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Memory retrieval and harshness of conflict in the hawk–dove game

Author

Listed:
  • Ennio Bilancini

    (IMT School for Advanced Studies Lucca)

  • Leonardo Boncinelli

    (University of Florence)

  • Sebastian Ille

    (New College of the Humanities)

  • Eugenio Vicario

    (University of Florence)

Abstract

We study the long-run dynamics of a repeated non-symmetric hawk–dove type interaction between agents of two different populations. Agents choose a strategy based on their previous experience with the other population by sampling from a collective memory of past interactions. We assume that the sample size differs between populations and define a measure of harshness of conflict in the hawk–dove interaction. We then show how the properties of the long-run equilibrium depend on the harshness of conflict and the relative length of the sample. In symmetric interactions, if conflict is harsh, the population which samples relatively more past interactions is able to appropriate a higher payoff in the long-run, while the population with a relatively smaller sample does so if conflict is mild. These results hold subject to constraints on the sample size which we discuss in detail. We further extend our results to non-symmetric hawk–dove games.

Suggested Citation

  • Ennio Bilancini & Leonardo Boncinelli & Sebastian Ille & Eugenio Vicario, 2022. "Memory retrieval and harshness of conflict in the hawk–dove game," Economic Theory Bulletin, Springer;Society for the Advancement of Economic Theory (SAET), vol. 10(2), pages 333-351, October.
    • Handle: RePEc:spr:etbull:v:10:y:2022:i:2:d:10.1007_s40505-022-00237-z
    DOI: 10.1007/s40505-022-00237-z
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    Cited by:

    1. Srinivas Arigapudi & Yuval Heller & Amnon Schreiber, 2021. "Sampling dynamics and stable mixing in hawk-dove games," Papers 2107.08423, arXiv.org, revised Jun 2022.
    2. Bilancini, Ennio & Boncinelli, Leonardo & Vicario, Eugenio, 2024. "Memory retrieval in the demand game with a few possible splits: Unfair conventions emerge in fair settings," Journal of Economic Dynamics and Control, Elsevier, vol. 165(C).
    3. Arigapudi, Srinivas & Heller, Yuval & Schreiber, Amnon, 2021. "Sampling Dynamics and Stable Mixing in Hawk–Dove Games," MPRA Paper 108819, University Library of Munich, Germany.
    4. Srinivas Arigapudi & Yuval Heller & Amnon Schreiber, 2023. "Heterogeneous Noise and Stable Miscoordination," Papers 2305.10301, arXiv.org.

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