Computability and Evolutionary Complexity: Markets As Complex Adaptive Systems (CAS)
The purpose of this Feature is to critically examine and to contribute to the burgeoning multi disciplinary literature on markets as complex adaptive systems (CAS). Three economists, Robert Axtell, Steven Durlauf and Arthur Robson who have distinguished themselves as pioneers in different aspects of how the thesis of evolutionary complexity pertains to market environments have contributed to this special issue. Axtell is concerned about the procedural aspects of attaining market equilibria in a decentralized setting and argues that principles on the complexity of feasible computation should rule in or out widely held models such as the Walrasian one. Robson puts forward the hypothesis called the Red Queen principle, well known from evolutionary biology, as a possible explanation for the evolution of complexity itself. Durlauf examines some of the claims that have been made in the name of complex systems theory to see whether these present testable hypothesis for economic models. My overview aims to use the wider literature on complex systems to provide a conceptual framework within which to discuss the issues raised for Economics in the above contributions and elsewhere. In particular, some assessment will be made on the extent to which modern complex systems theory and its application to markets as CAS constitutes a paradigm shift from more mainstream economic analysis.
|Date of creation:||08 Jan 2004|
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- Mishael Milakovic, 2001. "A Statistical Equilibrium Model of Wealth Distribution," Computing in Economics and Finance 2001 214, Society for Computational Economics.
- 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.
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- 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.
- 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, June.
- 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.
- 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.
- Miller, Merton H., 1986. "Financial Innovation: The Last Twenty Years and the Next," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(04), pages 459-471, December.
- 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.
- 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.
- Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
- Axtell, R. & Epstein, J.M. & Young, H.P., 2000. "The Emergence of Classes in a Multi-Agent Bargaining Model," Papers 9, Brookings Institution - Working Papers.
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