thus, I propose that ecosystems, organisms, and societies are three non-equivalent types within the specification hierarchy:

{ - { { , , }}}4

The lesson푆푒푙푓 푂푟푔푎푛푖푧푖푛푔from systems푆푦푠푡푒푚 science, 퐴푢푡표푝표푖푒푡푖푐as I see it, is not푈푛푖푡푦 that 푂푟푔푎푛푖푠푚all societies 푆표푐푖푒푡푦follow a universal퐸푐표푠푦푠푡푒푚 pattern and there is nothing we can do about it. The lesson is that growth is a self-defeating final cause; that unmitigated, positive feedback ultimately leads to invariability, senescence and a loss of adaptive capacity and that this is true in all self-organizing systems. To my mind, these are the insights that we should emphasize. The fact that we can succumb to these patterns does not mean that we always do. Societies are distinct from ecosystems because humans possess cognitive models whereas ecosystem models are somatic. This is what distinguishes us as a species, and this is what distinguishes human societies from ecosystems. Human models are also infinitely more diverse in that we are animated by all manner of final cause. When Yeats wrote to his son on the happiness of growth, extractive capitalism is likely not what he had in mind. Our “growth” can be personal and not limited to expansion through over-extraction and conspicuous consumerism. Ironically, when growth becomes the dominant final cause, our societies become less human and more like ecosystems.

4 A specification hierarchy represents hierarchical levels and nested subclasses (Salthe 1993). In these terms, the Gaia hypothesis would read, {organism{ecosystem}}, whereas functionalism would read, {organism{society}}.

Having established these points, the Rosennean narrative now allows for a general notion of health that can be applied to ecosystems as well as humans and their societies. “Health,” Mikulecky writes, “entails resilience, which is maximized by way of an optimal balance between undirected variation and developed constraint. In a system, too much of either is unhealthy and ultimately fatal. Hence, to maximize resilience, development must be regulated toward achieving the optimal balance (Mikulecky and Coffman 2012, pg. 133). Waltner-Toews provides another useful insight in this regard:

“in general, definitions of the health of plants, animals, people, communities, and ecosystems include some notion of balance and harmony and some notion of reserve or capacity to respond and adapt to a changing environment. Furthermore, health is directly related to the achievement of desirable and feasible goals” (Waltner-Toews 2001, pg. 8, emphasis added).

Through these insights we can now formulate a series of postulates that constitute a more general understanding of health and well-being applicable to all three types of autopoietic systems:

1. Health is the capacity for adaptive resilience, which implies diversity in form, functional capacity and perspective; 2. Health is sustainable in both the literal and figurative “environmental” sense of the word. Systems, in order to be healthy, must be able to sustain access to the “metabolites” they require to self-organize without undermining the admissible context; 3. Health is subjectively desirable, meaning that one cannot define for another what their model of optimality entails. It also means that for one to be healthy, one must have the opportunity to pursue aspirations (determined by a given system’s model of optimality) without undue constraint. 4. Health is the absence of constraint, of which disease is only one type. Poverty, injury, fear and oppression are others. All can be said to impact the health of an organism negatively. Pollution and extraction are constraints that impoverish and impact the health of ecosystems, whereas war or economic depression constrain the health of

societies. Conflict can, therefore, be viewed as multiple systems struggling to realize their respective models at the expense of others. Violence, as any infliction of constraint preventing others from realizing their aspirations. Power can be seen as the capacity of dominant models in supplanting models belonging to other systems.

Integrating insights from relational theory gives greater practical value to the systems narrative. That is, it presents us with a very clear picture of what we should encourage, what we should resist and why we should do so. Through Mikulecky and Coffman as well as Vandana Shiva, we can understand the ecological feasibility (i.e. sustainability) of our societies to be intimately related to their inclusivity and egalitarianism. As systems become increasingly self-referential, they systematically inflict violence upon lower level components to force them to adhere to an increasingly narrow range of growth-oriented functions. By marginalizing the health of non- conforming systems at lower levels, we discard perspectives which will be necessary for the survival of the whole system. Hegemony is, therefore, unsustainable; envisioning alternatives to growth requires a “diversity of perspectives – ‘multiculturalism (in a broad sense) – engendered by, and engendering, greater respect and appreciation of the ‘other’” (Mikulecky and Coffman 2012, pg. 132).

What is also required is a return to local knowledge systems and problem structuring approaches that return ownership to the hands of marginalized or excluded peoples as stakeholders. This, in effect, is precisely what the Ecohealth Approach seeks to foster through participatory issues framing, future visioning and scenario promotion. In turn, determining ecological feasibility and social viability is precisely what MuSIASEM is designed to do. Together, these methodologies provide some of the most appropriate and sophisticated principles and tools for assessing future scenarios and supporting decision-making in the pursuit of realizing societies which are: 1) ecologically feasible; 2) mutually desirable, and 3) adaptively resilient.

Section 2: Methodologies

Multi-scale integrated analysis of societal and ecosystem metabolism (MuSIASEM) and the Ecosystem Approach for Health represent two innovative and distinct approaches to sustainability assessment, scenario analysis, and decision support when dealing with complex problems in socioecological systems. As discussed earlier, both approaches are informed by a similar systems-oriented theoretical narrative. Here, in addressing the issue of coupled ecosystem and human health, these methods are argued to be complimentary.

Neither methodology provides operating models that offer predictions or point to deterministic outcomes as the result of certain processes. Rather, both methodologies necessarily approach complex problems, first and foremost, through scenario planning, which is described by Peterson as follows:

Scenario planning is a systemic method for thinking creatively about possible complex and uncertain futures. The central idea of scenario planning is to consider a variety of possible futures that include many of the important uncertainties in the system rather than to focus on the accurate prediction of a single outcome (Peterson, Cumming, and Carpenter 2003, pg. 359).

Scenarios themselves are “structured account[s] of a possible future[s]… futures that could be rather than futures that will be” (Peterson 2003, pg. 360). Scenario writing is the process of envisioning a range of possible future situations which can be instructive for developing contingency plans and policies when the future is volatile or uncertain. Shell Oil Corporation, for example, is well known for the success of its scenario writing division which, in part, is credited for the company’s success during the 1970s oil crisis (Schwartz 1991). Increasingly, scenario planning is being implemented in environmental management in recognition of the uncertainty inherent in socioecological systems.

Ecohealth employs a participatory framework for scenario writing which will be discussed in the following section. In brief, the goal of the Ecohealth Approach is not merely to envision what plausibly “could be”, but also to encourage futures which are ecologically feasible and socially

desirable. Rather than prescribing specific protocols, Ecohealth instead provides a more general set of principles and guidelines – laid out in Kay’s “diamond heuristic” - which can be used to accomplish this. MuSIASEM, on the other hand, provides very specific protocols for a narrower set of purposes: scenario analysis and decision support. As such, MuSIASEM can be an effective “plugged in” to the diamond heuristic. The participatory issues framing, future visioning and scenario writing process of The Ecohealth Approach can generate desirable scenarios, while MuSIASEM’s analysis tool-set can be used as a filter to analyze their viability (regarding societal constraints) and feasibility (regarding ecological constraints).

