Emergence in Psychology: Lessons from the History of Non-Reductionist Science
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What Is a Complex Adaptive System?
PROJECT GUTS What is a Complex Adaptive System? Introduction During the last three decades a leap has been made from the application of computing to help scientists ‘do’ science to the integration of computer science concepts, tools and theorems into the very fabric of science. The modeling of complex adaptive systems (CAS) is an example of such an integration of computer science into the very fabric of science; models of complex systems are used to understand, predict and prevent the most daunting problems we face today; issues such as climate change, loss of biodiversity, energy consumption and virulent disease affect us all. The study of complex adaptive systems, has come to be seen as a scientific frontier, and an increasing ability to interact systematically with highly complex systems that transcend separate disciplines will have a profound affect on future science, engineering and industry as well as in the management of our planet’s resources (Emmott et al., 2006). The name itself, “complex adaptive systems” conjures up images of complicated ideas that might be too difficult for a novice to understand. Instead, the study of CAS does exactly the opposite; it creates a unified method of studying disparate systems that elucidates the processes by which they operate. A complex system is simply a system in which many independent elements or agents interact, leading to emergent outcomes that are often difficult (or impossible) to predict simply by looking at the individual interactions. The “complex” part of CAS refers in fact to the vast interconnectedness of these systems. Using the principles of CAS to study these topics as related disciplines that can be better understood through the application of models, rather than a disparate collection of facts can strengthen learners’ understanding of these topics and prepare them to understand other systems by applying similar methods of analysis (Emmott et al., 2006). -
Environmental Influences: Family Systems Theory
Environmental Influences: Family Systems Theory Family Systems Theory provides a broad and comprehensive mechanism for understanding the core aspects of the Performance Competence Lifespan Framework — quality of life, member- ship, and a personal sense of competence. It also focuses on the most important component of environmental influences—home and family. From birth, a child’s Quality of Life is directly influ- enced by the kind of care, support, stimulation and education he or she receives from family mem- bers in the home. As infants begin to develop secure attachments with significant others, particu- larly family members, they begin to establish themselves as members of the first and most basic unit of society—the family, which forms the foundation for secure Membership in other groups throughout life. The infant begins to develop a Personal Sense of Competence when his mother responds consistently to his distress, when he takes his first step or says his first word, or when his father praises him for using the toilet. These early beginnings, then, are at the core of what each individual child will come to know and be able to do. As the PC Framework indicates, there are multiple environmental influences on performance and competence, but the family is the first and most important. The influence of family members on one another is not simple, but complex; it is not one-way, but reciprocal. The family, like a mechanical system, is made up of multiple parts that are interdependent. When one part does not function well, all other parts are impacted. Further, the family interacts with other systems, includ- ing those that provide direct services to the child—child care/preschools, schools and community agencies—and each system affects the other. -
Robustness of Multiloop Linear Feedback Systems
WA1 - 10:45 ROBUSTNESS OF MJLTILOOP LINEAR FEEDBACK SYSTEMS* J. C. Doyle Consultant Honeywell Systems and Research Center 2600 Ridgway Parkway Minneapolis, Minnesota 55413 ABSTRACT critical role in evaluating system robustness. The Bode plot or scalar Nyquist or Inverse Nyquist This paper presents new a approach to the diagram (polar plots of the loop transfer function) frequency-domain analysis of multiloop linear feed-provides a meansof assessing these quantities at back systems. The properties of the return a glance. For multiloop systems, individual loops difference equation are examined using the conceptsmay be analyzed using scalar techniques, but these of singular values, singular vectors and the methods fail to capture the essentially multi- spectral norm of a matrix.A number of new tools variable nature of manysystem. For example, for multiloop systems are developed which are scalar methods may ignore variations which analogous to those for scalar Nyquist and Bode simultaneously affect multiple loops. analysis. These provide a generalization of the scalar frequency-domain notions such as gain, There are a number of other possible ways bandwidth, stability margins and M-circles, and to extend the classical frequency-domain tech- provide considerable insight into system robust- niques. One involves using compensation or feed- ness. back to decouple(or approximately decouple) a multiloop system into a set of scalar systems which may be treated w#th scalar techniques (i.e., 1. Introduction "Diagonal Dominance", bsenbrock[4]). Another A critical property of feedback systems is method uses the eigenvaluesof the loop transfer their robustness; that is, their ability to main-matrix (G(s) in Figure 1) as a function of fre- tain performance in the face of uncertainties. -
