IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-00623966.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://paris1.hal.science/hal-00623966/document
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0037549711428948?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:hal:wpaper:hal-00623966 is not listed on IDEAS
    2. Juliette Rouchier & Emily Tanimura, 2012. "When overconfident agents slow down collective learning," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00623966, HAL.
    3. Baiba Renerte & Jan Hausfeld & Torsten Twardawski, 2020. "Gender, overconfidence, and optimal group composition for investment decisions," TWI Research Paper Series 121, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    4. Helen X. H. Bao & Steven Haotong Li, 2016. "Overconfidence And Real Estate Research: A Survey Of The Literature," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 61(04), pages 1-24, September.
    5. AskariSichani, Omid & Jalili, Mahdi, 2015. "Influence maximization of informed agents in social networks," Applied Mathematics and Computation, Elsevier, vol. 254(C), pages 229-239.
    6. Itzhak Ben-David & John R. Graham & Campbell R. Harvey, 2007. "Managerial Overconfidence and Corporate Policies," NBER Working Papers 13711, National Bureau of Economic Research, Inc.
    7. Dorota Skała, 2008. "Overconfidence in Psychology and Finance – an Interdisciplinary Literature Review," Bank i Kredyt, Narodowy Bank Polski, vol. 39(4), pages 33-50.
    8. Kris Hardies & Diane Breesch & Joël Branson, 2011. "Male and female auditors' overconfidence," Managerial Auditing Journal, Emerald Group Publishing, vol. 27(1), pages 105-118, November.
    9. Pawel Sobkowicz, 2009. "Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-11.
    10. Langnickel, Ferdinand & Zeisberger, Stefan, 2016. "Do we measure overconfidence? A closer look at the interval production task," Journal of Economic Behavior & Organization, Elsevier, vol. 128(C), pages 121-133.
    11. Daniel Fonseca Costa & Francisval Carvalho & Bruno César Moreira & José Willer Prado, 2017. "Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1775-1799, June.
    12. Herz, Holger & Schunk, Daniel & Zehnder, Christian, 2014. "How do judgmental overconfidence and overoptimism shape innovative activity?," Games and Economic Behavior, Elsevier, vol. 83(C), pages 1-23.
    13. Benoît, Jean-Pierre & Dubra, Juan, 2007. "Overconfidence?," MPRA Paper 6017, University Library of Munich, Germany, revised Nov 2007.
    14. 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.
    15. Fellner-Röhling, Gerlinde & Krügel, Sebastian, 2014. "Judgmental overconfidence and trading activity," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 827-842.
    16. María Cecilia Gimenez & Luis Reinaudi & Ana Pamela Paz-García & Paulo Marcelo Centres & Antonio José Ramirez-Pastor, 2021. "Opinion evolution in the presence of constant propaganda: homogeneous and localized cases," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(1), pages 1-11, January.
    17. Oberlechner, Thomas & Osler, Carol, 2012. "Survival of Overconfidence in Currency Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(1), pages 91-113, February.
    18. Danková, Katarína & Servátka, Maroš, 2019. "Gender robustness of overconfidence and excess entry," Journal of Economic Psychology, Elsevier, vol. 72(C), pages 179-199.
    19. Tiwari, Mukesh & Yang, Xiguang & Sen, Surajit, 2021. "Modeling the nonlinear effects of opinion kinematics in elections: A simple Ising model with random field based study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    20. Michailova, Julija, 2010. "Development of the overconfidence measurement instrument for the economic experiment," MPRA Paper 34799, University Library of Munich, Germany, revised Nov 2011.
    21. Song, Xiao & Shi, Wen & Tan, Gary & Ma, Yaofei, 2015. "Multi-level tolerance opinion dynamics in military command and control networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 322-332.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-00623966. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.