This brings us back to the concept of ecological feasibility that was introduced earlier. In the parlance of MuSIASEM and Ecohealth, societal configurations which result in environmental loading or resource extraction, to the point that they undermine the metabolism or resilience of ecological systems, are not feasible. That is to say that the configuration cannot be sustained once it undermines its own context. Thus, the feasibility of possible futures must be assessed by comparing the metabolic needs of ecosystems with that of proposed societal configurations. By constricting the option space to configurations which will not terminally undermine the ecological context, it then becomes possible to envision desirable configurations within an admissible context. This, in essence, is what both our methods set out to do. MuSIASEM, however, goes one step further in recognizing that societies are themselves subject to a set of “internal constraints” which limit the option space further.

Scenarios can, therefore, be plausible without being feasible or desirable with respect to the metabolic pattern and canon of feedback loops that stabilize socioecological systems and subsystems. What’s more, scenarios geared toward optimizing the integrity of systems in isolation, along with their associated metabolic patterns, can inadvertently undermine the integrity of others. Feasibility and desirability assessments are therefore critical in determining which scenarios are suitable for promotion and what the trade-offs associated with them will be. This process can be done as a pre-analytical, diagnostic step (before or during the scenario writing process) or as a simulation, scenario testing step (after scenarios are already written).

MuSIASEM provides a means of testing the viability and feasibility of envisioned metabolic patterns so that a trade-off matrix can be ascertained for decision and discussion support. The strength of MuSIASEM lies in the bioeconomic formalization of both societal and ecosystem metabolic patterns, allowing researchers to project trade-offs regarding implications for socioecological fund-flow elements and associated indicators of performance. Whether or not proposed trade-offs are necessary or acceptable is ultimately a social and political decision, subject to imbalances in stakeholder representation and power. MuSIASEM is not meant to be prescriptive regarding which scenarios should be promoted; rather the goal is to produce a holistic picture of scenario implications to illustrate sub-optimality inherent to the decision space. The ecosystem approach, however, provides a much needed collaborative and participatory framework for promoting ongoing sustainable management that is consistent with a diverse range of societal goals. The following section will describe how this methodological partnership might function.

2.1: The Ecosystem Approach to Health

Ecohealth adopts a post-normal approach to addressing complex problems, meaning that the role of the researcher is not to provide concrete predictions of deterministic outcomes but rather to generate narrative descriptions of how the future might unfold. The process for doing so is encapsulated within the “diamond heuristic” (fig. 17), so named for the prominent diamond shape in the centre in which participants are asked “which self-organizing entities do we want to encourage?” Before this question can be asked, however, Kay suggests that a series of preanalytical steps must be taken. First, a situation or a problem must be identified and contextualized. This requires two processes working in tandem: 1) developing a SOHO (self- organizing, holarchic, open system) description of the society in question along with its ecological context, and; 2) the development of an “issues framework” in which problems are related to stakeholders and institutions.

The process of generating a system description requires dialog between traditional science and complex systems theory. As James Kay was a central figure in the development of the Ecosystem Approach (which formed the basis for the Ecohealth Approach), his thermodynamic understanding of SOHO dynamics necessarily informs the process of generating a system description. The goal of the upper left quadrant of the diamond heuristic is to determine relevant indicators and state variables which can be helpful in discerning attractors, autocatalytic processes and feedback loops. Once a SOHO description is established, it becomes possible to determine the range of “ecological possibilities” within which scenarios can be developed. MuSIASEM employs similar methods for developing hierarchic and non-linear descriptions (grammars) of societal and ecological systems and can, therefore, be implemented within the upper-left quadrant of the diamond diagram to supplement Kay’s “attractor” approach to sustainability assessment.

As noted, the diamond heuristic does not call for specific protocols, although it does prescribe that systems approaches and collaborative processes be employed. Bunch (2003) has suggested the use of Checkland’s soft systems methodology for the process of developing an issues framework (the upper right hand quadrant). In order to define the range of agents,

70 stakeholders and perspectives relevant to a given issue, Checkland and Scholes’ CATWOE mnemonic can be employed (Checkland and Scholes 1990, ph. 35; also see Allen and Hoekstra 1992):

• C: Customers: referring to either “victims or beneficiaries” of T, • A: Actors: “those who would do T”, • T: Transformation Process: “the conversion of input to output”, • W: Weltanschauung: “the worldview which makes T meaningful in context”, • O: Owners: “those who could stop T” or “who or what can close the system down”, • E: Environmental constraints: “elements outside the system which it takes as given”

Waltner-Toews has further suggested the application of an Adaptive Methodology for Ecosystem Sustainabiiity and Health (AMESH, fig. 18), which is based on the following components in addition to Kay’s SOHO framework:

“1) The situation is brought to someone's attention, often because the local people, researchers, or some third-party agency perceives a problem. 2) The "responders" attempt to understand the situation systemically by incorporating a variety of multiscalar social and ecological perspectives. 3) Some combination of local stakeholders and researchers identifies system-based alternative courses of action at various scales and from various perspectives. 4) Stakeholders choose a course of action, develop a plan that incorporates a collaborative learning system, begin implementation, and ensure that governing, monitoring, and management co- evolve with the changing situation. 5) Outside investigators have the responsibility to try to understand the system, the process, and how the process interacts with, and perhaps determines, our understanding” (Waltner-Toews and Kay 2005, pg. 9).

Figure 17: The Diamond Heuristic depicting the general process for an Ecosystem Approach based on complex systems thinking and collaborative processes. Source: Kay et al. 1999

Figure 18: Waltner-Toews’ Adaptive Methodology for Ecosystem Sustainability and Health (AMESH). Source: Waltner-Toews and Kay 2005

Once relevant agents and processes are initially identified, stakeholders are invited to participate in discussion workshops garnered towards developing a more nuanced and multidimensional understanding of the problems. The Ecohealth Approach does not call for specific protocols to be employed toward this end and many types of methods can be employed for fostering discussion. However, as general guideline, Kay and Waltner-Toews call for approaches which are both collaborative and systems-based. Participants in workshops conducted by Bunch (Bunch 2008; Bunch 2003), for example, were asked to produce “rich pictures”; another conceptual modelling tool from Checkland’s SSM. These exercises allowed participants to explore the full range of problems from the perspectives of other stakeholders. Further, Bunch observed that “Upon reflection, participants tended to indicate that their initial concerns… were actually symptoms of problems rooted in the political, social, and management realm” (Bunch 2008 pg. 159). Through workshopping and rich picturing, participants are able to re-examine their initial understandings as coupled to processes as higher levels of governance and scale. These exercises serve the dual purpose of 1) allowing researchers and participants to gain a greater appreciation for the sheer complexity of the issues at hand, while; 2) allowing participants to perceive of general trends so that the root sources of problems can be identified. This illustrates the iterative nature of the ecosystem approach; initial question “what are the problems?” can be distilled into “Really now, what is the problem?” (Bunch 2008, pg. 159).

