Complex Systems

Edited by Jon Lawhead (University of Southern California)
About this topic
Summary The study of complex systems is an interdisciplinary field that examines how the interaction of many parts can give rise to holistic collective behavior at the system level.  Contemporary complex systems science is a synthesis of many different areas of inquiry, including non-linear dynamical systems theory, chaos theory, cybernetics, control theory, information theory, multiscale modeling, and non-equilibrium statistical mechanics.  There is as-yet no widely accepted general definition of "complex system," but a few common themes or properties can be observed.  Complex systems frequently tend to display self-organization, autopoiesis, non-linearity in their dynamics, chaotic behavior, emergent properties, adaptation or some combination of these traits.  In the natural sciences, the global climate, the economy, neural networks, and living organisms are among the systems generally regarded as "complex," and the methods or tools of complex systems theory are frequently applied to their study.  A holistic understanding of complex systems frequently involves contributions from many areas, including both the social and physical sciences as well as the humanities.  Given the challenges associated with coordinating this kind of vast interdisciplinary collaboration, philosophy--with its emphasis on what Wilfrid Sellars famously called "bridge-building" between disparate disciplines--has a clear and obvious role to play.  The study of complex systems also overlaps with a number of traditional problems in the philosophy of science and metaphysics, including mereology, the nature of laws and explanations, supervenience, emergence and reduction, the scale-relativity of ontology, and functionalism.  Applied philosophical issues raised by complex systems include: how do we understand causation and explanation in systems that require analysis from multiple perspectives, and which resist hierarchical organizational schemes?  Can computer simulations and multi-scale modeling provide a new way to explore strong emergence and self-organization?  How can we design organizational systems to most effectively engage in collaborative decision making while still mitigating the risks associated with large-scale collective action problems?  These questions are of extremely general importance as we move forward into the 21st century, and how we choose to address them will have implications for a diverse set of topics: challenges like how to meet the problems posed by anthropogenic climate change, how the digital revolution stands to impact our social organizations, how human society will cope with increasingly autonomous artificially intelligent agents, and how to design or manage the behavior of novel complex adaptive organisms all involve coming to grips with complexity theoretic concepts to some degree.
Key works Work in the fields from which modern complexity theory emerged, including information theory (Weaver 1948), chaos theory (Lorenz 1963; Prigogine 1984), statistical physics (Anderson 1994), and cybernetics (Simon 1962) are important for a foundational understanding of the relevant concepts.  Important early works in complexity theory include Lloyd & Pagels 1988 and Gell-Mann 1995.  More contemporary contributions have been made by Bar‐Yam 2004 (which explores the mathematical foundations of strong emergence), Ladyman et al 2013 (which offers a taxonomy of definitions of 'complexity'), and Hooker 2013 (which explores the physical underpinnings of complex dynamics).
Introductions Mitchell 2009Auyang 1999Hooker msMitchell 2012Dennett 1991
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  1. The Poetics of Purpose.Victoria N. Alexander - 2009 - Biosemiotics 2 (1):77-100.
    Hackles have been raised in biosemiotic circles by T. L. Short’s assertion that semiosis, as defined by Peirce, entails “acting for purposes” and therefore is not found below the level of the organism (2007a:174–177). This paper examines Short’s teleology and theory of purposeful behavior and offers a remedy to the disagreement. Remediation becomes possible when the issue is reframed in the terms of the complexity sciences, which allows intentionality to be understood as the interplay between local and global aspects of (...)
  2. Toward 'Complexics' as a Transdiscipline.Albert Bastardas I. Boada - unknown
    The proposed transdisciplinary field of ‘complexics’ would bring together allcontemporary efforts in any specific disciplines or by any researchersspecifically devoted to constructing tools, procedures, models and conceptsintended for transversal application that are aimed at understanding andexplaining the most interwoven and dynamic phenomena of reality. Our aimneeds to be, as Morin says, not “to reduce complexity to simplicity, [but] totranslate complexity into theory”.New tools for the conception, apprehension and treatment of the data ofexperience will need to be devised to complement existing (...)
