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Complex Systems

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  1. Peter Allen (2000). Knowledge, Ignorance and the Evolution of Complex Systems. World Futures 55 (1):37-70.
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  2. Peter Allen (1992). Modelling Evolution and Creativity in Complex Systems. World Futures 34 (1):105-123.
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  3. Peter M. Allen & Mark Strathern (2003). Evolution, Emergence, and Learning in Complex Systems. Emergence 5 (4):8-33.
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  4. Claes Andersson, Inverse Ontomimetic Simulation: A Window on Complex Systems.
    The present paper introduces "ontomimetic simulation" and argues that this class of models has enabled the investigation of hypotheses about complex systems in new ways that have epistemological relevance. Ontomimetic simulation can be differentiated from other types of modeling by its reliance on causal similarity in addition to representation. Phenomena are modeled not directly but via mimesis of the ontology (i.e. the "underlying physics", microlevel etc.) of systems and a subsequent animation of the resulting model ontology as a dynamical system. (...)
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  5. Harald Atmanspacher, A Semiotic Approach to Complex Systems.
    A key topic in the work of Burghard Rieger is the notion of meaning. To explore this notion, he and his collaborators developed a most sophisticated approach combining theoretical ideas and concepts of semiotics with empirical and numerical tools of computational linguistics (see [31] for a most recent comprehensive account). In the present contribution, relations of Rieger’s achievements to some issues of interest in the physics and philosophy of complex systems will be addressed.
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  6. I. C. Baianu (2006). Robert Rosen's Work and Complex Systems Biology. Axiomathes 16 (1-2).
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  7. Jean-Paul Banquet, Philippe Gaussier, Mathias Quoy & Arnaud Revel (2001). From Reflex to Planning: Multimodal Versatile Complex Systems in Biorobotics. Behavioral and Brain Sciences 24 (6):1051-1053.
    As models of living beings acting in a real world biorobots undergo an accelerated “philogenic” complexification. The first efficient robots performed simple animal behaviours (e.g., those of ants, crickets) and later on isolated elementary behaviours of complex beings. The increasing complexity of the tasks robots are dedicated to is matched by an increasing complexity and versatility of the architectures now supporting conditioning or even elementary planning.
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  8. William P. Bechtel (2001). The Compatibility of Complex Systems and Reduction: A Case Analysis of Memory Research. Minds And Machines 11 (4):483-502.
    Some theorists who emphasize the complexity of biological and cognitive systems and who advocate the employment of the tools of dynamical systems theory in explaining them construe complexity and reduction as exclusive alternatives. This paper argues that reduction, an approach to explanation that decomposes complex activities and localizes the components within the complex system, is not only compatible with an emphasis on complexity, but provides the foundation for dynamical analysis. Explanation via decomposition and localization is nonetheless extremely challenging, and an (...)
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  9. Michael J. Behe (2000). Self-Organization and Irreducibly Complex Systems: A Reply to Shanks and Joplin. Philosophy of Science 67 (1):155-162.
    Some biochemical systems require multiple, well-matched parts in order to function, and the removal of any of the parts eliminates the function. I have previously labeled such systems "irreducibly complex," and argued that they are stumbling blocks for Darwinian theory. Instead I proposed that they are best explained as the result of deliberate intelligent design. In a recent article Shanks and Joplin analyze and find wanting the use of irreducible complexity as a marker for intelligent design. Their primary counterexample is (...)
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  10. Tom R. Burns (2006). The Sociology of Complex Systems: An Overview of Actor-System-Dynamics Theory. World Futures 62 (6):411 – 440.
    This article illustrates the important scientific role that a systems approach might play within the social sciences and humanities, above all through its contribution to a common language, shared conceptualizations, and theoretical integration in the face of the extreme (and growing) fragmentation among the social sciences (and between the social sciences and the natural sciences). The article outlines a systems theoretic approach, actor-system-dynamics (ASD), whose authors have strived to re-establish systems theorizing in the social sciences (after a period of marginalization (...)
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  11. Paul Cilliers (2000). Rules and Complex Systems. Emergence 2 (3):40-50.
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  12. Paul Cilliers (1998). Complexity and Postmodernism: Understanding Complex Systems. Routledge.
    Complexity and Postmodernism explores the notion of complexity in the light of contemporary perspectives from philosophy and science. The book integrates insights from complexity and computational theory with the philosophical position of thinkers including Derrida and Lyotard. Paul Cilliers takes a critical stance towards the use of the analytical method as a tool to cope with complexity, and he rejects Searle's superficial contribution to the debate.
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  13. John Collier, Change and Identity in Complex Systems.
    Complex systems are dynamic and may show high levels of variability in both space and time. It is often difficult to decide on what constitutes a given complex system, i.e., where system boundaries should be set, and what amounts to substantial change within the system. We discuss two central themes: the nature of system definitions and their ability to cope with change, and the importance of system definitions for the mental metamodels that we use to describe and order ideas about (...)
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  14. Peter Csermely (2009). Weak Links: The Universal Key to the Stability of Networks and Complex Systems. Springer.
    A principle is born: the Granovetter study -- Why do we like networks? -- Network stability -- Weak links as stabilizers of complex systems -- Atoms, molecules, and macromolecules -- Weak links and cellular stability -- Weak links and the stability of organisms -- Social nets -- Networks of human culture -- The global web -- The Ecoweb -- Conclusions and perspectives.