These exercises allow participants to voice preferences and negotiate visions of mutually desirable futures. Concurrently, these negotiations are considered while researchers and scientists attempt to discern the range of ecological possibilities. Research and discussions are then synthesized into a range of desirable future scenarios which participants are then able to select from (the center diamond) and then begin to envision what would be required in order for these futures to be realized. The center of the diamond heuristic is therefore committed to practical questions concerning technical resources, infrastructure and expertise as well as political context, greater awareness and engagement. The research process itself is therefore intended to inform decision making and transform into an ongoing process of adaptive management. As new issues begin to emerge, the research process is started anew.

2.2: Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM)

Daly, in his article, Filters Against Folly (1987), begins by offering three propositions which are useful to remember when considering the issue of choice within finite biophysical contexts. In the spirit of MuSIASEM, I propose to re-postulate Daly’s propositions and offer four instead:

1) Not everything is possible, 2) Not everything possible is biophysically feasible, socially viable, or subjectively desirable, 3) Not everything that is both possible and desirable is feasible and/or viable, 4) Not everything that is feasible and/or viable is also desirable.

Despite wishful thinking, good intentions and careful planning, issues relating to sustainability and human wellbeing invariably boil down to the complex nexus of constraints that are imposed by this reality. MuSIASEM is about mapping that nexus.

Giampietro describes MuSASIEM as “quantitative storytelling” (Chifari et al. 2016). Like Ecohealth, practitioners of MuSIASEM firmly insist that the analyses generated by the methodology do no constitute operating models which provide specific predictions. Rather, MuSIASEM is about assessing constraints which are imposed on the option space of proposed scenarios. Although both the tool-kit and the methodological process of MuSIASEM have been well articulated elsewhere (Mario Giampietro et al. 2014), a cursory review of the methodology’s basic steps will be provided in the following sub-sections.

2.2.1 Fund-Flow Analysis in MuSIASEM

Quantitative assessment in MuSIASEM is made possible by operationalizing Nicholas Georgescu-Roegen’s bioeconomic fund-flow accounting framework. At first glance, bioeconomics is evocative of the better-known system dynamics modeling - employed by Jay Forrester of MIT as well as Donella and Dennis Meadows of the Sante Fe Institute5 - in the sense

5 In point of fact, Georgescu-Roegen’s “bioeconomics” was developed prior to the Santa Fe Institute’s use of SDM in the Limits to Growth report. In private correspondence, Dennis Meadows credited Georgescu-Roegen as “a substantial influence on the thinking of the members of my group” (Bonaiuti 2007, pg. 221).

that both account for categories of “stocks” and “flows.” However, while SDM focuses on only two categories, bioeconomics includes “funds” as a third. Georgescu-Roegen defined these identities as follows (Georgescu-Roegen 1971; Giampietro and Mayumi 2009):

• Flows: “Flow elements are those that are either produced or consumed during the analytical representation (they reflect the choice made by the analyst when deciding what the system does and how it interacts with its context). Flow elements can be described in terms of relevant monetary, energy and material flows” (Giampietro and Mayumi 2009, pg. 309). • Funds: Funds are defined as "agents" that act upon the production process but remain the same in the sense that they are never incorporated into the final product. • Stocks are defined as "reservoirs or buffers of flows" which can be "depleted or filled" over time. Flows can then be further categorized according to their source: • Stock-flows, referring to flows from stocks. • Fund-flows: flows from funds that do not result in "a change in the identity of the fund element". • Imported/exported flows: flows that are imported from or exported beyond the system boundaries.

Examples of fund elements used in MuSIASEM include “population, work force, technological capital, managed land, and total available land”. Examples of flow elements include “food, energy, water and money” (Giampietro et al. 2014). Stocks are non-renewable; exhaustible like “beer from a barrel.” Examples of stocks include water in aquifers, mineral deposits or fossil fuels in oil fields. Milk from a cow would therefore be considered a “fund-flow” whereas oil from an oil field would be considered a “stock-flow.” Imported-flows become necessary when a societal system is unable to generate its own required flows, or when a system has a sufficient surplus of flows which can be traded across system boundaries. Food shipped to urbanized islands (such as Oahu or Great Britain) is a very clear example of “imported flows”, one which

illustrates the inability of those systems (due to high population in proportion to available land) to generate an autonomous food system.

Figure 19: Examples of flows and funds – Simple “cow grammar”

Figure 19 illustrates a simple demonstration of the fund-flow concept; a “cow grammar”. Here, solar energy flows to the Earth and is absorbed by the soil. The soil then converts the energy into standing biomass. Given adequate energy and nutrients, the soil can continually produce grass so long as it is not overconsumed to the point of soil erosion. Thus, the soil serves as a fund of grass, and the grass can be considered a “fund-flow” for the cow. The cow’s identity is now arbitrary, as Silva-Macher and Farrell note “an element’s identity, as either a flow or a fund, is process specific” (Silva-Macher and Farrell 2014, pg.751). In other words, we could choose to milk the cow or butcher it for beef. Dairy is a renewable resource, so long as the cow is well kept and not over-milked. Because the cow remains more or less the same throughout the process of producing milk, we can identify dairy cow as a fund and the milk it produces as a fund-flow. Because butchering the cow would irreversably exhaust its productive capacity (i.e. it can only be butchered once) we would consider a beef-cow to be a stock and the beef itself

to be a stock-flow. Further, in the interest of discerning feedbacks, one might also view the cow as a fund producing flows of excrement necessary for nurishing the soil. Finally, it should be noted that Georgescu-Roegen’s fund-flow framework was specifically designed to address the issue of scale. While the cow here appears as a stock or fund, the cow could also be considered a flow over a larger scale. Cows (plural) are, after all, a fund producing a flow of more cows.

2.2.2: Hierarchies of Societal Compartments

Once flows, funds, and stocks are defined, the system must then be described in terms of its composition of functional compartments at multiple scales. Each compartment is made up of a portion of the total system fund elements that produce an output of flows for other compartments in the system. Similarly, each compartment requires an input of flows in order to function. Although compartment boundaries can be subjectively defined by the observer, MuSIASEM researchers often use a similar set of categories to describe the metabolic pattern of societies: whole society (WS); households (HH); paid work (PW); building and manufacturing (BM); energy and mining (EM); services and government (SG); and agriculture (AG) (Giampietro et al. 2014).

Figure 20 depicts an example of a hierarchy of functional, societal system compartments commonly used in MuSIASEM. Here, MuSIASEM operationalizes Ulanowicz’ theory of hypercycles and further subdivides the hierarchy into “dissipative” and “hypercyclic” compartments. A “hypercyclic” functional component is defined as one which “drives the functioning of the entire system by generating the required flows (food, energy, water), technology and infrastructure for itself as well as for the rest of the system.” A dissipative component “consumes the surplus of resources provided by the hypercycle and is responsible for the reproduction and adaptivity of human activity, thus holding together the entire system” (Giampietro et al. 2014, pg. 13).

Figure 20: Hierarchy of societal system components. Source: Mario Giampietro et al. 2014.

