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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, A Dynamical Approach to Identity and Diversity in Complex Systems.
    The subject of this chapter is the identity of individual dynamical objects and properties. Two problems have dominated the literature: transtemporal identity and the relation between composition and identity. Most traditional approaches to identity rely on some version of classification via essential or typical properties, whether nominal or real. Nominal properties have the disadvantage of producing unnatural classifications, and have several other problems. Real properties, however, are often inaccessible or hard to define (strict definition would make them nominal). I suggest (...)
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  14. 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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  15. 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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  16. 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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  17. 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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  18. Martin 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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  19. 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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  20. Henry Kilpatrick (1999). Reviews: From Biology to Sociopolitics: Conceptual Continuity in Complex Systems, Heinz Herrmann. [REVIEW] Emergence 1 (2):170-172.
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  21. Helena Knyazeva & Sergei Kurdyumov (2001). Nonlinear Synthesis and Co-Evolution of Complex Systems. World Futures 57 (3):239-261.
    Today a change is imperative in approaching global problems: what is needed is not arm-twisting and power politics, but searching for ways of co-evolution in the complex social and geopolitical systems of the world. The modern theory of self-organization of complex systems provides us with an understanding of the possible forms of coexistence of heterogeneous social and geopolitical structures at different stages of development regarding the different paths of their sustainable co-evolutionary development. The theory argues that the evolutionary channel to (...)
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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. Meinard Kuhlmann (2011). Mechanisms in Dynamically Complex Systems. In Phyllis McKay Illari & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
    In recent debates mechanisms are often discussed in the context of ‘complex systems’ which are understood as having a complicated compositional structure. I want to draw the attention to another, radically different kind of complex system, in fact one that many scientists regard as the only genuine kind of complex system. Instead of being compositionally complex these systems rather exhibit highly non-trivial dynamical patterns on the basis of structurally simple arrangements of large numbers of non-linearly interacting constituents. The characteristic dynamical (...)
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  24. 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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  25. 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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  26. 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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  27. F. Mallamace & H. Eugene Stanley (eds.) (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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  28. F. Mallamace & H. Eugene Stanley (eds.) (1997). The Physics of Complex Systems: Proceedings of the International School of Physics <>: Course Cxxxiv: Varenna on Lake Como, Villa Monastero, 9-19 July 1996. [REVIEW] Ios Press.
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  29. Russ Marion & Josh Bacon (1999). Organizational Extinction and Complex Systems. Emergence 1 (4):71-96.
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  30. 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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  31. 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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  32. 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). [REVIEW] Biological Theory 1 (1):98-101.
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  33. 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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  34. 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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  35. 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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  36. 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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  37. Birute Regine & Roger Lewin (2000). Leading at the Edge: How Leaders Influence Complex Systems. Emergence 2 (2):5-23.
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  38. 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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  39. 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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  40. Roberto Serra (ed.) (1986). Introduction to the Physics of Complex Systems: The Mesoscopic Approach to Fluctuations, Non Linearity, and Self-Organization. Pergamon.
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  41. 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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  42. 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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  43. 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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  44. P. Ylikoski (2009). Book Review: Sawyer, R. Keith. (2005). Social Emergence: Societies as Complex Systems. Cambridge, UK: Cambridge University Press. [REVIEW] Philosophy of the Social Sciences 39 (3):527-530.
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  45. 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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  46. 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. R. Badii (1997). Complexity: Hierarchical Structures and Scaling in Physics. Cambridge University Press.
    This is a comprehensive discussion of complexity as it arises in physical, chemical, and biological systems, as well as in mathematical models of nature. Common features of these apparently unrelated fields are emphasised and incorporated into a uniform mathematical description, with the support of a large number of detailed examples and illustrations. The quantitative study of complexity is a rapidly developing subject with special impact in the fields of physics, mathematics, information science, and biology. Because of the variety of the (...)
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  4. Christopher Belanger (2013). On Two Mathematical Definitions of Observational Equivalence: Manifest Isomorphism and Epsilon-Congruence Reconsidered. Studies in History and Philosophy of Science Part B 44 (2):69-76.