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  15. Bruce Edmonds, Understanding Observed Complex Systems – the Hard Complexity Problem.
    bruce@edmonds.name http://bruce.edmonds.name Abstract. Two kinds of problem are distinguished: the first of finding processes which produce complex outcomes from the interaction of simple parts, and the second of finding which process resulted in an observed complex outcome. The former I call the easy complexity problem and the later the hard complexity problem. It is often assumed that progress with the easy problem will aid process with the hard problem. However this assumes that the “reverse engineering” problem, of determining the process (...)
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  16. Gonzalo Génova, M. Rosario González & Anabel Fraga (2007). Ethical Education in Software Engineering: Responsibility in the Production of Complex Systems. Science and Engineering Ethics 13 (4).
    Among the various contemporary schools of moral thinking, consequence-based ethics, as opposed to rule-based, seems to have a good acceptance among professionals such as software engineers. But naïve consequentialism is intellectually too weak to serve as a practical guide in the profession. Besides, the complexity of software systems makes it very hard to know in advance the consequences that will derive from professional activities in the production of software. Therefore, following the spirit of well-known codes of ethics such as the (...)
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  17. M. H. Krieger (2001). Foundations of Complex-Systems Theories. Studies in History and Philosophy of Science Part B 32 (1):135-136.
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  18. Henrik Jeldtoft Jensen (1998). Self-Organized Criticality: Emergent Complex Behavior in Physical and Biological Systems. Cambridge University Press.
    Self-organized criticality (SOC) is based upon the idea that complex behavior can develop spontaneously in certain multi-body systems whose dynamics vary abruptly. This book is a clear and concise introduction to the field of self-organized criticality, and contains an overview of the main research results. The author begins with an examination of what is meant by SOC, and the systems in which it can occur. He then presents and analyzes computer models to describe a number of systems, and he explains (...)
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  19. Henry Kilpatrick (1999). Reviews: From Biology to Sociopolitics: Conceptual Continuity in Complex Systems, Heinz Herrmann. Emergence 1 (2):170-172.
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  20. Helena Knyazeva (2005). Figures of Time in Evolution of Complex Systems. Journal for General Philosophy of Science 36 (2):289 - 304.
    Owing to intensive development of the theory of self-organization of complex systems called also synergetics, profound changes in our notions of time occur. Whereas at the beginning of the 20th century, natural sciences, by picking up the general spirit of Einstein's theory of relativity, consider a geometrization as an ideal, i.e. try to represent time and force interactions through space and the changes of its properties, nowadays, at the beginning of the 21st century, time turns to be in the focus (...)
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  21. Helena Knyazeva & Sergei Kurdyumov (2001). Nonlinear Synthesis and Co-Evolution of Complex Systems. World Futures 57 (3):239-261.
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  22. M. Krieger (2001). Foundations of Complex-Systems Theories. Studies in History and Philosophy of Science Part B 32 (1):135-136.
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  23. Wayne Nahu Lanham (2008). The Spiral Template: The Revolution in the Evolution From Simple to Complex Systems. World Futures 64 (1):60 – 71.
    Change is an inborn trait of all organisms at every level of existence. This article proposes that the evolution of all life follows a course as if bound by a guiding principle or template. Overcoming disorder and entropy through diversity, this template has the properties of a spiral force, which acts to maintain continuity during change and transitions, and operates at all levels, from the simplest of forms to the most complex. Drawing from Chaos Theory, biology, depth psychology, and Buddhism, (...)
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  24. Jay L. Lemke & Nora H. Sabelli (2008). Complex Systems and Educational Change: Towards a New Research Agenda. Educational Philosophy and Theory 40 (1):118–129.
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  25. Kara Vander Linden (2006). A Grounded Approach to the Study of Complex Systems. World Futures 62 (7):491 – 497.
    The complex and dynamic nature of systems pose a particular challenge to researchers and require the use of a research methodology designed to deal with such systems. The properties of fit, relevance, understandability, generality, control, workability, generalizability, and modifiability make Glaserian grounded theory and grounded action particularly well suited for studying systems. These methods are innovative, systemic, and sophisticated enough to reveal the underlying complexities of systems and plan actions that address their complex, dynamic nature while remaining grounded in what (...)
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  26. F. Mallamace & H. Eugene Stanley (2004). The Physics of Complex Systems: New Advances and Perspectives. Ios Press.
    Remembering Fermi MORREL H. COHEN Department of Physics and Astronomy, Rutgers University Frelinghuysen Road. Piscataway, NJ 08854-8019 USA and Department ...
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  27. Russ Marion & Josh Bacon (1999). Organizational Extinction and Complex Systems. Emergence 1 (4):71-96.
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  28. Gottfried Mayer-Kress & Mason A. Porter (2001). Remarks on Whale Cultures From a Complex Systems Perspective. Behavioral and Brain Sciences 24 (2):344-344.
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  29. Giorgio Metta & Giulio Sandini (2001). Embodiment and Complex Systems. Behavioral and Brain Sciences 24 (6):1068-1069.
    In agreement with the target article, we would like to point out a few aspects related to embodiment which further support the position of biorobotics. We argue that, especially when complex systems are considered, modeling through a physical implementation can provide hints to comprehend the whole picture behind the specific set of experimental data.
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  30. Sandra D. Mitchell (2006). Modularity?More Than a Buzzword?: Modularity in Development and Evolution Gerhard Schlosser and G�Nter P. Wagner , Eds Chicago: University of Chicago Press, 2003 (600 Pp; $35.00 Pbk; ISBN 0226738558); Modularity: Understanding the Development and Evolution of Natural Complex Systems Werner Callebaut and Diego Rasskin-Gutman , Eds Cambridge, MA: MIT Press, 2005 (464 Pp; $55.00 Hbk; ISBN 0262033267). Biological Theory 1 (1):98-101.