These “functional compartments” can be quantified in terms of their allocated share of three types of societal fund elements: human activity (HA), power capacity (technical capital, PC) and managed land (ML). For example, the active capacity of the people living at the level of the “whole society” (level n) is represented in MuSIASEM as “total human activity”, a productive fund. Total human activity (THA) is calculated by simply multiplying the population by the number of hours in a year:

= × 8,670 /

When suitable, the equation푇퐻퐴 for THA푝표푝푢푙푎푡푖표푛 can be expanded toℎ include표푢푟푠 푦푒푎푟 the human activity of tourism as well. The total human activity of the paid work sector (level n-1) is approximated by examining the “dependancy ratio”; demographic figures such as working age, retirement, and employment rate. The total fund shares allocated to the functional compartments must “provide closure at all levels”, meaning that the sum of human activity distributed across the

two compartments at level n-1 (households and paid work) and below must equal that of level n (THA):

= +

퐻퐻 푃푊 Thus, the THA of the paid work sector푇퐻퐴 minus퐻퐴 the demographic퐻퐴 overheads (youth, old age, unemployment, etc) will be counted toward the household (HH) compartment, representing leisure time, household management and self-care. Because HH is a dissipative component, essential to the reproduction of the system, the THA allocated to it cannot fall below a certain threshold if the metabolic pattern is to remain viable. An example of an inviable pattern of human activity could represent a situation in which the population in a condition of severe “time poverty” in which too much human activity is allocated to the paid work sector, leaving very little to be allocated to households. Another inviable pattern could be represented in a situation in which children or the elderly are forced to work in order to supply the hypercyclic surplus necessary to provide goods and services in a society.

The fund-share characteristics of each and every system compartment are then represented in dendrograms. This provides a complete picture of total throughput of each flow category: energy throughput (ET), water throughput (WT) and food throughput (FT). Fund categories are represented as capacities: human activity (HA), power capacity (PC) and managed land (ML). Total throughput and capacity are represened for each hierarchical level and for each functional compartment as the dendrogram bifurcates downward.

Figure 21: Example dendrogram of Societal Funds and Flows Source: Giampietro et al. 2014..

2.2.3: Multi-Purpose Grammars

MuSIASEM employs a unique tool, “multi-purpose grammars”, to represent patterns of flows being generated or consumed by functional compartments made up of funds. A grammar, in MuSIASEM, is a defined as “a formal system of rules for accounting for metabolic flows [that] identify and characterize the respective flows across the various compartments of society in terms of both quantity and quality” (Giampietro et al. 2014, pg. 15). Whereas in linguistics the term “grammar” refers to the "set of rules defining what constitutes the basis and how to organize the spoken language to link in an effective way semantic to syntactic statements", multi-purpose grammars used in MuSIASEM are "associated with any meta-system of accounting based on a flexible network of expected relations between semantic categories (e.g. relevant attributes of sustainability) and formal categories (names-indicators) generated by production rules applied to gathered data (tokens)" (Giampietro, Mayumi, and Ramos-Martin 2009, pg. 319). Constructing a grammar requires:

“a pre-analytical choice of: 1) a lexicon – the typology of categories associated with the chosen narratives; 2) a vocabulary – the list of semantic and formal categories

used in the representation; 3) a list of expected relations over the various semantic categories; and 4) a set of production rules determining a given formal representation of these relations…” (Giampietro et al. 2014, pg. 223)

This process is related to formal language theory as well as Rosen’s concept of the modeling relation. As with any formalization, grammars, in order to be meaningful, must be encoded and “implemented through a set of semantic decisions about the choice and use of data" (ibid, pg. 223). As such, grammars are non-equivalent; a water grammar will obviously follow a different set of rules than an energy or food grammar. In other words; they are subjective, quantitative narratives depicting the direction of flows in a metabolic pattern.

Figure 22: Example of a food grammar. Source: Giampietro et al. 2014.

Figure 22 depicts a “food grammar”; a formalization of food production and consumption across a society. Here, the semantic categories (e.g. local production, domestic supply, vegetable products, urban end uses, etc.) are connected by syntactic strings (arrows) representing biophysical flows. Gross supply of vegetables and animal protein are initially quantified in tonnes as they enter the system from imports and local production. Losses are incurred through processing as they are made available for human consumption. Available energy resources in MuSIASEM (such as food or fossil fuels) are referred to as “energy carriers” and here they are further qualified in terms of carbohydrate, protein and fat content. Further losses are acrued through distribution and food-flows are finally consumed by end uses; functional compartments are made up of funds (human activity, in this case). A portion of the net food supply (seed, feed and eggs) must be reinvested in local production in order to raise livestock and sew fields. These flows are referred to as the “internal autocatalytic investment”, i.e. the flows necessary to sustain the hypercyle. Likewise, portions of certain funds (such as human activity, water bodies and hectares of managed land) must be invested in local agricultural production as well. The energy grammar in figure 23 behaves similarly to a food grammar in that energy must be reinvested in the energy and mining sector to provide a surplus for other end uses.

Figure 23: Example of an Energy Grammar. Source: Giampietro et al. 2014.

2.2.4: Intensive and Extensive Variables

Once MuSIASEM has defined a hierarchy of functional compartments and a grammar of expected relations it then becomes possible to calculate intensive variables, such as “rates, densities [and] intensities” that describe performance (Giampietro et al. 2014). These “flow/fund ratios” are calculated by comparing extensive fund values to extensive flow values for various functional compartments. Figure 24 is a dendrogram comparing the relative size of the human activity fund to the energy throughput flow on a per capita, per year basis at various levels in a given society. For example, the human activity of the household (HH) sector comprises 90 percent of total human activity at 7,900 hours per year while the exosomatic throughput (ET, i.e. non-food related energy) of households accounts for thirty percent of total

energy throughput (TET) at 60 gigajoules. The flow/fund “energy metabolic rate” (EMR) of the household sector is calculated as:

÷ =

퐻퐻 퐻퐻 60 ÷퐸푇 7,900 퐻퐴 = .00759퐸푀푅

Thus, the EMR of the household퐺퐽 sector is roughlyℎ푟푠 8 MJ per퐺퐽 hour,푝푒푟 perℎ푟 capita.

Figure 24: Dendrogram depicting disaggregated societal compartments with extensive fund-shares and intensive flow-rates. HH: household sector; ET: energy throughput; PW: paid work sector; SG: services and government sector; SG: services and government sector; PS: primary and secondary flow sector; THA: total human activity. Source: http://www.nexus- assessment.info/methodology/how-it-works

This type of analysis is scalar, meaning that it can be aggregated to the level of the whole society or disaggregated to smaller compartments. What is important is that the numbers be consistant throughout scalar levels so that the sum of extensive or intensive values at lower levels is equal to that of the aggregated whole (e.g. + = and +

= ). Further, these assessments can be used퐻퐴푆퐺 to generate퐻퐴푃푆 a퐻퐴 profile푃푊 of socio퐻퐴푃푊- 퐻퐴퐻퐻 푇퐻퐴 85

economic systems based on intensive and extensive varaible characteristics, allowing MuSIASEM to classify them into types. Giampietro proposes that agroecosystems, for example, can be classified as “low-external-input agriculture” (LEIA) or “high-external-input agriculture” (HEIA) depending on their “degree of openness”, i.e their reliance on imported energy, materials or food (Giampietro 2003; Giampietro et al. 2014).