    In this article I examine two mathematical definitions of observational equivalence, one proposed by Charlotte Werndl and based on manifest isomorphism, and the other based on Ornstein and Weiss’s ε-congruence. I argue, for two related reasons, that neither can function as a purely mathematical definition of observational equivalence. First, each definition permits of counterexamples; second, overcoming these counterexamples will introduce non-mathematical premises about the systems in question. Accordingly, the prospects for a broadly applicable and purely mathematical definition of observational equivalence (...)
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  5. D. M. Borchert (ed.) (2006). Encyclopedia of Philosophy, Second Edition.
  6. Seamus Bradley, Scientific Uncertainty: A User's Guide. Grantham Institute on Climate Change Discussion Paper.
    There are different kinds of uncertainty. I outline some of the various ways that uncertainty enters science, focusing on uncertainty in climate science and weather prediction. I then show how we cope with some of these sources of error through sophisticated modelling techniques. I show how we maintain confidence in the face of error.
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  7. 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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  8. 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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  9. 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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  10. 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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  11. 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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  12. Matthew W. Parker (1998). Did Poincare Really Discover Chaos? [REVIEW] Studies in the History and Philosophy of Modern Physics 29 (4):575-588.
  13. I. Prigogine (1984). Order Out of Chaos: Man's New Dialogue with Nature. Distributed by Random House.
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  14. 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. R. Badii (1997). Complexity: Hierarchical Structures and Scaling in Physics. Cambridge University Press.
    This is a comprehensive discussion of complexity as it arises in physical, chemical, and biological systems, as well as in mathematical models of nature. Common features of these apparently unrelated fields are emphasised and incorporated into a uniform mathematical description, with the support of a large number of detailed examples and illustrations. The quantitative study of complexity is a rapidly developing subject with special impact in the fields of physics, mathematics, information science, and biology. Because of the variety of the (...)
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  2. 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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  3. Andrew A. Fingelkurts & Alexander A. Fingelkurts (2013). Dissipative Many-Body Model and a Nested Operational Architectonics of the Brain. Physics of Life Reviews 10:103-105.
    This paper briefly review a current trend in neuroscience aiming to combine neurophysiological and physical concepts in order to understand the emergence of spatio-temporal patterns within brain activity by which brain constructs knowledge from multiple streams of information. The authors further suggest that the meanings, which subjectively are experienced as thoughts or perceptions can best be described objectively as created and carried by large fields of neural activity within the operational architectonics of brain functioning.
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  4. Andrew A. Fingelkurts, Alexander A. Fingelkurts & Carlos F. H. Neves (forthcoming). Consciousness as a Phenomenon in the Operational Architectonics of Brain Organization: Criticality and Self-Organization Considerations. Chaos, Solitons and Fractals.
    In this paper we aim to show that phenomenal consciousness is realized by a particular level of brain operational organization and that understanding human consciousness requires a description of the laws of the immediately underlying neural collective phenomena, the nested hierarchy of electromagnetic fields of brain activity – operational architectonics. We argue that the subjective mental reality and the objective neurobiological reality, although seemingly worlds apart, are intimately connected along a unified metastable continuum and are both guided by the universal (...)
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  5. Hermann Haken & Helena Knyazeva (2000). Synergetik: Zwischen Reduktionismus Und Holismus. Philosophia Naturalis 37 (1):21-44.
    Die philosophischen Folgerungen der Synergetik, einer interdisziplinären Theorie der Evolution und Selbstorganisation komplexer nichtlinearer Systeme, werden in diesem Artikel zur Diskussion gestellt. Das sind der weltanschauliche Sinn des Begriffs von der „Nichtlinearität“, die konstruktive Rolle des Chaos in der Evolution, eine neue Vorstellung von diskreten Spektren evolutionärer Wege in komplexen Systemen, die Prinzipien des Aufbaus von komplexem evolutionärem Ganzen, der Integration von komplexen Strukturen, die sich mit verschiedenen Geschwindigkeiten entwickeln, die Methoden des nichtlinearen Managements komplexer Systeme. Die Synergetik entdeckt allgemeingültige (...)