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  31. Robert B. Northrop (2010). Introduction to Complexity and Complex Systems. Taylor & Francis.
    Introduction to complexity and complex systems -- Introduction to large linear systems -- Introduction to biochemical oscillators and nonlinear biochemical systems -- Modularity, redundancy, degeneracy, pleiotropy and robustness in complex biological systems -- The evolution of biological complexity; invertebrate immune systems -- Irreducible and specified complexity in living systems -- The complex adaptive and innate human immune systems -- Complexity in quasispecies : microRNAs -- Introduction to complexity in economic systems -- Complexity in quasispecies : micrornas -- Dealing with complexity.
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  32. Jay Odenbaugh (2003). Complex Systems, Trade‐Offs, and Theoretical Population Biology: Richard Levin's “Strategy of Model Building in Population Biology” Revisited. Philosophy of Science 70 (5):1496-1507.
    Ecologist Richard Levins argues population biologists must trade‐off the generality, realism, and precision of their models since biological systems are complex and our limitations are severe. Steven Orzack and Elliott Sober argue that there are cases where these model properties cannot be varied independently of one another. If this is correct, then Levins's thesis that there is a necessary trade‐off between generality, precision, and realism in mathematical models in biology is false. I argue that Orzack and Sober's arguments fail since (...)
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  33. Jay Odenbaugh (2003). Complex Systems, Trade-Offs, and Theoretical Population Biology: Richard Levin's "Strategy of Model Building in Population Biology" Revisited. Philosophy of Science 70 (5):1496-1507.
    Ecologist Richard Levins (1966, 1968) argues population biologists must trade-off the generality, realism and precision of their models since biological systems are complex and our limitations are severe. Elliott Sober and Steven Orzack (1993) argue that there are cases where these model properties cannot be varied independently of one another. If this is correct, then Levins` thesis that there is a necessary trade-off between generality, precision, and realism in mathematical models in biology is false. I argue that Sober and Orzack`s (...)
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  34. Nikos Papastergiadis (2010). Understanding Hybrid Identities: From Mechanical Models to Complex Systems. World Futures 66 (3 & 4):243 – 265.
    This article examines the use of organic and mechanistic metaphors that have underpinned the modeling of national governance in the social sciences and also framed the representation of the social impact of migration. It argues that the global patterns of migration and the contemporary forms of hybrid subjectivity do not fit well with these conceptual frameworks. The limits of this framework are examined through Harald Kleinschmidt's theory of residentialism, and the outlines of an alternative conceptual frame is proposed by drawing (...)
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  35. Birute Regine & Roger Lewin (2000). Leading at the Edge: How Leaders Influence Complex Systems. Emergence 2 (2):5-23.
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  36. Kurt A. Richardson & Michael R. Lissack (2001). On the Status of Boundaries, Both Natural and Organizational: A Complex Systems Perspective. Emergence 3 (4):32-49.
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  37. S. Schweber, Wachter &unknown & M. (2000). Complex Systems, Modelling and Simulation. Studies in History and Philosophy of Science Part B 31 (4):583-609.
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  38. Andreas Wagner (1999). Causality in Complex Systems. Biology and Philosophy 14 (1).
    Systems involving many interacting variables are at the heart of the natural and social sciences. Causal language is pervasive in the analysis of such systems, especially when insight into their behavior is translated into policy decisions. This is exemplified by economics, but to an increasing extent also by biology, due to the advent of sophisticated tools to identify the genetic basis of many diseases. It is argued here that a regularity notion of causality can only be meaningfully defined for systems (...)
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  39. William C. Wimsatt, The Ontology of Complex Systems: Levels of Organization, Perspectives, and Causal Thickets.
    Willard van Orman Quine once said that he had a preference for a desert ontology. This was in an earlier day when concerns with logical structure and ontological simplicity reigned supreme. Ontological genocide was practiced upon whole classes of upper-level or "derivative" entities in the name of elegance, and we were secure in the belief that one strayed irremediably into the realm of conceptual confusion and possible error the further one got from ontic fundamentalism. In those days, one paid more (...)
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  40. Kazuko Yamasaki, Kaushik Matia, Fabio Pammolli, Sergey Buldyrev, Massimo Riccaboni, H. Eugene Stanley & Dongfeng Fu, Preferential Attachment and Growth Dynamics in Complex Systems.
    Complex systems can be characterized by classes of equivalency of their elements defined according to system specific rules. We propose a generalized preferential attachment model to describe the class size distribution. The model postulates preferential growth of the existing classes and the steady influx of new classes. According to the model, the distribution changes from a pure exponential form for zero influx of new classes to a power law with an exponential cut-off form when the influx of new classes is (...)
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  41. P. Ylikoski (2009). Book Review: Sawyer, R. Keith. (2005). Social Emergence: Societies as Complex Systems. Cambridge, UK: Cambridge University Press. Philosophy of the Social Sciences 39 (3):527-530.
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  42. Elias Zafiris (2005). Complex Systems From the Perspective of Category Theory: I. Functioning of the Adjunction Concept. Axiomathes 15 (1).
    We develop a category theoretical scheme for the comprehension of the information structure associated with a complex system, in terms of families of partial or local information carriers. The scheme is based on the existence of a categorical adjunction, that provides a theoretical platform for the descriptive analysis of the complex system as a process of functorial information communication.