  3. Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis.Michael Baumgartner & Alrik Thiem - 2015 - R Journal 7 (1):176-184.
  4. Optimal Synchronization of Two Different in-Commensurate Fractional-Order Chaotic Systems with Fractional Cost Function.Reza Behinfaraz & Mohammadali Badamchizadeh - 2016 - Complexity 21 (S1):401-416.
  5. Searching for Complex CA Rules with GAs.Eleonora Bilotta, Antonio Lafusa & Pietro Pantano - 2003 - Complexity 8 (3):56-67.
  6. Conceptualizing Social Systems: A Critical Argument for the Nonlinear Perspective.Robert J. Brodnick - 2000 - Dissertation, Temple University
    For thousands of years societies, cultures, and organizations framed their worlds through a variety of lenses that allowed them to peer into nature and into themselves. Over time these lenses changed. The western world embraced a scientific and linear paradigm. Recently, fresh approaches have arisen through lenses called the new sciences. Discoveries are extending beyond their birthplaces in the physical and biological sciences to impact organizational science and practice. This dissertation compares two perspectives affecting social systems---linear and nonlinear. To give (...)
  7. Five Frames for the “Decameron”: Communication and Social Systems in the “Cornice.”. [REVIEW]Anthony Cassell - 1985 - Speculum 60 (1):190-192.
  8. Agency, Emergence and Organization.Philip Clayton & Stuart A. Kauffman - 2006 - Biology and Philosophy 21:501-521.
  9. Chaos and Order the Complex Structure of Living Systems.Friedrich Cramer - 1993
  10. The Value of Weather Event Science for Pending UN Climate Policy Decisions.Justin Donhauser - forthcoming - Ethics, Policy and Environment.
    This essay furthers debate about the burgeoning science of Probabilistic Event Attribution (PEA) and its relevance to imminent climate policy decisions. It critically examines Allen Thompson and Friederike Otto’s recent arguments concerning the implications of PEA studies for how the United Nations Framework Convention on Climate Change (UNFCCC) policy framework should be revised during the 2016 ‘review and decision.’ I show that their contention that PEA studies cannot usefully inform decision-making about adaptation policies and strategies is misguided and argue that (...)
  11. In What Sense is the Kolmogorov-Sinai Entropy a Measure for Chaotic Behaviour?--Bridging the Gap Between Dynamical Systems Theory and Communication Theory.R. Frigg - 2004 - British Journal for the Philosophy of Science 55 (3):411-434.
  12. Organization or Religious Life in Odessos.Zlatozara Gočeva - 1996 - Kernos 9:121-127.
  13. Chaotic Logic Language, Thought, and Reality From the Perspective of Complex Systems Science.Ben Goertzel - 1994
  14. Densities and Entropies in Cellular Automata.Pierre Guillon & Charalampos Zinoviadis - 2012 - In S. Barry Cooper (ed.), How the World Computes. pp. 253--263.
  15. Classification of Emergence and its Relation to Self‐Organization.Julianne D. Halley & David A. Winkler - 2008 - Complexity 13 (5):10-15.
  16. Emergent Behavior in Two Complex Cellular Automata Rule Sets.Christopher J. Hazard, Kyle R. Kimport & David H. Johnson - 2005 - Complexity 10 (5):45-55.
  17. Regularity in Nonlinear Dynamical Systems.D. Lynn Holt & R. Glynn Holt - 1993 - British Journal for the Philosophy of Science 44 (4):711-727.
    Laws of nature have been traditionally thought to express regularities in the systems which they describe, and, via their expression of regularities, to allow us to explain and predict the behavior of these systems. Using the driven simple pendulum as a paradigm, we identify three senses that regularity might have in connection with nonlinear dynamical systems: periodicity, uniqueness, and perturbative stability. Such systems are always regular only in the second of these senses, and that sense is not robust enough to (...)