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  6. Helena Knyazeva (2009). Nonlinear Cobweb of Cognition. Foundations of Science 14 (3).
    The modern conception of enactive cognition is under discussion from the standpoint concerning the notions of nonlinear dynamics and synergetics. The contribution of Francisco Varela and his precursors is considered. It is shown that the perceptual and mental processes are bound up with the “architecture” of human body and nonlinear and circular connecting links between the subject of cognition and the world constructed by him can be metaphorically called a nonlinear cobweb of cognition. Cognition is an autopoietic activity because it (...)
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  7. 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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  8. Helena Knyazeva (2004). The Complex Nonlinear Thinking: Edgar Morin's Demand of a Reform of Thinking and the Contribution of Synergetics. World Futures 60 (5 & 6):389 – 405.
    Main principles of the complex nonlinear thinking which are based on the notions of the modern theory of evolution and self-organization of complex systems called also synergetics are under discussion in this article. The principles are transdisciplinary, holistic, and oriented to a human being. The notions of system complexity, nonlinearity of evolution, creative chaos, space-time definiteness of structure-attractors of evolution, resonant influences, nonlinear and soft management are here of great importance. In this connection, a prominent contribution made to system analysis (...)
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  9. Helena Knyazeva (2003). Self-Reflective Synergetics. Systems Research and Behavioral Science 20 (1):53-64.
    An attempt to critically analyse the claims of the theory of self-organization of complex systems (synergetics) to the interdisciplinary generalizations and the universal efficacy of its models is made in the paper. The grounds for transfer of synergetic models to different disciplinary fields are under discussion. It is argued that synergetics is a mental scheme or a heuristic approach to exploring the complex behaviour of systems, rather than a universal key to solving concrete scientific problems. The prospects for development and (...)
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  10. Helena Knyazeva (1999). Synergetics and the Images of Future. Futures 31 (3):281-290.
    The hope of finding new methods of predicting the course of historical processes could be connected with the recent developments of the theory of self-organisation, also called synergetics. It provides us with knowledge of constructive principles of co-evolution of complex social systems, co-evolution of countries and geopolitical regions being at different stages of development, integration of the East and the West, the North and the South. Due to the growth of population on the Earth in blow-up regime, the general and (...)
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  11. Helena Knyazeva (1998). The Synergetic View of Human Creativity. Evolution and Cognition 4 (2):145-155.
    The heuristic value of synergetic models of evolving and self-organizing complex systems as well as their application to epistemological problems is shown in this paper. Nonlinear synergetic models turn out to be fruitful in comprehending epistemological problems such as the nature of human creativity, the functioning of human intuition and imagination, the historical development of science and culture. In the light of synergetics creative thinking can be viewed as a selforganization and self-completion of images and thoughts, filling up gaps in (...)
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  12. 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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  13. 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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  14. 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. Christopher Belanger (2013). On Two Mathematical Definitions of Observational Equivalence: Manifest Isomorphism and Epsilon-Congruence Reconsidered. Studies in History and Philosophy of Science Part B 44 (2):69-76.
    In this article I examine two mathematical definitions of observational equivalence, one proposed by Charlotte Werndl and based on manifest isomorphism, and the other based on Ornstein and Weiss’s ε-congruence. I argue, for two related reasons, that neither can function as a purely mathematical definition of observational equivalence. First, each definition permits of counterexamples; second, overcoming these counterexamples will introduce non-mathematical premises about the systems in question. Accordingly, the prospects for a broadly applicable and purely mathematical definition of observational equivalence (...)
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  3. 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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  4. Andrew A. Fingelkurts & Alexander A. Fingelkurts (2013). Dissipative Many-Body Model and a Nested Operational Architectonics of the Brain. Physics of Life Reviews 10:103-105.