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  43. Elias Zafiris (2005). Complex Systems From the Perspective of Category Theory: II. Covering Systems and Sheaves. Axiomathes 15 (2).
    Using the concept of adjunction, for the comprehension of the structure of a complex system, developed in Part I, we introduce the notion of covering systems consisting of partially or locally defined adequately understood objects. This notion incorporates the necessary and sufficient conditions for a sheaf theoretical representation of the informational content included in the structure of a complex system in terms of localization systems. Furthermore, it accommodates a formulation of an invariance property of information communication concerning the analysis of (...)
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Chaos
  1. Harald Atmanspacher, Characterizing Spontaneous Irregular Behavior in Coupled Map Lattices.
    Two-dimensional coupled map lattices display, in a specific parameter range, a stable phase (quasi-) periodic in both space and time. With small changes to the model parameters, this stable phase develops spontaneous eruptions of nonperiodic behavior. Although this behavior itself appears irregular, it can be characterized in a systematic fashion. In particular, parameter-independent features of the spontaneous eruptions may allow useful empirical characterizations of other phenomena that are intrinsically hard to predict and reproduce. Specific features of the distributions of lifetimes (...)
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  2. Harald Atmanspacher, Ontic and Epistemic Descriptions of Chaotic Systems.
    Traditional philosophical discourse draws a distinction between ontology and epistemology and generally enforces this distinction by keeping the two subject areas separated and unrelated. In addition, the relationship between the two areas is of central importance to physics and philosophy of physics. For instance, all kinds of measurement-related problems force us to consider both our knowledge of the states and observables of a system (epistemic perspective) and its states and observables independent of such knowledge (ontic perspective). This applies to quantum (...)
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  3. Peter Coles (2006). From Cosmos to Chaos: The Science of Unpredictability. Oxford University Press.
    Cosmology has undergone a revolution in recent years. The exciting interplay between astronomy and fundamental physics has led to dramatic revelations, including the existence of the dark matter and the dark energy that appear to dominate our cosmos. But these discoveries only reveal themselves through small effects in noisy experimental data. Dealing with such observations requires the careful application of probability and statistics. But it is not only in the arcane world of fundamental physics that probability theory plays such an (...)
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  4. Roman Frigg, Chaos and Randomness: An Equivalence Proof of a Generalized Version of the Shannon Entropy and the Kolmogorov–Sinai Entropy for Hamiltonian Dynamical Systems.
    Chaos is often explained in terms of random behaviour; and having positive Kolmogorov–Sinai entropy (KSE) is taken to be indicative of randomness. Although seemly plausible, the association of positive KSE with random behaviour needs justification since the definition of the KSE does not make reference to any notion that is connected to randomness. A common way of justifying this use of the KSE is to draw parallels between the KSE and ShannonÕs information theoretic entropy. However, as it stands this no (...)
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  5. Sheldon Goldstein, Absence of Chaos in Bohmian Dynamics.
    In a recent paper [1], O. F. de Alcantara Bonfim, J. Florencio, and F. C. S´ a Barreto claim to have found numerical evidence of chaos in the motion of a Bohmian quantum particle in a double square-well potential, for a wave function that is a superposition of five energy eigenstates. But according to the result proven here, chaos for this motion is impossible. We prove in fact that for a particle on the line in a superposition of n + (...)
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  6. Jeffrey Koperski (2001). Has Chaos Been Explained? British Journal for the Philosophy of Science 52 (4):683-700.
    In his recent book, Explaining Chaos, Peter Smith presents a new problem in the foundations of chaos theory. Specifically, he argues that the standard ways of justifying idealizations in mathematical models fail when it comes to the infinite intricacy found in strange attractors. I argue that Smith's analysis undermines much of the explanatory power of chaos theory. A better approach is developed by drawing analogies from the models found in continuum mechanics.
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  7. Theodor Leiber (1997). On the Actual Impact of Deterministic Chaos. Synthese 113 (3):357-379.
    The notion of (deterministic) chaos is frequently used in an increasing number of scientific (as well as non-scientific) contexts, ranging from mathematics and the physics of dynamical systems to all sorts of complicated time evolutions, e.g., in chemistry, biology, physiology, economy, sociology, and even psychology. Despite (or just because of) these widespread applications, however, there seem to fluctuate around several misunderstandings about the actual impact of deterministic chaos on several problems of philosophical interest, e.g., on matters of prediction and computability, (...)
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  8. Michael Strevens (2006). Chaos. In D. M. Borchert (ed.), Encyclopedia of Philosophy, second edition.
    A physical system has a chaotic dynamics, according to the dictionary, if its behavior depends sensitively on its initial conditions, that is, if systems of the same type starting out with very similar sets of initial conditions can end up in states that are, in some relevant sense, very different. But when science calls a system chaotic, it normally implies two additional claims: that the dynamics of the system is relatively simple, in the sense that it can be expressed in (...)
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Complexity
  1. Ion C. Baianu (forthcoming). Categorical Ontology of Levels and Emergent Complexity: An Introduction. Axiomathes.
    An overview of the following three related papers in this issue presents the Emergence of Highly Complex Systems such as living organisms, man, society and the human mind from the viewpoint of the current Ontological Theory of Levels. The ontology of spacetime structures in the Universe is discussed beginning with the quantum level; then, the striking emergence of the higher levels of reality is examined from a categorical—relational and logical viewpoint. The ontological problems and methodology aspects discussed in the first (...)
