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Computability and Evolutionary Complexity: Markets as Complex Adaptive Systems (CAS)

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  • Sheri M. Markose

Abstract

Few will argue that the epi-phenomena of biological systems and socio-economic systems are anything but complex. The purpose of this Feature is to examine critically and contribute to the burgeoning multi-disciplinary literature on markets as complex adaptive systems (CAS). The new sciences of complexity, the principles of self-organisation and emergence along with the methods of evolutionary computation and artificially intelligent agent models have been developed in a multi-disciplinary fashion. The cognoscenti here consider that complex systems whether natural or artificial, physical, biological or socio-economic can be characterised by a unifying set of principles. Further, it is held that these principles mark a paradigm shift from earlier ways of viewing such phenomenon. Copyright 2005 Royal Economic Society.

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Bibliographic Info

Article provided by Royal Economic Society in its journal The Economic Journal.

Volume (Year): 115 (2005)
Issue (Month): 504 (06)
Pages: F159-F192

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Handle: RePEc:ecj:econjl:v:115:y:2005:i:504:p:f159-f192

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  1. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-37, February.
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  5. Sheri M. Markose, 2001. "The New Evolutionary Computational Paradigm of Complex Adaptive Systems: Challenges and Prospects for Economics and Finance," Economics Discussion Papers 532, University of Essex, Department of Economics.
  6. Lux, T. & M. Marchesi, . "Scaling and Criticality in a Stochastic Multi-Agent Model of a Financial Market," Discussion Paper Serie B 438, University of Bonn, Germany, revised Jul 1998.
  7. Shyam Sunder & MODELS A, 2002. "Markets as Artifacts: Aggregate Efficiency from Zero-Intelligence Traders," Yale School of Management Working Papers ysm284, Yale School of Management, revised 01 Sep 2004.
  8. Sorin Solomon, 1998. "Stochastic Lotka-Volterra Systems of Competing Auto-Catalytic Agents Lead Generically to Truncated Pareto Power Wealth Distribution, Truncated Levy Distribution of Market Returns, Clustered Volatility," Papers cond-mat/9803367, arXiv.org.
  9. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
  10. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
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  12. repec:fth:stanho:e-89-28 is not listed on IDEAS
  13. Mishael Milakovic, 2001. "A Statistical Equilibrium Model of Wealth Distribution," Computing in Economics and Finance 2001 214, Society for Computational Economics.
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