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  1. Philip Anderson & Jack Cohen (1999). Reviews: Coping with Uncertainty, Insights From the New Sciences of Chaos, Self-Organization, and Complexity, Uri Merry. [REVIEW] Emergence 1 (2):106-108.
    (1999). Reviews: Coping with Uncertainty, Insights from the New Sciences of Chaos, Self-Organization, and Complexity, Uri Merry. Emergence: Vol. 1, No. 2, pp. 106-108.
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  2. 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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  3. Ion C. Baianu (2007). Categorical Ontology of Levels and Emergent Complexity: An Introduction. [REVIEW] Axiomathes 17 (3-4):209-222.
    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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  4. Robert W. Batterman & Homer White (1996). Chaos and Algorithmic Complexity. Foundations of Physics 26 (3):307-336.
    Our aim is to discover whether the notion of algorithmic orbit-complexity can serve to define “chaos” in a dynamical system. We begin with a mostly expository discussion of algorithmic complexity and certain results of Brudno, Pesin, and Ruelle (BRP theorems) which relate the degree of exponential instability of a dynamical system to the average algorithmic complexity of its orbits. When one speaks of predicting the behavior of a dynamical system, one usually has in mind one or more variables in the (...)
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  5. Charles H. Bennett (1986). On the Nature and Origin of Complexity in Discrete, Homogeneous, Locally-Interacting Systems. Foundations of Physics 16 (6):585-592.
    The observed complexity of nature is often attributed to an intrinsic propensity of matter to self-organize under certain (e.g., dissipative) conditions. In order better to understand and test this vague thesis, we define complexity as “logical depth,” a notion based on algorithmic information and computational time complexity. Informally, logical depth is the number of steps in the deductive or causal path connecting a thing with its plausible origin. We then assess the effects of dissipation, noise, and spatial and other symmetries (...)
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  6. 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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  7. Andrew A. Fingelkurts, Alexander A. Fingelkurts & Carlos F. H. Neves (2013). Consciousness as a Phenomenon in the Operational Architectonics of Brain Organization: Criticality and Self-Organization Considerations. Chaos, Solitons and Fractals 55:13-31.
    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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  8. Amit Hagar, Review of M. Thalos' "Without Hierarchy" (OUP 2012).
  9. Amit Hagar, Thou Shalt Not Commute!
    For many among the scientifically informed public, and even among physicists, Heisenberg's uncertainty principle epitomizes quantum mechanics. Nevertheless, more than 86 years after its inception, there is no consensus over the interpretation, scope, and validity of this principle. The aim of this chapter is to offer one such interpretation, the traces of which may be found already in Heisenberg's letters to Pauli from 1926, and in Dirac's anticipation of Heisenberg's uncertainty relations from 1927, that stems form the hypothesis of finite (...)
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  10. 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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  11. John J. Kineman (2011). Relational Science: A Synthesis. [REVIEW] Axiomathes 21 (3):393-437.
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  12. Helena Knyazeva (2009). Nonlinear Cobweb of Cognition. Foundations of Science 14 (3):167-179.
    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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  13. 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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  14. 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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  15. 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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  16. 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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  17. 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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  18. 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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  19. Alexander Reutlinger (forthcoming). Why Is There Universal Macro-Behavior? Renormalization Group Explanation As Non-Causal Explanation. Philosophy of Science.
    Renormalization group (RG) methods are an established strategy to explain how it is possible that microscopically different systems exhibit virtually the same macro behavior when undergoing phase-transitions. I argue – in agreement with Robert Batterman – that RG explanations are non-causal explanations. However, Batterman misidentifies the reason why RG explanations are non-causal: it is not the case that an explanation is non- causal if it ignores causal details. I propose an alternative argument, according to which RG explanations are non-causal explanations (...)
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  20. 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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  21. 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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