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  2. Hans Poser (2007). Theories of Complexity and Their Problems. Frontiers of Philosophy in China 2 (3):423-436.
    Complexity theories are on the way to establish a new worldview—processes instead of objects, history and uniqueness of everything instead of repetition and lawlikeness are the elements. These theories from deterministic chaos via the dissipative structures, the theory of catastrophes, self organization and synergetics are mathematical models, connected with a new understanding of science. They are characterized by new fundamental commitments of sciences. But at the same time, they are characterized by epistemic boundaries.
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  3. Michael Strevens (2005). How Are the Sciences of Complex Systems Possible? Philosophy of Science 72 (4):531-556.
    To understand the behavior of a complex system, you must understand the interactions among its parts. Doing so is difficult for non-decomposable systems, in which the interactions strongly influence the short-term behavior of the parts. Science's principal tool for dealing with non-decomposable systems is a variety of probabilistic analysis that I call EPA. I show that EPA's power derives from an assumption that appears to be false of non-decomposable complex systems, in virtue of their very non-decomposability. Yet EPA is extremely (...)
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  4. Michael Strevens (2003). Bigger Than Chaos: Understanding Complexity Through Probability. Harvard University Press.
    In this book, Michael Strevens aims to explain how simplicity can coexist with, indeed be caused by, the tangled interconnections between a complex system's ...
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Nonlinear Dynamics
  1. Sunny Auyang, Nonlinear Dynamics: How Science Comprehends Chaos.
    Behaviors of chaotic systems are unpredictable. Chaotic systems are deterministic, their evolutions being governed by dynamical equations. Are the two statements contradictory? They are not, because the theory of chaos encompasses two levels of description. On a higher level, unpredictability appears as an emergent property of systems that are predictable on a lower level. In this talk, we examine the structure of dynamical theories to see how they employ multiple descriptive levels to explain chaos, bifurcation, and other complexities of nonlinear (...)
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  2. Mohammed H. I. Dore & J. Barkley Rosser, Do Nonlinear Dynamics in Economics Amount to a Kuhnian Paradigm Shift?
    Much empirical analysis and econometric work recognizes that there are nonlinearities, regime shifts or structural breaks, asymmetric adjustment costs, irreversibilities and lagged dependencies. Hence, empirical work has already transcended neoclassical economics. Some progress has also been made in modeling endogenously generated cyclical growth and fluctuations. All this is inconsistent with neoclassical general equilibrium. Hence there is growing evidence of Kuhnian anomalies. It therefore follows that there is a Kuhnian crisis in economics and further research in nonlinear dynamics and complexity can (...)
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  3. Gordon G. Globus (2005). Nonlinear Dynamics at the Cutting Edge of Modernity: A Postmodern View. Philosophy, Psychiatry, and Psychology 12 (3):229-234.
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  4. Stephen H. Kellert (2001). Extrascientific Uses of Physics: The Case of Nonlinear Dynamics and Legal Theory. Proceedings of the Philosophy of Science Association 2001 (3):S455-.
    This essay explores the metaphorical use of the area of nonlinear dynamics popularly known as "chaos theory," surveying its use in one particular field: legal theory. After sketching some of the mistakes encountered in these efforts, I outline the possibility of the fruitful use of nonlinear dynamics for thinking about our legal system. I then offer some general lessons to be drawn from these examples-both cautionary maxims and a limited defense of cross-disciplinary borrowing. I conclude with some reflections on the (...)
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  5. Erik Rietveld (forthcoming). Context-Switching and Responsiveness to Real Relevance. In Julian Kiverstein & Michael Wheeler (eds.), Heidegger and Cognitive Science. Palgrave.
    Our everyday activities unfold in situations that offer a multiplicity of possibilities for action. While typing this text, the apple on the right side of my laptop affords eating, my e-mail checking, and the glass of water drinking from it. Every now and then I unreflectively switch from typing to eating or drinking and back to typing again. A relevant possibility for action is embedded in a field of other soliciting possibilities for action (Rietveld, 2008). Michael Wheeler and Hubert Dreyfus (...)
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  6. Erik Rietveld (2008). The Skillful Body as a Concernful System of Possible Actions: Phenomena and Neurodynamics. Theory & Psychology 18 (3):341-361.
    For Merleau-Ponty,consciousness in skillful coping is a matter of prereflective ‘I can’ and not explicit ‘I think that.’ The body unifies many domain-specific capacities. There exists a direct link between the perceived possibilities for action in the situation (‘affordances’) and the organism’s capacities. From Merleau-Ponty’s descriptions it is clear that in a flow of skillful actions, the leading ‘I can’ may change from moment to moment without explicit deliberation. How these transitions occur, however, is less clear. Given that Merleau-Ponty suggested (...)
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  7. Barkley Rosser, Aspects of Dialectics and Nonlinear Dynamics.
    Three principles of dialectical analysis are examined in terms of nonlinear dynamics models. The three principles are the transformation of quantity into quality, the interpenetration of opposites, and the negation of the negation. The first two of these especially are interpreted within the frameworks of catastrophe, chaos, and emergent dynamics complexity theoretic models, with the concept of bifurcation playing a central role. Problems with this viewpoint are also discussed.
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  8. Barkley Rosser, Do Nonlinear Dynamics in Economics Amount to a Kuhnian Paradigm Shift?