2.2.5: Impredicative Loop Analysis

“It is important,” Giampietro et al. argue, “to keep in mind that the various fund and flow elements affect each other at different scales and in relation to different dimensions. This reflects the typical impredicative causal relation found in autopoietic systems” (Giampietro et al. 2014, pg. 18). As discussed in section 1, societies and ecosystems can be classified as causally closed, autopoietic unities which organize around autocatalytic loops. The identity of an autopoietic system can be defined differently depending on the scale of observation. In the large scale view (top down) the "organization...of the whole" determines "the proper functioning and reproduction of the parts", whereas in the bottom up, small scale view it appears that "identity of the parts that determines, through their interaction, the reproduction of the identity of the whole" (Giampietro et al. 2014, pg. 34).

MuSIASEM employs a tool known as “impredicative loop analysis” for simulating scenarios which alter fund or flow variables across scales. Impredicative loop analysis (ILA) tests for congruence between system requirements and the generative capacity of functional compartments. Congruence in these analyses is related to the concept of viability adopted by MuSIASEM. Here, viability is distinct from feasibility in that it is concerned with the internal constraints imposed from within the system itself. Feasibility, to be discussed in later sections, refers to boundary conditions (i.e. the external constraints beyond human control).

A metabolic pattern is viable if, given the structural constraints (the size and characteristics of fund elements), it is capable of generating an adequate internal supply of the various flows it consumes (food, energy, manufactured goods,

infrastructures, services). Viability is checked by cross-verifying the stability of the dynamic budgets of the individual flows (food, energy, water, money) in relation to internal constraints. For example, the total flow of food consumed by society must be provided by the agricultural sector or by imports (Mario Giampietro et al. 2014 pg. 19).

Figure 25: An example of impredicative loop analysis depicting a shortfall in the cereal production necessary to operate a small farm. Source: Madrid López 2014, pg. 119.

Impredicative loop analysis uses four-quadrant charts to test congruence between a set of forced relations. The example of an ILA in figure 25, provided by Madrid-López (2014), simulates cereal production necessary to operate a small farm. The right extent of the x-axis represents the extensive-flow variable (e.g. total cereal consumed within a given society) whereas the top extent of the y-axis represents an extensive fund variable (total farm land within a society). Both variables are expressed at the level of the whole system; level n. The δ angle is the intensive flow-fund ratio that tells us the rate at which a society must produce cereal in order to satisfy consumption. The left extent of the x-axis is the extensive fund value

for the functional compartment “productive land” at level n-1. The α angle is a fund-fund ratio which expresses the proportion of total farm land which can be classified as productive land versus land reserved for other uses (including conservation land or pasture). The bottom extent of the y-axis is the extensive fund-value for “land producing cereal” and the β angle shows the proportion of land cereal producing land versus vegetable producing land. The ƴ angle represents a “threshold value”, i.e. the value necessary for a given functional compartment at a lower level to satisfy the requirements of the whole system. In this case, current cereal production of 2530 kilograms per hectare is insufficient to satisfy the total cereal requirements. This metabolic pattern is not viable. The farm is incapable of providing enough cereal necessary for human and animal consumption and must, therefore, rely on imports to overcome the shortfall.

ILA is versatile in that it can be used to discern threshold values and test congruence between any production and consumption of relevant flows (based on specific fund compartments) in a society. Total GDP as a flow of economic activity, for example, must be produced by the paid work sector which, in highly developed countries, accounts for less than 10% of total human activity. Total exosomatic throughput, as illustrated in the energy grammar, must be produced by the energy and mining sector. ILA also makes it possible to simulate viable solutions. In the case of the farm in figure 25, scenario analysis might simulate assigning more land from “other land uses” to the production of grain. On the other hand, this proposed change may impact the farm’s capacity to raise livestock or conservation green space. How then might this affect the farm’s profits, soil erosion, the water table or biodiversity? Conversely, how might cereal production be affected if we wished to optimize greenspace? The farm might become even less viable and reliance on imported grain might increase. Trade-offs are inevitable and the purpose of ILA is to serve as a useful tool in determining what consequences a given scenario might entail.

2.2.6: The Sudoku Effect

In addition to ILA, MuSIASEM relies on the concept of the “Sudoku effect” in order to analyze the “viability-space” of metabolic patterns before scenario writing takes place. Giampietro et al. posit that the option space of viable metabolic patterns in socioecological systems is analogous to the constrained option space of numerical patterns in a game of Sudoku. In this process the multi-level matrix (developed in the process of generating the system’s grammar and hierarchical description) serves to provide mutual information similar to the cells in a Sudoku grid. In the same way that a completed Sudoku must be congruent in terms of the rules of the game (i.e. no number can appear twice within the same horizontal/vertical line or internal 3x3 grid) the multi-level matrix in MuSIASEM must be congruent in terms of the impredicative relations of flows and funds between functional compartments as determined by the syntactic rules laid out in grammars. Just as the entry of new numbers within a Sudoku grid constrains the option space of the game in terms of which new numbers can be entered, the option space of the multi-level matrix is 1) vertically constrained by extensive fund or flow variables; 2) horizontally constrained by the need to maintain the intensity of flows, and; 3) internally constrained by the hypercyclic characteristics of the metabolic pattern. A key assumption in this process is that the multi-level matrix is subject to the condition of closure in the same sense that Sudoku is a zero-sum game.

Figure 26: Multi-level matrix depicting the Sudoku Effect

2.2.7: Bioeconomic Pressure as an Indicator of Societal Desirability

The multi-level matrix in figure 26 depicts energy use across hierarchical levels in Spain. In a food scarcity scenario (entailing a reduction on imported food) this pattern could be adjusted by diverting a proportion of the production factors to the agricultural sector to increase yields. Because the production factors are limited (vertical constraints), this adjustment will necessarily require a reduction in the productive capacity of other functional compartments. Beyond a certain threshold, these compartments may lose functionality due to internal constraints. The corollary to this is that the society itself must adhere to human expectations concerning its performance in providing well-being, leisure and a material standard of living. MuSIASEM employs Georgescu-Roegen’s concept of “bioeconomic pressure” (BEP) as an indicator of desirable societal system performance. BEP is defined as follows:

“An indicator of the material standard of living and hence of the desirability of the metabolic pattern. The stronger the BEP in a society, the larger the share of production factors that ought to be allocated to the dissipative macro- compartment. It is formalized as the ratio of the total amount of production factors (fund and flow elements) of the society over the amount of production factors (fund and flow elements) allocated to hypercyclic compartments (the larger the share in the dissipative compartment, the smaller the share in the hypercyclic compartment)”. (Giampietro et al. 2014)

