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Learning with Communication Barriers Due to Overconfidence. What "Model-To-Model Analysis" Can Add to the Understanding of a Problem

Author

Listed:
  • Juliette Rouchier

    (LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - 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)

Abstract

In this paper, we describe a process of validation for an already published model, which relies on the M2M paradigm of work. The initial model showed that over-confident agents, which refuse to communicate with agents whose beliefs differ, disturb collective learning within a population. We produce an analytical model based on probabilistic analysis, that enables us to explain better the process at stake in our first model, and demonstrates that this process is indeed converging. To make sure that the convergence time is meaningful for our question (not just for an infinite number of agents living for an infinite time), we use the analytical model to produce very simple simulations and assess that the result holds in finite contexts.

Suggested Citation

  • Juliette Rouchier & Emily Tanimura, 2016. "Learning with Communication Barriers Due to Overconfidence. What "Model-To-Model Analysis" Can Add to the Understanding of a Problem," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01394206, HAL.
    • Handle: RePEc:hal:cesptp:hal-01394206
    DOI: 10.18564/jasss.3039
    as

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