  18. Self‐Assembling Systems.Paul Humphreys - 2006 - Philosophy of Science 73 (5):595-604.
    Starting with the view that methodological constraints depend upon the nature of the system investigated, a tripartite division between theoretical, semitheoretical, and empirical discoveries is made. Many nanosystems can only be investigated semitheoretically or empirically, and this aspect leads to some nanophenomena being weakly emergent. Self-assembling systems are used as an example, their existence suggesting that the class of systems that is not Kim-reducible may be quite large.
  19. The Age of Bifurcation Understanding the Changing World.Ervin Laszlo - 1991
  20. Notes From the Existential Underground: The Universe as a Complex Emergent System.Michelle Kathryn McGee - 2016 - Cosmos and History: The Journal of Natural and Social Philosophy 12 (2):172-183.
  21. Complexity and Diversity.E. R. Nakamura & Complexity and Diversity Workshop - 1997
  22. Non-Linear Lattice Models: Complex Dynamics, Pattern Formation and Aspects of Chaos.J. Pouget - 2005 - Philosophical Magazine 85 (33-35):4067-4094.
  23. Syntactic Autonomy, Cellular Automata, and RNA Editing: Or Why Self-Organization Needs Symbols to Evolve and How It Might Evolve Them.Luis M. Rocha - 2000 - In Jerry L. R. Chandler & Gertrudis van de Vijver (eds.), Closure: Emergent Organizations and Their Dynamics. New York Academy of Sciences. pp. 901.
  24. Complex Dynamics of Macroeconomic Collapse.Barkley Rosser - manuscript
    This paper presents a view of the process of transition from planned command socialism to mixed market capitalism involving nonlinear complex dynamical phenomena. After the former institutional structure disappears a coordination failure can bring about macroeconomic collapse as in almost all of the former Soviet bloc or macroeconomic boom as in China. A closely linked phenomenon is the rise of the underground economy as inflation and income inequality increase. This can lead to a jump from one equilibrium to a very (...)
  25. Self-Fulfilling Chaotic Mistakes: Some Examples and Implications.Barkley Rosser - manuscript
    In talks given early in the 1990s, Jean-Michel Grandmont (1998) introduced the concept of the self-fulfilling mistake. This phenomenon can emerge when economic agents cannot distinguish between randomness and determinism, a situation that can occur when the underlying true dynamics are chaotic (Radunskaya, 1994), although such a situation could arise with other forms of complex nonlinear dynamics besides those involving chaotic dynamics. In such a situation, agents may be unable to discern the true dynamics and may adopt simple, boundedly rational (...)
  26. Complex Ecologic-Economic Dynamics and Environmental Policy Forthcoming, Ecological Economics.J. Barkley Rosser - unknown
    Various complex dynamics in ecologic-economic systems are presented with an emphasis upon models of global warming dynamics and fishery dynamics. Chaotic and catastrophic dynamic patterns are shown to be possible, along with other complex dynamics arising from nonlinearities in such combined systems. Problems associated with amplified oscillations due to these nonlinear interactions in the combined interactions of human economic decisionmaking with ecological dynamics are identified and discussed. Implications for policy are examined with strong recommendations for greater emphasis in particular upon (...)
  27. Emergence, Complexity and Reductionism.Tushar Kanti Sarkar - 1979 - Dissertation, University of Waterloo (Canada)
  28. On the Challenge of Developing a Formal Mathematical Theory for Establishing Emergence in Complex Systems.Daniel Solow - 2000 - Complexity 6 (1):49-52.
  29. The Emergence of Knowledge in Organization.Ralph Stacey - 2000 - Emergence: Complexity and Organization 2 (4):23-39.
  30. Emergence and Community: The Story of Three Complex Adaptive Entities.Richard W. Stackman, Linda S. Henderson & Deborah P. Bloch - 2006 - Emergence: Complexity and Organization 8 (3).
  31. Inference Behavior in Multiple-Cue Tasks Involving Both Linear and Nonlinear Relations.David A. Summers & Kenneth R. Hammond - 1966 - Journal of Experimental Psychology 71 (5):751.