In terms of energy use, bioeconomic pressure is calculated as (Giampietro, Mayumi, and Sorman 2011):

= / × / × /

푃푊 푃푊 푃푆 퐵퐸푃For this 푇퐸푇equation,푇퐻퐴 the푇퐻퐴 total exosomatic퐻퐴 퐻퐴 throughput퐻퐴 (TET) variable could be substituted for other types of flows, such as total water throughput (TWT) or total food throughput (TFT). The equation used to calculate BEP can be broken down into a series of relations which are themselves indicators of development. Total throughput (TET, TWT, TFT) divided by total human activity yields the total “metabolic rate” for level n, the level of the whole society (SA). These values are represented as , , or respectively. As an alternative,

energy metabolic rate ( ) can퐸푀푅 be푆퐴 substituted푊푀푅푆퐴 with퐹푀푅 the푆퐴 dimensionless proxy variable, endo/exo, defined as “the퐸푀푅 ratio푆퐴 between the pace of exosomatic metabolism and the pace of endosomatic metabolism in a given society” (Giampietro, Mayumi, and Sorman 2011). MuSIASEM employs BEP, and its associated relations, as alternatives to gross domestic product (GDP) as an indicator of material well-being within a society. Extensive empirical analysis conducted by Giampietro et al. have even concluded that BEP is highly correlated to other measures/indicators of development such as the human development index (HDI) or GDP per capita.

2.2.8: Feasibility and Ecological Indicators of Desirability

Feasibility, in MuSIASEM, refers to the set of “external constraints” to which a societal metabolic pattern is subject. In other words, determining external constraints requires examining a system’s boundary conditions and its relationship with higher holarchic levels. For societies, the embedding holon will always necessarily be the environmental context that provides both source and sink capacity. In congruence with the philosophical and theoretical bases for MuSIASEM, embedding ecosystems are themselves recognized as dissipative systems with distinct metabolic patterns. MuSIASEM determines feasibility by examining the extent to which the metabolic patterns of various societies 1) rely upon and exploit provisional ecosystem services, and 2) disrupt the metabolic patterns of embedding ecosystems through pollution of harmful substances. To achieve this, MuSIASEM first calculates the gross requirements of biophysical flows (e.g. food, energy or water flowing into a society) as well as the waste flows flowing out of a society. In keeping with the bioeconomic accounting framework adopted by MuSIASEM, provisional ecosystem services (e.g. energy, food or water) are conceptualized as flows provided by ecological fund elements such as land, soil, biodiversity or natural water bodies. A socio-economic system with requirements that are beyond the provisional capacity of their embedding ecosystems is deemed infeasible. Indeed, in this sense, only low-external-input agrarian (LEIA) or traditional hunter-gatherer societies could be considered to strictly feasible. High-external-input-agriculture (HEIA) or urban societies are infeasible by definition due to a high demand for biophysical energy and materials that outpaces the local productive capacity. HEIA societies, Giampietro posits, “exist only by the grace of external inputs, the purchase of which is financed by selling the produced flows on the market” (Giampietro et al. 2014, pg. 48)

It is worth noting that even low-external-input societies do not constitute “steady state” societies in the sense that they are indefinitely feasible. As with the cow in figure 19, ecological flows can only be maintained so long as the exploitation is not such that the fund itself will lose integrity. It is possible (and historically common) for socieities to undermine the integrity of embedding ecosystem funds to the extent that the ecosystem is no longer able to support the

society. MuSIASEM also defines the “desirability” of scenarios by examining the degree to which ecosystem metabolism is disrupted through environmental loading. This is accomplished by comparing the metabolic characteristics of altered ecosystems with the benchmark- characteristics of unaltered ecosystems of the same type. MuSIASEM then operationalizes Kay and Schneider’s thermodynamic understanding of ecosystem integrity to develop indicators based on the required thermodynamic costs associated with ecosystem self-organization (Giampietro et al. 2014).

In keeping with systems ecology, Giampietro et al. reason that “undisturbed ecosystems maximize their level of energy dissipation by sustaining as large an amount of standing biomass (SB) as possible at the lowest possible thermodynamic cost” (Giampietro et al. 2014, pg. 41). As evapotranspiration can be considered the primary “thermodynamic cost” of self-organization in terrestrial ecosystems, MuSIASEM calculates “negentropic cost” (NEC) by dividing plant-active- water-flow (PAWF) per kilogram of standing biomass. Here, PAWF is considered a flow and standing biomass is considered a fund. Once again, a flow/fund ratio, that characterizes the metabolic requirements of ecological systems, can be derived. NEC is then coupled with a suite of other related ecosystem health indicators such as gross primary production, net primary production and respiration rate (autotrophic respiration/ gross primary production).

This process is most relevant when focusing on the rural component of socioecological systems as agriculture is a form of ecosystem exploitation. In general, these examinations illustrate a much higher negentropic cost associated with industrial monoculture than with low-impact organic agriculture (Giampietro et al. 2014). Specifically, the negentropic costs associated with certain types of crops can be compared to determine the feasibility of a region or country’s crop mix with respect to land and water use. GIS data can be used to determine the change in evapotranspiration between unaltered ecosystems and managed land associated with a specific crop. Alternatively, the evapotranspiration of one crop type might be compared with another. In doing so, the change in evapotranspiration between land uses can allow us to extrapolate resulting changes in water requirements between land-use types.

MuSIASEM further considers the potential stress placed on structural and functional ecosystem fund components (such as water bodies) or societal compartments as well. Figure 27 depicts a case study which compares the cubic water requirement (CWR) of sugarcane cultivation in Mauritius against the the possible culivation of food crops for the sake of food and water security as well as ecological integrity (Giampietro et al. 2014, chapter 12). In this particular case, MuSIASEM researchers determined that the “food-crop” scenario would be ecologically desirable due to less extensive exploitation of water funds and lower negentropic costs.

Figure 27: LU/LC scenario from Mauritius case study. Vector colors represent the cubic water requirements (CWR) of different crops and land uses. Adapted from Giampietro et al. 2013

Unfortunately, however, scenario 2.1 would also entail a significant reallocation of human activity to the agricultural sector to accommodate the increased labour requirements of a diversified crop portfolio. In this case, researchers discovered that the reallocation necessary would be inconsistant with the society’s distribution of human activity. The ecologically feasible/desirable option is not socially viable. These results further underscore the importance of the Sudoku effect in MuSIASEM and the puzzle-like nature of sustainability science .

The final consideration pertinent to our review of MuSIASEM is the methodology’s treatment of waste-flows:

“In the case of the release of harmful substances, the harmful flow as generated by a human activity categorized at a local scale can be identified, that is, an economic activity carried out in a sub-compartment of an economic sector. On the socioeconomic side this activity can be associated with a flow of money (e.g. profit, wages and investments), the requirement of fund elements (e.g. job and capital), and a given land use, all associated with these physical flows - e.g. the economic characterization of an industrial infrastructure with a textile plant dumping chemical substance into the near river. Then the density of the flow associated with this aspect of societal metabolism, expressed per unit of land use of the given category, represents the mechanism through which a bridge is established with ecological metabolism. In fact, when considering the amount of hectares in that particular land use (e.g. hectares for a textile plant) and knowing the expected load of chemical substances to be dumped in by rivers, it becomes possible to connect the socioeconomic attributes (flow of added value and the number of jobs generated by the plant) with the environmental load associated with its waste effluents” (Giampietro et al. 2013, emphasis added).

