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Theory of Conjectural Learning

Author

Listed:
  • Mabel Tidball

    (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier)

  • Alain Jean-Marie

    (NEO - Network Engineering and Operations - Centre Inria d'Université Côte d'Azur - Inria - Institut National de Recherche en Informatique et en Automatique)

  • Tania Jiménez

    (LIA - Laboratoire Informatique d'Avignon - AU - Avignon Université - Centre d'Enseignement et de Recherche en Informatique - CERI)

Abstract

In the context of dynamic games in which players have limited information, we introduce the family of Conjectural Learning procedures. With Conjectural Learning, agents form conjectures about what the opponent will play, as a function of their action or some state variable. They revise these conjectures at each interaction. We prove general properties of Conjectural Learning procedures, comparing their steady-states to the cooperative solution of a corresponding static game with complete information. We then specify simple functional forms of conjectures and analyze the resulting dynamic systems, in terms of steady states and their relation with Pareto solutions of the complete information framework. We illustrate the transient and stationary behavior of these Conjectural Learning procedures in the Fish War model of Levhari and Mirman.

Suggested Citation

  • Mabel Tidball & Alain Jean-Marie & Tania Jiménez, 2026. "Theory of Conjectural Learning," Post-Print hal-05454222, HAL.
    • Handle: RePEc:hal:journl:hal-05454222
    Note: View the original document on HAL open archive server: https://hal.science/hal-05454222v1
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