Edited by Jon Lawhead (University of Southern California)
|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 2009; Auyang 1999; Hooker ms; Mitchell 2012; Dennett 1991|
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