The effects of waste-flows on the quality of land can then be further tied to the integrity of ecological funds such as soil, biodiversity or water bodies. Once again, the desirability of the extent of environmental loading can be assessed through benchmarking against unaltered ecosystems. As stated, the most important aspect of this procedure in MuSIASEM is to “bridge” societal and ecosystem metabolism by directly relating ecologically undesirable waste flows (associated with certain activities and land uses) with socially desirable flows (e.g. jobs, goods, and income). This conceptualization represents a sort of ecological opportunity cost, i.e. what is lost when something else is gained.

2.3: Discussion: Land systems, ecosystem services and coupled human and ecosystem health

Land is the essential factor which allows MuSIASEM to define relations between societal and ecosystem metabolism so that a comprehensive scenario trade-off matrix can be developed. This section will demonstrate how MuSIASEM’s treatment of land systems can allow us to integrate a variety of ecosystem services, relevant to coupled human and ecosystem health, into the MuSIASEM accounting framework.

MuSIASEM represents land in four distinct aspects: 1) “a geographical area, defined by the boundary of the study area”; 2) a Ricardian production factor; 3) a set of geographically distributed human and environmental funds, qualities and processes, and; 4) “a geographical, place-based framework for an indexing system… formalized within GIS” (Aspinall 2014, pg. 49). Most relevant to the discussion here is aspect 3, which recognizes land as a form of natural capital and, thus, a fund component concurrently serving both societies and ecosystems (Daily, 1997; Daily and Matson 2005). The specific flows which are produced by land-systems depend heavily upon land use and land cover (LU/LC). Developed land can be considered a fund of socioeconomic goods and services; unmanaged land, high in ecosystem integrity and natural capital, can be considered a fund for producing flows of ecosystem services. Agricultural land, or other land types with multiple uses, could be considered either or both depending on the degree of ecosystem alteration and integrity (Aspinall and Serrano-Tovar 2014). The Millenium Ecosystem Assessment (2005) identifies four categories of ecosystem services:

• Regulating: “benefits obtained from regulation of ecosystems (e.g. climate regulation and water purification).” • Supporting: “services needed for the production of all other ecosystem services.” • Provisioning: “products obtained from ecosystems (e.g. food and water).” • Cultural: “non-material benefits obtained from ecosystems (e.g. cultural heritage).”

Figure 28: Ecosystem services as providing the constituents of well-being. Source: Duraiappah et al. 2005

The services associated with land are not exclusive to one or multiple land uses/cover, as land systems are capable of producing multiple if not all types of ecosystem services concurrently depending on their characteristics. In general, MuSIASEM only accounts for provisioning ecosystem services as flows of biophysical “metabolites” necessary for the metabolic viability of societies. However, in excluding other types of ecosystem services, metabolic assessments only provide a partial picture necessary for testing scenarios relating to human well-being.

Here, it should be noted that Giampietro et al. take care to point out that although MuSIASEM is extensive, it is also never claimed to be exhaustive in scope. The methodology, first designed to study societal metabolism primarily in terms of energy (Giampietro and Mayumi 2009; Giampietro, Mayumi, and Ramos-Martin 2009; Giampietro, Mayumi, and Ramos-martin 2007), has expanded over multiple iterations to include the accounting of many other societal and ecosystem flows (Giampietro et al. 2013; 2014). Recently, Aspinall has acknowledged that while MuSIASEM in its current form “focuses on food, water and energy, with certain environmental impacts also considered (for example greenhouse gas emissions)… other goods and services

could be added within the MuSIASEM resource accounting framework” (Aspinall, 2014, emphasis added). In light of this statement, it is clear that MuSIASEM should be further extended to account for regulating, cultural and supporting ecosystem services which play an important role in the impredicative loops that stabilize socioecological systems. The rationalization for doing so is partially provided by Aspinall:

“In the context of resource accounting for sustainability assessment, it is necessary to emphasize that continued societal access to ecosystem goods and services, such as food and water, is based on the persistence and resilience of land systems as functioning autopoietic systems encompassing stocks and funds of natural capital. Long-term sustainability of goods and services that arise from land systems is, therefore, not solely a property of land system functions based on biophysical system performance linked to biodiversity… but also fully reliant on a hypercycle which maintains land as a fund element, the hypercycle in most land systems being dominated by the human sub-system” (Aspinall 2014 pg. 51).

“Additionally, it is important to emphasize that ecosystem goods and services obtained from land systems are not produced solely by the biophysical (environment) sub-systems, especially biodiversity; human sub-systems, and particularly land management activities, contribute to recovery and delivery of ecosystem goods and services… The recovery of ecosystem services through land management also has economic, social and environmental costs.” (Aspinall 2014 pg.53)

Aspinall’s statement provides a clear picture of the impredicative loop involving land funds in socioecological systems: land provides ecosystem goods and services (flows) which benefit societies in myriad ways; societal flows are then reinvested into land management, which ideally should allow the flow of ecosystem services to continue.

Currently, MuSIASEM quantifies the role that humans play in the socioecological metabolism by calculating the fund of total human activity and subdividing that fund according to various

“overheads” such as working age. Overheads effecting the distribution of human health are first determined by demographics. The dependency ratio is calculated according to age, unemployment and absence so that the human activity associated with non-working individuals is allocated to the household (HH) sector of the societal holon. These overheads are applied depending on basic human time requirements with respect to self-care, transportation or family activities. Once overheads are accounted for, we are left with the human activity which must be invested into the hypercyclic compartment. Poor health can have a significant impact on the allocation and productivity of time spent in both household and productive categories. The combined costs of absenteeism and presenteeism (i.e. working through illness) to the U.S. economy has been estimated at $576 billion per year (Klachefsky 2012), representing a significant overhead on the hypercyclic component of the societal metabolism. That is to say nothing of the effects of illness on the functioning of households; the social, emotional and cultural costs of lost wellbeing. Illness, thus, constitutes an overhead on both productive and reproductive aspects of society. Large scale disruptions to the health status of the population (such as disease outbreak, food and water contamination or epidemics of chronic illness) effect the fund of human activity and can therefore represent a threat to the very viability of social systems themselves. Ecosystem services are a mitigating factor in all of these cases. The recent Ebola outbreak in west Africa - which has been linked to destruction of terrestrial ecosystems - is a clear example of the ways in which disease can disrupt the functioning of society and how ecosystem services can mitigate or prevent these disturbances.

