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Agent-based Computational Economics: a Methodological Appraisal

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  • Paola Tubaro

Abstract

This paper is an overview of "Agent-based Computational Economics (ACE)", an emerging approach to the study of decentralized market economies, in methodological perspective. It summarizes similarities and differences with respect to conventional economic models, outlines the unique methodological characteristics of this approach, and discusses its implications for economic methodology as a whole. While ACE rejoins the reflection on the unintended social consequences of purposeful individual action which is constitutive of economics as a discipline, the paper shows that it complements state-of the-art research in experimental and behavioral economics. In particular, the methods and techniques of ACE have reinforced the laboratory finding that fundamental economic results rely less on rational choice theory than is usually assumed, and have provided insight into the importance of market structures and rules in addition to individual choice. In addition, ACE has enlarged the range of inter-individual interactions that are of interest for economists. In this perspective, ACE provides the economist‘s toolbox with valuable supplements to existing economic techniques rather than proposing a radical alternative. Despite some open methodological questions, it has potential for better integration into economics in the future.

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File URL: http://economix.fr/pdf/dt/2009/WP_EcoX_2009-42.pdf
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Bibliographic Info

Paper provided by University of Paris West - Nanterre la Défense, EconomiX in its series EconomiX Working Papers with number 2009-42.

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Length: 26 pages
Date of creation: 2009
Date of revision:
Handle: RePEc:drm:wpaper:2009-42

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Related research

Keywords: Agent-based Computational Economics; Economic Methodology; Experimental Economics.;

This paper has been announced in the following NEP Reports:

References

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  1. Sean Crockett & Stephen Spear & Shyam Sunder, 1899. "Learning Competitive Equilibrium," GSIA Working Papers 2003-E18, Carnegie Mellon University, Tepper School of Business.
  2. Robert Axtell, 2005. "The Complexity of Exchange," Economic Journal, Royal Economic Society, vol. 115(504), pages F193-F210, 06.
  3. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011 Elsevier.
  4. Arthur, W Brian, 1993. "On Designing Economic Agents That Behave Like Human Agents," Journal of Evolutionary Economics, Springer, vol. 3(1), pages 1-22, February.
  5. Olivier Barreteau, 2003. "Our Companion Modelling Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 6(2), pages 1.
  6. Rosaria Conte & Mario Paolucci, 2001. "Intelligent Social Learning," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(1), pages 3.
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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Agent-based computational economics in Milan
    by paolatubaro in Paola Tubaro's Blog on 2010-05-20 22:18:32
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Cited by:
  1. Romar Correa, 2011. "On Concurrent Solutions in Differential Games," Business Systems Research, Society for Promotion of Business Information Technology (BIT), vol. 2(1), pages 17-23.

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