    This paper briefly review a current trend in neuroscience aiming to combine neurophysiological and physical concepts in order to understand the emergence of spatio-temporal patterns within brain activity by which brain constructs knowledge from multiple streams of information. The authors further suggest that the meanings, which subjectively are experienced as thoughts or perceptions can best be described objectively as created and carried by large fields of neural activity within the operational architectonics of brain functioning.
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  5. Gordon G. Globus (2005). Nonlinear Dynamics at the Cutting Edge of Modernity: A Postmodern View. Philosophy, Psychiatry, and Psychology 12 (3):229-234.
  6. 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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  7. Julian Kiverstein & Michael Wheeler (eds.) (2012). Heidegger and Cognitive Science. Palgrave Macmillan.
  8. Helena Knyazeva (1999). Synergetics and the Images of Future. Futures 31 (3):281-290.
    The hope of finding new methods of predicting the course of historical processes could be connected with the recent developments of the theory of self-organisation, also called synergetics. It provides us with knowledge of constructive principles of co-evolution of complex social systems, co-evolution of countries and geopolitical regions being at different stages of development, integration of the East and the West, the North and the South. Due to the growth of population on the Earth in blow-up regime, the general and (...)
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  9. Matthew W. Parker (2009). Computing the Uncomputable; or, the Discrete Charm of Second-Order Simulacra. Synthese 169 (3):447 - 463.
    We examine a case in which non-computable behavior in a model is revealed by computer simulation. This is possible due to differing notions of computability for sets in a continuous space. The argument originally given for the validity of the simulation involves a simpler simulation of the simulation , still further simulations thereof, and a universality conjecture. There are difficulties with that argument, but there are other, heuristic arguments supporting the qualitative results. It is urged, using this example, that absolute (...)
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  10. Matthew W. Parker (2003). Undecidability in Rn: Riddled Basins, the KAM Tori, and the Stability of the Solar System. Philosophy of Science 70 (2):359-382.
    Some have suggested that certain classical physical systems have undecidable long-term behavior, without specifying an appropriate notion of decidability over the reals. We introduce such a notion, decidability in (or d- ) for any measure , which is particularly appropriate for physics and in some ways more intuitive than Ko's (1991) recursive approximability (r.a.). For Lebesgue measure , d- implies r.a. Sets with positive -measure that are sufficiently "riddled" with holes are never d- but are often r.a. This explicates Sommerer (...)
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  11. Matthew W. Parker (1998). Did Poincare Really Discover Chaos? [REVIEW] Studies in the History and Philosophy of Modern Physics 29 (4):575-588.
  12. 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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  13. 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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  14. 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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  15. 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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  16. 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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  17. Jessica Wahman (2005). Determined by Chaos: The Nonlinear Dynamics of Free Will. Philosophy, Psychiatry, and Psychology 12 (3):235-237.
  18. 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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  19. Jessica M. Wilson (forthcoming). Nonlinearity and Metaphysical Emergence. In Stephen Mumford & Matthew Tugby (eds.), Metaphysics and Science.
    The nonlinearity of a composite system, whereby certain of its features (including powers and behaviors) cannot be seen as linear or other broadly additive combinations of features of the system's composing entities, has been frequently seen as a mark of metaphysical emergence, coupling the dependence of a composite system on an underlying system of composing entities with the composite system's ontological autonomy from its underlying system. But why think that nonlinearity is a mark of emergence, and moreover, of metaphysical rather (...)
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  20. 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. The General Systems Theory: An Adequate (2002). Paulina Taboada. In Paulina Taboada, Kateryna Fedoryka Cuddeback & Patricia Donohue-White (eds.), Person, Society, and Value: Towards a Personalist Concept of Health. Kluwer Academic Pub..
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  2. Evandro Agazzi (1978). Systems Theory and the Problem of Reductionism. Erkenntnis 12 (3):339 - 358.
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  3. 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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  4. 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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  5. A. Bielecki, Andrzej Kokoszka & P. Holas (2000). Dynamic Systems Theory Approach to Consciousness. International Journal of Neuroscience 104 (1):29-47.
  6. 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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