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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 in a framework where the paradigm driving the agents' behavior is left unspecified. The overreaching aim is to gain insights regarding 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 he 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, he 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, 2017. "Paradigm shifts," Economics Discussion Papers 2017-92, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwedp:201792
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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.
    2. R. Guesnerie, 2002. "Anchoring Economic Predictions in Common Knowledge," Econometrica, Econometric Society, vol. 70(2), pages 439-480, March.
    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. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    5. 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.
    6. David Easley & Maureen O'Hara, 2010. "Microstructure and Ambiguity," Journal of Finance, American Finance Association, vol. 65(5), pages 1817-1846, October.
    7. Alan Kirman, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(1), pages 137-156.
    8. 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.
    9. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    10. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    11. 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.
    12. Shiller, Robert J, 1995. "Conversation, Information, and Herd Behavior," American Economic Review, American Economic Association, vol. 85(2), pages 181-185, May.
    13. 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.
    14. Bernard Dumas & Jeff Fleming & Robert E. Whaley, 1998. "Implied Volatility Functions: Empirical Tests," Journal of Finance, American Finance Association, vol. 53(6), pages 2059-2106, December.
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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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