Figure 29 depicts the cycle of health, society and land management. Multiple impredicative loops can be discerned within this conceptualization: structural ecosystem funds provide flows of provisional ecosystem services to the whole society as well as other ecosystem services which maintain the health of humans; the societal holon processes these flows and in turn provides market goods and services necessary to maintain high standards of living; healthy and well-provisioned humans provide activity to maintain society; society then provides effective land management to maintain the ecosystem. If any of these holons are compromised, then so too is the entire system. Consider poverty as an example; when societies are unable to provide material standards of living, decision makers might resort to unsustainable land management

to bolster exports. When this occurs, ecosystems are degraded, and negative health outcomes can invariably ensue. As population health suffers, poverty is exacerbated (Gatzweiler and Baumüller 2014).

Figure 29: Diagram depicting the impredicative relationship between three nested holons: ecosystem metabolism, societal metabolism, and human health.

It is not difficult to conceptualize supporting, regulating and cultural ecosystem services as flows. Accounting for them, however, is another matter. How does one quantify, for example, the value of cultural ecosystem services? One method for addressing this might be to account for both the market and non-market benefits of ecosystems services through a combination of quantitative and qualitative assessment. Following Aspinall, quantitative assessment of benefits can be accomplished by integrating the association of land use and land cover (LU/LC) with ecosystem services in similar fashion that MuSIASEM uses land use to associate societal flows and waste flows.

Qualitative valuation, on the other hand, is something that MuSIASEM is not designed for. While monetary benefits can be easily accounted for, qualitative benefits can vary depending

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on different stakeholder’s associations with ecosystem services. Tuvendal and Elmquist note that “a key for successful adaptive comanagement is to create a positive feedback loop, i.e., a mechanism for progressively improving the situation” and therefore “engagement in appropriate processes, e.g., deliberation of various sorts, by stakeholders is paramount to effectively manage the sustainable generation of ecosystem services” (Tuvendal and Elmqvist 2011). Qualitative valuation should be therefore carried out with stakeholder participation in order to account for these imbalances as well to foster a sense of ownership over the process. In order to integrate the full range of ecosystem services into our analyses we must turn to the ecosystem approach as an extension to MuSASIEM. It also now becomes necessary to revisit the way in which MuSIASEM determines the desirability of scenarios. In light of the discussion from section 1, MuSIASEM’s convention of calculating desirability through bioeconomic pressure and negentropic cost could be considered ill-advised, which is not to say that these indicators are not useful. Rather, NEC and BEP could be more appropriately considered as indicators of adaptive capacity. Desirability, like optimality, is subjective and determinations as to the desirability of socioecological configurations should be negotiated rather than calculated.

In a study on qualitative valuation of ecosystem services and issues relating to river brownification, Tuvendal and Elmquist have suggested the application of scoping exercises and a structured interview process oriented to “[discovering] (1) the respondents’ operation, experience, and local ecological knowledge, (2) the consequences of brownification […] and (3) strategies to cope with this disturbance” (Tuvendal and Elmqvist 2011). These sorts of exercises can be conducted in the process of developing an issues framework (the upper right-hand section of the diamond heuristic) the results of which can then be fed back into MuSIASEM so that they can be contextualized within a set of performance indicators to produce multi-criteria analyses depicting environmental impacts and trade-offs.

Typically, MuSIASEM employs multi-objective integrated representations (MOIR) that concurrently depict the status of variables, relevent to a given problemshed, within a muli- criteria, multiple scale performance space (see figure 30). Because many of these variables are tied together in one way or another, it is possible to simulataneously simulate scenarios in which the alteration of certain variables will entail consequences for others (Gomiero and

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Giampietro 2005). The diagrams themslves are often referred to as “spider”, “bullseye” or “amoeba” diagrams. The concentric circles moving outward display the degree of variable performance depending on benchmarks of feasibility, viability or desiarbility (i.e. BEP and NEC.) As an alternative approach, I suggest that the multi-criteria assessments carried out by MuSIASEM should be extended to include qualitative valuations of factors which cannot be easily quantified. Doing so will allow the MCA itself to be framed to include a variety of perspectives which will reflect the fact that trade-offs will appear differently to different stakeholders or affected parties. Only then can a serious discussion on the desirability of scenarios be attempted.

Figure 30: Example of a multi-objective integrated representation. Source: Giampietro 2003.

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3: Conclusions

Section 1 provided a discussion on the theories relevant to understanding the nature of complex, self-organizing systems. In conclusion, it was argued that relational theory provides a number of insights which can be useful in developing more comprehensive approaches to issues relating to coupled human and ecosystem health:

1. Organisms, ecosystems, and societies are non-equivalent types within a specification hierarchy. All three are systems of metabolism and repair, but ecosystems are not organisms, and societies are not ecosystems. While similar patterns in growth and development are common to all three system-types, this should not be interpreted to mean that human societies are predetermined to collapse periodically in the same way as ecosystems. However, loss of functional diversity, degradation of admissible contexts, and unrestrained positive feedbacks are universally pathological. Political ecologists and systems theorists should, therefore, find common cause to resist the current hegemony of extractive capitalism which manifests in ecological degradation as well as social, political and economic oppression. 2. Models of optimality (i.e. concepts of health and wellbeing) are subjective and unique to different systems. Maintaining functional diversity requires fostering a diversity of perspectives in order to resist the excessive self-referentiality which precipitates the collapse of complex systems. Therefore, the challenge of sustainability, in the broadest sense, is not only to preserve the integrity of ecological contexts, but also to do so in a way that promotes human pluralism and mutually desirable outcomes.

Section 2 reviewed two methodologies that can be instrumental in envisioning and promoting sustainable and desirable societies. In light of the discussion from section 1, a series of further conclusions regarding these methodologies can be drawn:

1. Ecohealth, bolstered by heuristics such as the diamond diagram, the AMESH framework, and soft systems methodology, can be a useful approach for envisioning and promoting desirable scenarios in such a way that encourages and integrates a diversity of

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perspectives, including those which have been historically marginalized or excluded. Simultaneously, MuSIASEM - through impredicative loop analysis, multi-purpose grammars, multi-level matrices and multi-criteria analysis – can filter the option space so that unrealistic scenarios can be discarded. 2. In order to provide a more robust picture of human and ecosystem health, the multi- criteria analyses conducted through the MuSIASEM methodology should be extended to include qualitative indicators of ecosystem services, which can be achieved during the process for developing an issues framework within the Ecohealth Approach. Furthermore, MuSIASEM’s desirability indicators (BEP and NEC) should be reconceptualized as indicators of adaptive resilience and determinations as to the desirability of scenarios should be negotiated through participatory methods as well.

Invariably, the success of these measures will depend on the willingness of decision makers to compromise; not merely between different human aspirations, but also between the health and wellbeing of all autopoietic systems relevant to sustainability. Kay (2008) suggests that it is not ecosystems that we should seek to manage, but rather our own interactions with them. He further notes that “when a system is optimal, their components are themselves run in a suboptimal way” (Kay, Waltner-Toews, and Lister 2008, pg. 19). Sustainability is, therefore, not unlike tuning a drum or truing a bicycle wheel; more art than science. Balance can be achieved, but only through an equal respect and consideration for each component, on their own terms, in relation to all others. Only when we ignore this, do systems break.

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