    Much empirical analysis and econometric work recognizes that there are nonlinearities, regime shifts or structural breaks, asymmetric adjustment costs, irreversibilities and lagged dependencies. Hence, empirical work has already transcended neoclassical economics. Some progress has also been made in modeling endogenously generated cyclical growth and fluctuations. All this is inconsistent with neoclassical general equilibrium. Hence there is growing evidence of Kuhnian anomalies. It therefore follows that there is a Kuhnian crisis in economics and further research in nonlinear dynamics and complexity can (...)
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  9. Alexander Rueger & W. David Sharp (1996). Simple Theories of a Messy World: Truth and Explanatory Power in Nonlinear Dynamics. British Journal for the Philosophy of Science 47 (1):93-112.
    Philosophers like Duhem and Cartwright have argued that there is a tension between laws' abilities to explain and to represent. Abstract laws exemplify the first quality, phenomenological laws the second. This view has both metaphysical and methodological aspects: the world is too complex to be represented by simple theories; supplementing simple theories to make them represent reality blocks their confirmation. We argue that both aspects are incompatible with recent developments in nonlinear dynamics. Confirmation procedures and modelling strategies in nonlinear dynamics (...)
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  10. Jessica Wahman (2005). Determined by Chaos: The Nonlinear Dynamics of Free Will. Philosophy, Psychiatry, and Psychology 12 (3):235-237.
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  11. Graham White (2000). Lewis, Causality, and Possible Worlds. Dialectica 54 (2):133–137.
    We show that, given standard assumptions about classical dynamical systems, Lewis' conception of possible worlds is incompatible with classical physics in that it would imply that all dynamical systems were integrable.
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  12. Jeffrey Yoshimi (2007). Mathematizing Phenomenology. Phenomenology and the Cognitive Sciences 6 (3).
    Husserl is well known for his critique of the “mathematizing tendencies” of modern science, and is particularly emphatic that mathematics and phenomenology are distinct and in some sense incompatible. But Husserl himself uses mathematical methods in phenomenology. In the first half of the paper I give a detailed analysis of this tension, showing how those Husserlian doctrines which seem to speak against application of mathematics to phenomenology do not in fact do so. In the second half of the paper I (...)
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Systems Theory
  1. Evandro Agazzi (1978). Systems Theory and the Problem of Reductionism. Erkenntnis 12 (3):339 - 358.
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  2. Kenneth D. Bailey (2005). Emergence, Drop-Back and Reductionism in Living Systems Theory. Axiomathes 15 (1).
    Millers Living Systems Theory (LST) is known to be very comprehensive. It comprises eight nested hierarchical levels. It also includes twenty critical subsystems. While Millers approach has been analyzed and applied in great detail, some problematic features remain, requiring further explication. One of these is the relationship between reduction and emergence in LST. There are at least four relevant possibilities. One is that LST exhibits neither clear reductionism nor emergence, but is essentially neutral in this regard. Another is that the (...)
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  3. Philip Barnard & Tim Dalgleish (2005). Psychological-Level Systems Theory: The Missing Link in Bridging Emotion Theory and Neurobiology Through Dynamic Systems Modeling. Behavioral and Brain Sciences 28 (2):196-197.
    Bridging between psychological and neurobiological systems requires that the system components are closely specified at both the psychological and brain levels of analysis. We argue that in developing his dynamic systems theory framework, Lewis has sidestepped the notion of a psychological level systems model altogether, and has taken a partisan approach to his exposition of a brain-level systems model.
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  4. A. Bielecki, Andrzej Kokoszka & P. Holas (2000). Dynamic Systems Theory Approach to Consciousness. International Journal of Neuroscience 104 (1):29-47.
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  5. James A. Blachowicz (1971). Systems Theory and Evolutionary Models of the Development of Science. Philosophy of Science 38 (2):178-199.
    Philosophers of science have used various formulations of the "random mutation--natural selection" scheme to explain the development of scientific knowledge. But the uncritical acceptance of this evolutionary model has led to substantive problems concerning the relation between fact and theory. The primary difficulty lies in the fact that those who adopt this model (Popper and Kuhn, for example) are led to claim that theories arise chiefly through the processes of relatively random change. Systems theory constitutes a general criticism of this (...)
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  6. Ann Burlein (2005). The Productive Power of Ambiguity: Rethinking Homosexuality Through the Virtual and Developmental Systems Theory. Hypatia 20 (1):21-53.
    : This paper juxtaposes Deleuze's notion of the virtual alongside Oyama's notion of a developmental system in order to explore the promises and perils of thinking bodily identity as indeterminate at a time when new technologies render bodily ambiguity increasingly productive of both economic profit and power relations.
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  7. Guy Burneko (1991). It Happens by Itself: The Tao of Cooperation, Systems Theory, and Constitutive Hermeneutics. World Futures 31 (2):139-160.
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  8. Wayne Christensen (1996). A Complex Systems Theory of Teleology. Biology and Philosophy 11 (3).
    Part I [sections 2–4] draws out the conceptual links between modern conceptions of teleology and their Aristotelian predecessor, briefly outlines the mode of functional analysis employed to explicate teleology, and develops the notion of cybernetic organisation in order to distinguish teleonomic and teleomatic systems. Part II is concerned with arriving at a coherent notion of intentional control. Section 5 argues that intentionality is to be understood in terms of the representational properties of cybernetic systems. Following from this, section 6 argues (...)
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  9. Hans-Ulrich Dallmann (1998). Niklas Luhmann's Systems Theory as a Challenge for Ethics. Ethical Theory and Moral Practice 1 (1):85-102.
