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The Evolution of Conventions under Condition-Dependent Mistakes

Author

Listed:
  • Ennio Bilancini
  • Leonardo Boncinelli

    (Dipartimento di Scienze per l'Economia e l'Impresa)

Abstract

In this paper we study the long run convention emerging from stag-hunt interactions when errors converge to zero at a rate that is positively related to the payoff earned in the previous period. We refer to such errors as condition-dependent mistakes. We find that, if interactions are sufficiently stable over time, then the payoff-dominant convention emerges in the long run. Moreover, if interactions are neither too stable nor too volatile, then the risk-dominant convention is selected in the long run. Finally, if interactions are quite volatile, then the maximin convention emerges even if it is not risk-dominant. We introduce the notion of \emph{condition-adjusted-risk-dominance} to characterize the convention emerging in the long run under condition-dependent mistakes. We contrast these results with the results obtained under alternative error models: uniform mistakes, i.e., errors converge to zero at a rate that is constant over states, and payoff-dependent mistakes, i.e., errors converge to zero at a rate that depends on expected losses.

Suggested Citation

  • Ennio Bilancini & Leonardo Boncinelli, 2016. "The Evolution of Conventions under Condition-Dependent Mistakes," Working Papers - Economics wp2016_11.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    • Handle: RePEc:frz:wpaper:wp2016_11.rdf
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    Cited by:

    1. Dolgopolov, Arthur, 2024. "Reinforcement learning in a prisoner's dilemma," Games and Economic Behavior, Elsevier, vol. 144(C), pages 84-103.
    2. Arigapudi, Srinivas, 2020. "Exit from equilibrium in coordination games under probit choice," Games and Economic Behavior, Elsevier, vol. 122(C), pages 168-202.
    3. Bilancini, Ennio & Boncinelli, Leonardo & Newton, Jonathan, 2020. "Evolution and Rawlsian social choice in matching," Games and Economic Behavior, Elsevier, vol. 123(C), pages 68-80.
    4. Sawa, Ryoji & Wu, Jiabin, 2023. "Statistical inference in evolutionary dynamics," Games and Economic Behavior, Elsevier, vol. 137(C), pages 294-316.
    5. Hwang, Sung-Ha & Rey-Bellet, Luc, 2021. "Positive feedback in coordination games: Stochastic evolutionary dynamics and the logit choice rule," Games and Economic Behavior, Elsevier, vol. 126(C), pages 355-373.
    6. Jonathan Newton, 2018. "Evolutionary Game Theory: A Renaissance," Games, MDPI, vol. 9(2), pages 1-67, May.
    7. 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).
    8. John Lynham & Philip R. Neary, 2024. "Tiebout sorting in online communities," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 73(3), pages 1149-1174, October.
    9. Irene Crimaldi & Pierre-Yves Louis & Ida Minelli, 2020. "Interacting non-linear reinforced stochastic processes: Synchronization and no-synchronization," Working Papers hal-02910341, HAL.
    10. Sawa, Ryoji, 2021. "A stochastic stability analysis with observation errors in normal form games," Games and Economic Behavior, Elsevier, vol. 129(C), pages 570-589.
    11. Emerson Melo, 2022. "On the uniqueness of quantal response equilibria and its application to network games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 74(3), pages 681-725, October.
    12. Bilancini, Ennio & Boncinelli, Leonardo & Nax, Heinrich H., 2021. "What noise matters? Experimental evidence for stochastic deviations in social norms," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    13. Roberto Rozzi, 2021. "Competing Conventions with Costly Information Acquisition," Games, MDPI, vol. 12(3), pages 1-29, June.
    14. Nax, Heinrich H. & Newton, Jonathan, 2019. "Risk attitudes and risk dominance in the long run," Games and Economic Behavior, Elsevier, vol. 116(C), pages 179-184.
    15. Abhimanyu Khan, 2021. "Evolution of conventions in games between behavioural rules," Economic Theory Bulletin, Springer;Society for the Advancement of Economic Theory (SAET), vol. 9(2), pages 209-224, October.
    16. Eugenio Vicario, 2021. "Imitation and Local Interactions: Long Run Equilibrium Selection," Games, MDPI, vol. 12(2), pages 1-19, April.
    17. Sawa, Ryoji, 2021. "A prospect theory Nash bargaining solution and its stochastic stability," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 692-711.
    18. Sawa, Ryoji & Wu, Jiabin, 2018. "Reference-dependent preferences, super-dominance and stochastic stability," Journal of Mathematical Economics, Elsevier, vol. 78(C), pages 96-104.
    19. Sawa, Ryoji & Wu, Jiabin, 2018. "Prospect dynamics and loss dominance," Games and Economic Behavior, Elsevier, vol. 112(C), pages 98-124.
    20. Nicola Campigotto, 2021. "Pairwise imitation and evolution of the social contract," Journal of Evolutionary Economics, Springer, vol. 31(4), pages 1333-1354, September.
    21. Bilancini, Ennio & Boncinelli, Leonardo, 2022. "The evolution of conventions in the presence of social competition," Games and Economic Behavior, Elsevier, vol. 133(C), pages 50-57.

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    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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