An Analysis of Power and Stress Using Cybernetic Epistemology Randall Robert Lyle Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1992 An analysis of power and stress using cybernetic epistemology Randall Robert Lyle Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Educational Psychology Commons, Family, Life Course, and Society Commons, and the Philosophy Commons Recommended Citation Lyle, Randall Robert, "An analysis of power and stress using cybernetic epistemology " (1992). Retrospective Theses and Dissertations. 10131. https://lib.dr.iastate.edu/rtd/10131 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely afreet reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from lefr to right in equal sections with small overlaps. -
Emergentism As an Option in the Philosophy of Religion: Between Materialist Atheism and Pantheism
SURI 7 (2) 2019: 1-22 Emergentism as an Option in the Philosophy of Religion: Between Materialist Atheism and Pantheism James Franklin University of New South Wales Abstract: Among worldviews, in addition to the options of materialist atheism, pantheism and personal theism, there exists a fourth, “local emergentism”. It holds that there are no gods, nor does the universe overall have divine aspects or any purpose. But locally, in our region of space and time, the properties of matter have given rise to entities which are completely different from matter in kind and to a degree god-like: consciousnesses with rational powers and intrinsic worth. The emergentist option is compared with the standard alternatives and the arguments for and against it are laid out. It is argued that, among options in the philosophy of religion, it involves the minimal reworking of the manifest image of common sense. Hence it deserves a place at the table in arguments as to the overall nature of the universe. Keywords: Emergence; pantheism; personal theism; naturalism; consciousness 1. INTRODUCTION The main options among world views are normally classifiable as either materialist atheism, pantheism (widely understood) or personal theism. According to materialist atheism, there exists nothing except the material universe as we ordinarily conceive it, and its properties are fully described by science (present or future). According to personal theism, there exists a separate entity (or entities) of a much higher form than those found in the 2019 Philosophical Association of the Philippines 2 Emergentism as an Option in the Philosophy of Religion material universe, a god or gods. -
Adaptation of Elaborated Feedback in E-Learning
Adaptation of Elaborated Feedback in e-Learning Ekaterina Vasilyeva1,2, Mykola Pechenizkiy2, and Paul De Bra2 1 Department of Computer Science and Information Systems, University of Jyväskylä, P.O. Box 35, 40351 Jyväskylä, Finland 2 Department of Mathematics and Computer Science, Eindhoven University of Technology, P.O. Box 513, 5600MB Eindhoven, the Netherlands {e.vasilyeva,m.pechenizkiy}@tue.nl, [email protected] Abstract. Design of feedback is a critical issue of online assessment develop- ment within Web-based Learning Systems (WBLSs). In our work we demon- strate the possibilities of tailoring the feedback to the students’ learning style (LS), certitude in response and its correctness. We observe in the experimental studies that these factors have a significant influence on the feedback prefer- ences of students and the effectiveness of elaborated feedback (EF), i.e. stu- dents’ performance improvement during the test. These observations helped us to develop a simple EF recommendation approach. Our experimental study shows that (1) many students are eager to follow the recommendations on ne- cessity to read certain EF in the majority of cases; (2) the students more often find the recommended EF to be useful, and (3) the recommended EF helped to answer related questions better. Keywords: feedback authoring, feedback personalization, online assessment. 1 Introduction Online assessment is an important component of modern education. Nowadays it is used not only in e-learning for (self-)evaluation, but also tends to replace or comple- ment traditional methods of formal evaluation of the student’s performance in blended learning. Feedback is usually a significant part of the assessment as students need to be in- formed about the results of their (current and/or overall) performance. -
What Is a Complex System?