    The author discusses Niklas Luhmann's concept of ethics and morals. Therefore he sketches the main traits of Luhmann's theory of systems (e.g. the terms autopoiesis, system and environment, code and programme). From the system-theoretical point of view, ethics are characterized as the reflexive theory of morals. Morals are described as the communication of regard or disregard. The author shows which consequences follow from this concept by discussing problems concerning several subsystems at the same time. The problems of Luhmann's theory of (...)
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  10. Daniel Dennett, Intentional Systems Theory.
    Intentional systems theory is in the first place an analysis of the meanings of such everyday ‘mentalistic’ terms as ‘believe,’ ‘desire,’ ‘expect,’ ‘decide,’ and ‘intend,’ the terms of ‘folk psychology’ (Dennett 1971) that we use to interpret, explain, and predict the behavior of other human beings, animals, some artifacts such as robots and computers, and indeed ourselves. In traditional parlance, we seem to be attributing minds to the things we thus interpret, and this raises a host of questions about the (...)
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  11. Chunyu Dong (2010). Intelligent Design From the Viewpoint of Complex Systems Theory. Frontiers of Philosophy in China 5 (3):461-470.
    Based on an analysis of the origins and characteristics of Intelligent Design (ID), this essay discusses the related issues of probability and irreducible complexity. From the viewpoint of complex systems theory, I suggest that Intelligent Design is not, as certain advocates claim, the only reasonable approach for dealing with the current difficulties of evolutionary biology.
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  12. Dave Elder-Vass (2007). Luhmann and Emergentism: Competing Paradigms for Social Systems Theory? Philosophy of the Social Sciences 37 (4):408-432.
    Social systems theory has been dominated in recent years by the work of Niklas Luhmann, but there is another strand of systems thinking, which is receiving increasing attention in sociology: emergentism. For emergentism, the core problems of systems thinking are concerned with causation and reductionism; for Luhmann, they are questions of meaning and self-reference. Arguing from an emergentist perspective, the article finds that emergentism addresses its own core problem successfully, while Luhmann's approach is incapable of resolving questions of causation and (...)
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  13. Horace Fairlamb (forthcoming). Must Complex Systems Theory Be Materialistic? Foundations of Science.
    So far, the sciences of complexity have received less attention from philosophers than from scientists. Responding to Salthe’s (Found Sci 15, 4(6):357–367, 2010a) model of evolution, I focus on its metaphysical implications, asking whether the implications of his canonical developmental trajectory (CDT) must be materialistic as his reading proposes.
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  14. Alan Fogel, Ilse de Koeyer, Cory Secrist & Ryan Nagy (2002). Dynamic Systems Theory Places the Scientist in the System. Behavioral and Brain Sciences 25 (5):623-624.
    Dynamic systems theory is a way of describing the patterns that emerge from relationships in the universe. In the study of interpersonal relationships, within and between species, the scientist is an active and engaged participant in those relationships. Separation between self and other, scientist and subject, runs counter to systems thinking and creates an unnecessary divide between humans and animals.
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  15. Roman Frigg (2004). In What Sense is the Kolmogorov-Sinai Entropy a Measure for Chaotic Behaviour?—Bridging the Gap Between Dynamical Systems Theory and Communication Theory. British Journal for the Philosophy of Science 55 (3):411 - 434.
    On an influential account, chaos is explained in terms of random behaviour; and random behaviour in turn is explained in terms of having positive Kolmogorov-Sinai entropy (KSE). Though intuitively plausible, the association of the KSE with random behaviour needs justification since the definition of the KSE does not make reference to any notion that is connected to randomness. I provide this justification for the case of Hamiltonian systems by proving that the KSE is equivalent to a generalized version of Shannon's (...)
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  16. Philippe Gagnon (2010). “What We Have Learnt From Systems Theory About the Things That Nature’s Understanding Achieves”. In Dirk Evers, Antje Jackelén & Taede Smedes (eds.), How do we Know? Understanding in Science and Theology. Forum Scientiarum.
    The problem of knowledge has been centred around the study of the content of our consciousness, seeing the world through internal representation, without any satisfactory account of the operations of nature that would be a pre-condition for our own performances in terms of concept efficiency in organizing action externally. If we want to better understand where and how meaning fits in nature, we have to find the proper way to decipher its organization, and account for the fact that we have (...)
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  17. Philippe Gagnon (2002). La Théologie de la Nature Et la Science à l'Ère de L'Information. Cerf.
    The history of the relationship between Christian theology and the natural sciences has been conditioned by the initial decision of the masters of the "first scientific revolution" to disregard any necessary explanatory premiss to account for the constituting organization and the framing of naturally occurring entities. Not paying any attention to hierarchical control, they ended-up disseminating a vision and understanding in which it was no longer possible for a theology of nature to send questions in the direction of the experimental (...)
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  18. Robert M. Galatzer-Levy (2005). Exploring Psychological Complexity Through Dynamic Systems Theory: A Complement to Reductionism. Behavioral and Brain Sciences 28 (2):206-207.
    Dynamic systems theory (DS) provides tools for exploring how simpler elements can interact to produce complex psychological configurations. It may, as Lewis demonstrates, provide means for explicating relationships between two reductionist approaches to overlapping sets of phenomena. The result is a description of psychological phenomena at a level that begins to achieve the richness we would hope to achieve in examining psychological life as it is experienced and explored in psychoanalysis.
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  19. Peter Godfrey-Smith (2000). Explanatory Symmetries, Preformation, and Developmental Systems Theory. Philosophy of Science 67 (3):331.