  32. The Strife of Systems.Toledo Sebastián Álvarez - 1988 - Theoria 4 (1):256-259.
  33. The Lure of Modern Science Fractal Thinking.Bruce J. West & William D. Deering - 1995
  34. Leadership and the New Science.Margaret J. Wheatley - 1993 - Performance Resources.
  35. Basins of Attraction in Cellular Automata.Andrew Wuensche - 2000 - Complexity 5 (6):19-25.
  36. Essays and Commentaries-Basins of Attraction in Cellular Automata.Andrew Wuensche - 2000 - Complexity 5 (6):19-25.
  37. Adaptive-Impulsive Function Projective Synchronization for a Class of Time-Delay Chaotic Systems.Song Zheng - 2016 - Complexity 21 (2):333-341.
  38. Further Results on the Impulsive Synchronization of Uncertain Complex-Variable Chaotic Delayed Systems.Song Zheng - 2016 - Complexity 21 (5):131-142.
  39. Synchronization Analysis of Complex-Variable Chaotic Systems with Discontinuous Unidirectional Coupling.Song Zheng - 2016 - Complexity 21 (6):343-355.
  1. Local Lyapunov Exponents Computed From Observed Data.H. D. I. Abarbanel - 1992 - Journal of Nonlinear Science 2 (3):343-365.
    We develop methods for determining local Lyapunov exponents from observations of a scalar data set. Using average mutual information and the method of false neighbors, we reconstruct a multivariate time series, and then use local polynomial neighborhood-to-neighborhood maps to determine the phase space partial derivatives required to compute Lyapunov exponents. In several examples we demonstrate that the methods allow one to accurately reproduce results determined when the dynamics is known beforehand. We present a new recursive QR decomposition method for finding (...)
  2. Voltage Transformer Ferroresonance Analysis Using Multiple Scales Method and Chaos Theory.A. Abbasi, S. H. Fathi, G. B. Gharehpatian, A. Gholami & H. R. Abbasi - 2013 - Complexity 18 (6):34-45.
  3. Reviews of the Reviews.Bruce Abell, L. M. Simmons, Michael McMaster & Thomas Petzinger - 1999 - Emergence: Complexity and Organization 1 (2):201-206.
  4. Chaos, Gaia, Eros a Chaos Pioneer Uncovers the Three Great Streams of History.Ralph Abraham - 1994
  5. Le Chaos En Economie.Issa Ado - 1992 - Journal de Economistes Et des Etudes Humaines 3 (1):109-126.
  6. Global Chaos Synchronization of New Chaotic System Using Linear Active Control.Israr Ahmad, Azizan Bin Saaban, Adyda Binti Ibrahim & Mohammad Shahzad - 2016 - Complexity 21 (1):379-386.
  7. In the Shadow of Chaos.Stephen Amott - 1999 - Philosophy Today 43 (1):49-56.
  8. Children in Chaos.Mary Anderson - 1996 - Newsletter of the Society for the Advancement of American Philosophy 24 (74):24-27.
  9. Reviews: Coping with Uncertainty, Insights From the New Sciences of Chaos, Self-Organization, and Complexity, Uri Merry. [REVIEW]Philip Anderson & Jack Cohen - 1999 - Emergence: Complexity and Organization 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.
  10. Book Review: Quantum Chaos-An Introduction. [REVIEW]S. M. Anlage - 2000 - Foundations of Physics 30 (7):1135-1138.
  11. The Fuzzy Logic of Chaos and Probabilistic Inference.I. Antoniou & Z. Suchanecki - 1997 - Foundations of Physics 27 (3):333-362.
    The logic of a physical system consists of the elementary observables of the system. We show that for chaotic systems the logic is not any more the classical Boolean lattice but a kind of fuzzy logic which we characterize for a class of chaotic maps. Among other interesting properties the fuzzy logic of chaos does not allow for infinite combinations of propositions. This fact reflects the instability of dynamics and it is shared also by quantum systems with diagonal singularity. We (...)
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