What is a Complex System? James Ladyman, James Lambert Department of Philosophy, University of Bristol, U.K. Karoline Wiesner Department of Mathematics and Centre for Complexity Sciences, University of Bristol, U.K. (Dated: March 8, 2012) Complex systems research is becoming ever more important in both the natural and social sciences. It is commonly implied that there is such a thing as a complex system, different examples of which are studied across many disciplines. However, there is no concise definition of a complex system, let alone a definition on which all scientists agree. We review various attempts to characterize a complex system, and consider a core set of features that are widely associated with complex systems in the literature and by those in the field. We argue that some of these features are neither necessary nor sufficient for complexity, and that some of them are too vague or confused to be of any analytical use. In order to bring mathematical rigour to the issue we then review some standard measures of complexity from the scientific literature, and offer a taxonomy for them, before arguing that the one that best captures the qualitative notion of the order produced by complex systems is that of the Statistical Complexity. Finally, we offer our own list of necessary conditions as a characterization of complexity. These conditions are qualitative and may not be jointly sufficient for complexity. We close with some suggestions for future work. I. INTRODUCTION The idea of complexity is sometimes said to be part of a new unifying framework for science, and a revolution in our understanding of systems the behaviour of which has proved difficult to predict and control thus far, such as the human brain and the world economy. -
Selforganization, Emergence, and Constraint in Complex Natural
1 SelfOrganization, Emergence, and Constraint in Complex Natural Systems Abstract: Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraintbased interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We show that the constraintbased account of emergence also leads naturally into a meaningful definition of selforganization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper. Keywords: Complexity Emergence Selforganization Spontaneous order Dynamical systems Corresponding Author: Jonathan Lawhead, PhD University of Southern California Philosophy & Earth Sciences 3651 Trousdale Parkway Zumberge Hall of Science, 223D Los Angeles, CA 900890740 [email protected] 775.287.8005 2 0. Introduction There’s a growing body of multidisciplinary research exploring complexity theory and related ideas. This field has not yet really settled yet, and so there’s plenty of terminological confusion out there. Different people use the same terms to mean different things (witness the constellation of definitions of ‘complexity’ itself). A good understanding of how central concepts in complexity theory fit together will help in applying those concepts to realworld social and scientific problems. -
Lecture 13: Feedback Linearization1
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.243j (Fall 2003): DYNAMICS OF NONLINEAR SYSTEMS by A. Megretski Lecture 13: Feedback Linearization1 Using control authority to transform nonlinear models into linear ones is one of the most commonly used ideas of practical nonlinear control design. Generally, the trick helps one to recognize “simple” nonlinear feedback design tasks. 13.1 Motivation and objectives In this section, we give a motivating example and state technical objectives of theory of feedback linearization. 13.1.1 Example: fully actuated mechanical systems Equations of rather general mechanical systems can be written in the form M (q(t))¨q(t) + F (q(t); q˙(t)) = u(t); (13.1) where q(t) 2 Rk is the position vector, u(t) is the vector of actuation forces and torques, F : Rk £ Rk 7! Rk is a given vector-valued function, and M : Rk 7! Rk£k is a given function taking positive definite symmetric matrix values (the inertia matrix). When u = u(t) is fixed (for example, when u(t) = u0 cos(t) is a harmonic excitation), analysis of (13.1) is usually an extremely difficult task. However, when u(t) is an unrestricted control effort to be chosen, a simple change of control variable u(t) = M (q(t))(v(t) + F (q(t); q˙(t))) (13.2) transforms (13.1) into a linear double integrator model q¨(t) = v(t): (13.3) 1Version of October 29, 2003 2 The transformation from (13.1) to (13.3) is a typical example of feedback linearization, which uses a strong control authority to simplify system equations. -
Complex Adaptive Team Systems (CATS)
