When overconfident agents slow down collective learning
Juliette Rouchier () and
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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 - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université - EHESS - École des hautes études en sciences sociales
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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.
Keywords: bounded-confidence; belief dissemination; social influence; simulation agents; agent-based computational economics (search for similar items in EconPapers)
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Published in SIMULATION, SAGE Publications, 2012, 88 (1), pp.33-49. ⟨10.1177/0037549711428948⟩
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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:hal-00623966
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