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When overconfident agents slow down collective learning

Author

Listed:
  • Juliette Rouchier

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Emily Tanimura

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper presents a model of influence where agents' beliefs are based on an objective reality, such as the properties of an environment. The perception of the objective reality is not direct: all agents know is that the more correct a belief, the more successful the actions that are deduced from this belief. (A pair of agents can influence each other when )Agents can influence eachother by pair when they perform a joint action. They are not only defined by individual beliefs, but also idyosynchratic confidence in their belief - this means that they are not all willing to (engage in action with) act with agents with a different belief and to be influenced by them. We show here that the distribution of confidence in the group has a huge impact on the speed and quality of collective learning and in particular that a small number of overconfident agents can prevent the whole group frow learning properly.

Suggested Citation

  • Juliette Rouchier & Emily Tanimura, 2012. "When overconfident agents slow down collective learning," Post-Print hal-00623966, HAL.
    • Handle: RePEc:hal:journl:hal-00623966
    DOI: 10.1177/0037549711428948
    Note: View the original document on HAL open archive server: https://paris1.hal.science/hal-00623966
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    References listed on IDEAS

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    1. Gilles Hilary & Lior Menzly, 2006. "Does Past Success Lead Analysts to Become Overconfident?," Management Science, INFORMS, vol. 52(4), pages 489-500, April.
    2. Klayman, Joshua & Soll, Jack B. & Gonzalez-Vallejo, Claudia & Barlas, Sema, 1999. "Overconfidence: It Depends on How, What, and Whom You Ask, , , , , , , , ," Organizational Behavior and Human Decision Processes, Elsevier, vol. 79(3), pages 216-247, September.
    3. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    4. Guillaume Deffuant & Frederic Amblard & Gérard Weisbuch, 2002. "How Can Extremism Prevail? a Study Based on the Relative Agreement Interaction Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-1.
    5. Galam, Serge, 1997. "Rational group decision making: A random field Ising model at T = 0," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 238(1), pages 66-80.
    6. Dan Lovallo & Colin Camerer, 1999. "Overconfidence and Excess Entry: An Experimental Approach," American Economic Review, American Economic Association, vol. 89(1), pages 306-318, March.
    7. Katarzyna Sznajd-Weron & Józef Sznajd, 2000. "Opinion Evolution In Closed Community," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1157-1165.
    8. Bruno Biais & Denis Hilton & Karine Mazurier & Sébastien Pouget, 2005. "Judgemental Overconfidence, Self-Monitoring, and Trading Performance in an Experimental Financial Market," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(2), pages 287-312.
    9. Brad M. Barber & Terrance Odean, 2001. "Boys will be Boys: Gender, Overconfidence, and Common Stock Investment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(1), pages 261-292.
    10. Delre, S.A. & Jager, W. & Bijmolt, T.H.A. & Janssen, M.A., 2007. "Targeting and timing promotional activities: An agent-based model for the takeoff of new products," Journal of Business Research, Elsevier, vol. 60(8), pages 826-835, August.
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    Cited by:

    1. Juliette Rouchier & Paola Tubaro & Cécile Emery, 2014. "Opinion transmission in organizations: an agent-based modeling approach," Computational and Mathematical Organization Theory, Springer, vol. 20(3), pages 252-277, September.
    2. Victorien Barbet & Noé Guiraud & Vincent Laperrière & Juliette Rouchier, 2019. "Haggling on Values: Towards Consensus or Trouble," Working Papers halshs-02066846, HAL.
    3. George Butler & Gabriella Pigozzi & Juliette Rouchier, 2019. "Mixing Dyadic and Deliberative Opinion Dynamics in an Agent-Based Model of Group Decision-Making," Complexity, Hindawi, vol. 2019, pages 1-31, August.

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