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Paradigm shifts

Author

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  • Guy Maugis, Pierre-André

Abstract

The author studies the evolution of the number of coexisting beliefs in a financial market. Crucially, he undertakes to do so in a framework where the paradigms, beliefs, and models driving agents behavior are left totally unspecified; i.e., the author does not make any parametric or non-parametric model assumptions. The overreaching aim of this exercise is to characterise the dynamic of the variety of beliefs in an auction-based financial market independently of any assumptions on agents behaviors. The resulting framework may be seen as an abstract agent-based model. In a computer experiment the authors exhibits a cycle between two states, so that either all agents act according to the same belief, or there is no leading belief; i.e., there is one dominating belief, or none. Further, the author finds that the frequency of this cycle is positively linked to the quality of the information available to the agents.

Suggested Citation

  • Guy Maugis, Pierre-André, 2019. "Paradigm shifts," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-9.
    • Handle: RePEc:zbw:ifweej:201943
    DOI: 10.5018/economics-ejournal.ja.2019-43
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    References listed on IDEAS

    as
    1. Arthur, W.B. & Holland, J.H. & LeBaron, B. & Palmer, R. & Tayler, P., 1996. "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Working papers 9625, Wisconsin Madison - Social Systems.
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    3. Hommes, Cars & Huang, Hai & Wang, Duo, 2005. "A robust rational route to randomness in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 29(6), pages 1043-1072, June.
    4. Shiller, Robert J., 1999. "Human behavior and the efficiency of the financial system," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 20, pages 1305-1340, Elsevier.
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    6. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    7. David Easley & Maureen O'Hara, 2010. "Microstructure and Ambiguity," Journal of Finance, American Finance Association, vol. 65(5), pages 1817-1846, October.
    8. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 19-49, August.
    9. Heemeijer, Peter & Hommes, Cars & Sonnemans, Joep & Tuinstra, Jan, 2009. "Price stability and volatility in markets with positive and negative expectations feedback: An experimental investigation," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1052-1072, May.
    10. Alan Kirman, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(1), pages 137-156.
    11. Biondo, Alessio Emanuele, 2018. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-21.
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    More about this item

    Keywords

    agent based model; information cascade; herding behavior;
    All these keywords.

    JEL classification:

    • G40 - Financial Economics - - Behavioral Finance - - - General

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