Presented by: John R. Turner, Ph.D. [email protected] Complex Adaptive Team Rose Baker, Ph.D. Systems (CATS) [email protected] Kerry Romine, MS [email protected] University of North Texas College of Information Department of Learning Technologies Systems Open –vs– Closed Systems Systems Theory Complex Adaptive Team System (CATS) Complex Adaptive Systems Complexity Theory Open –vs– Closed Systems Open Systems Closed Systems Linear Non‐Linear Predictable Non‐Predictable Presents Simple Problems Presents Complex & Wicked Problems Systems theory Input – Process – Output Changes in one part of the system effects other parts of the system. The system is the sum of the parts. Changes are generally predictable. Complex Adaptive Systems “Interdependent agents that interact, learn from each other, and adapt their behaviors accordingly.” (Beck & Plowman, 2014, p. 1246) The building block for higher level agents or systems while continuously adapting to environmental changes. (Bovaird, 2008) CAS ‐ Characteristics • Non‐Linearity • Contains many constituents interacting non‐linearly. • Open System • Open in which boundaries permit interaction with environment. • Feedback Loops • Contains Feedback loops. • Scalable • Structure spanning several scales. • Emergence • Emergent behavior leads to new state. Complexity Theory “Targets a sub‐set of all systems; a sub‐set which is abundant and is the basis of all novelty; a sub‐set from which structure emerges…. That is, self‐ organization occurs through the dynamics, interactions and feedback of -
Complex Adaptive Systems
Evidence scan: Complex adaptive systems August 2010 Identify Innovate Demonstrate Encourage Contents Key messages 3 1. Scope 4 2. Concepts 6 3. Sectors outside of healthcare 10 4. Healthcare 13 5. Practical examples 18 6. Usefulness and lessons learnt 24 References 28 Health Foundation evidence scans provide information to help those involved in improving the quality of healthcare understand what research is available on particular topics. Evidence scans provide a rapid collation of empirical research about a topic relevant to the Health Foundation's work. Although all of the evidence is sourced and compiled systematically, they are not systematic reviews. They do not seek to summarise theoretical literature or to explore in any depth the concepts covered by the scan or those arising from it. This evidence scan was prepared by The Evidence Centre on behalf of the Health Foundation. © 2010 The Health Foundation Previously published as Research scan: Complex adaptive systems Key messages Complex adaptive systems thinking is an approach that challenges simple cause and effect assumptions, and instead sees healthcare and other systems as a dynamic process. One where the interactions and relationships of different components simultaneously affect and are shaped by the system. This research scan collates more than 100 articles The scan suggests that a complex adaptive systems about complex adaptive systems thinking in approach has something to offer when thinking healthcare and other sectors. The purpose is to about leadership and organisational development provide a synopsis of evidence to help inform in healthcare, not least of which because it may discussions and to help identify if there is need for challenge taken for granted assumptions and further research or development in this area. -
Emergence Theories and Pragmatic Realism Charbel Niño El-Hani Federal University of Bahia, Brazil
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by CommonKnowledge Essays in Philosophy Volume 3 Article 3 Issue 2 Pragmatism and Neopragmatism 6-2002 Emergence Theories and Pragmatic Realism Charbel Niño El-Hani Federal University of Bahia, Brazil Sami Pihlström University of Helsinki, Finland Follow this and additional works at: http://commons.pacificu.edu/eip Part of the Philosophy Commons Recommended Citation El-Hani, Charbel Niño and Pihlström, Sami (2002) "Emergence Theories and Pragmatic Realism," Essays in Philosophy: Vol. 3: Iss. 2, Article 3. Essays in Philosophy is a biannual journal published by Pacific nivU ersity Library | ISSN 1526-0569 | http://commons.pacificu.edu/eip/ Emergence Theories Essays in Philosophy A Biannual Journal Volume 3, Number 2 Emergence Theories and Pragmatic Realism Abstract The tradition of pragmatism has, especially since Dewey, been characterized by a commitment to non- reductive naturalism. The notion of emergence, popular in the early decades of the twentieth century and currently re-emerging as a central concept in metaphysics and the philosophy of mind, may be useful in explicating that commitment. The present paper discusses the issue of the reality of emergent properties, drawing particular attention to a pragmatic way of approaching this issue. The reality of emergents can be defended as a pragmatically-useful ontological commitment; hence, pragmatism can be employed as a tool in the debate over the structure and reality of emergence. This strategy of justifying ontological commitments is examined through historical and systematic discussions of the pragmatist tradition. It turns out, among other things, that while classical pragmatists did not specify any technical notion of emergence in the contemporary sense, their non-reductively naturalist views are relevant to the more recent emergence discussions -- especially because they rejected the metaphysical realism typical of today’s ontologically-oriented emergence theories.