    Some central ideas associated with developmental systems theory (DST) are outlined for non-specialists. These ideas concern the nature of biological development, the alleged distinction between "genetic" and "environmental" traits, the relations between organism and environment, and evolutionary processes. I also discuss some criticisms of the DST approach.
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  20. Paul E. Griffiths & Russell D. Gray (2005). Discussion: Three Ways to Misunderstand Developmental Systems Theory. Biology and Philosophy 20 (2-3).
    Developmental systems theory (DST) is a general theoretical perspective on development, heredity and evolution. It is intended to facilitate the study of interactions between the many factors that influence development without reviving `dichotomous' debates over nature or nurture, gene or environment, biology or culture. Several recent papers have addressed the relationship between DST and the thriving new discipline of evolutionary developmental biology (EDB). The contributions to this literature by evolutionary developmental biologists contain three important misunderstandings of DST.
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  21. Francis Halsall (2008). Systems of Art: Art, History and Systems Theory. Peter Lang.
    Systems theory understands phenomena in terms of the systems of which they are part. This book is about a systems theoretical approach to thinking about art.
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  22. Boris Hennig (2000). Luhmann Und Die Formale Mathematik. In Peter-Ulrich Merz-Benz & Gerhard Wagner (eds.), Die Logik Der Systeme. Universitätsverlag Konstanz.
    Niklas Luhmann verwendet in seiner soziologischen Systemtheorie offenbar etwas, das er den Büchern des englischen Mathematikers George Spencer Brown entnimmt. Dessen Formenkalkül ist für Luhmann, wie Günther Schulte treffend bemerkt, “Mädchen für alles, mit dem er nicht nur in der Lage ist Teezukochen, sondern auch Auto oder Straßenbahn zu fahren”. Der erste Blick in Spencer Browns Laws of Form vermittelt einen anderen Eindruck: nichts scheinen sie mit soziologischer Systemtheorie zu tun zu haben. Der vorliegende Text bearbeitet hieran anknüpfend eine recht (...)
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  23. Karen L. Hollis (2000). Strategies for Integrating Biological Theory, Control Systems Theory, and Pavlovian Conditioning. Behavioral and Brain Sciences 23 (2):258-259.
    To make possible the integration proposed by Domjan et al., psychologists first need to close the research gap between behavioral ecology and the study of Pavlovian conditioning. I suggest two strategies, namely, to adopt more behavioral ecological approaches to social behavior or to co-opt problems already addressed by behavioral ecologists that are especially well suited to the study of Pavlovian conditioning.
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  24. Scott Hotton & Jeff Yoshimi (2011). Extending Dynamical Systems Theory to Model Embodied Cognition. Cognitive Science 35 (3):444-479.
    We define a mathematical formalism based on the concept of an ‘‘open dynamical system” and show how it can be used to model embodied cognition. This formalism extends classical dynamical systems theory by distinguishing a ‘‘total system’’ (which models an agent in an environment) and an ‘‘agent system’’ (which models an agent by itself), and it includes tools for analyzing the collections of overlapping paths that occur in an embedded agent's state space. To illustrate the way this formalism can be (...)
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  25. Pierre Jolicoeur (1990). Identification of Disoriented Objects: A Dual-Systems Theory. Mind and Language 5 (4):387-410.
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  26. Rob Koegel (1998). Implications of Sacred Pleasure for Systems Theory. World Futures 53 (1):81-83.
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  27. Gert König (1972). Concepts of Theory and Systems Theory. Philosophy and History 5 (1):39-40.
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  28. Richard Levins (2008). Dialectics and Systems Theory. In Bertell Ollman & Tony Smith (eds.), Dialectics for the New Century. Palgrave Macmillan.
    Systems Theory is best understood in its dual nature as an episode in the generic development of human understanding of the world, and as the specific product of its social history. On the one hand it is a "moment" in the investigation of complex systems, the place between the formulation of a problem and the interpretation of its solution where mathematical modeling can make the obscure obvious. On the other hand it is the attempt of a reductionist scientific tradition to (...)
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  29. Markus Ekkehard Locker (2006). Systems Theory and the Conundrum of Ens: Thoughts and Aphorisms. Foundations of Science 11 (3).
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  30. Ian I. Mitroff & Francisco Sagasti (1973). Epistemology as General Systems Theory: An Approach to the Design of Complex Decision-Making Experiments. Philosophy of the Social Sciences 3 (1):117-134.
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  31. Gerald Morgan (1982). Systems Theory: Philosophical and Methodological Problems I. V. Blauberg, V. N. Sadovsky, and E. G. Yudin Moscow: Progress Publishers, 1977. Pp. 318. $5.50Mankind and the Year 2000 V. Kosolapov Moscow: Progress Publishers (Current Problems Series), 1976. Pp. 236. $3.25. Dialogue 21 (02):336-342.
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  32. David Morris (2004). The Sense of Space. State University of New York Press.
    Drawing on the philosophies of Merleau-Ponty and Bergon, as well as contemporary psychology to develop a renewed account of the moving, perceiving body, the ...
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  33. David Morris (2002). Thinking the Body, From Hegel's Speculative Logic of Measure to Dynamic Systems Theory. Journal of Speculative Philosophy 16 (3):182-197.
    A study of shifts in scientific strategies for measuring the living body, especially in dynamic systems theory: (1) sheds light on Hegel's concept of measure in The Science of Logic, and the dialectical transition from categories of being to categories of essence; (2) shows how Hegel's speculative logic anticipates and analyzes key tensions in scientific attempts to measure and conceive the dynamic agency of the body. The study's analysis of the body as having an essentially dynamic identity irreducible (...)
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