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Dynamics in near-potential games

Author

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  • Candogan, Ozan
  • Ozdaglar, Asuman
  • Parrilo, Pablo A.

Abstract

We consider discrete-time learning dynamics in finite strategic form games, and show that games that are close to a potential game inherit many of the dynamical properties of potential games. We first study the evolution of the sequence of pure strategy profiles under better/best response dynamics. We show that this sequence converges to a (pure) approximate equilibrium set whose size is a function of the “distance” to a given nearby potential game. We then focus on logit response dynamics, and provide a characterization of the limiting outcome in terms of the distance of the game to a given potential game and the corresponding potential function. Finally, we turn attention to fictitious play, and establish that in near-potential games the sequence of empirical frequencies of player actions converges to a neighborhood of (mixed) equilibria, where the size of the neighborhood increases according to the distance to the set of potential games.

Suggested Citation

  • Candogan, Ozan & Ozdaglar, Asuman & Parrilo, Pablo A., 2013. "Dynamics in near-potential games," Games and Economic Behavior, Elsevier, vol. 82(C), pages 66-90.
  • Handle: RePEc:eee:gamebe:v:82:y:2013:i:c:p:66-90
    DOI: 10.1016/j.geb.2013.07.001
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    2. Hlafo Alfie Mimun & Matteo Quattropani & Marco Scarsini, 2022. "Best-Response dynamics in two-person random games with correlated payoffs," Papers 2209.12967, arXiv.org, revised Jan 2024.
    3. Carlos Alós-Ferrer & Nick Netzer, 2015. "Robust stochastic stability," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 58(1), pages 31-57, January.
    4. Hódsági, Kristóf & Szabó, György, 2019. "Bursts in three-strategy evolutionary ordinal potential games on a square lattice," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1379-1387.
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    8. Jinlong Lei & Uday V. Shanbhag, 2020. "Asynchronous Schemes for Stochastic and Misspecified Potential Games and Nonconvex Optimization," Operations Research, INFORMS, vol. 68(6), pages 1742-1766, November.
    9. Torsten Heinrich & Yoojin Jang & Luca Mungo & Marco Pangallo & Alex Scott & Bassel Tarbush & Samuel Wiese, 2023. "Best-response dynamics, playing sequences, and convergence to equilibrium in random games," International Journal of Game Theory, Springer;Game Theory Society, vol. 52(3), pages 703-735, September.
    10. Torsten Heinrich & Yoojin Jang & Luca Mungo & Marco Pangallo & Alex Scott & Bassel Tarbush & Samuel Wiese, 2021. "Best-response dynamics, playing sequences, and convergence to equilibrium in random games," Papers 2101.04222, arXiv.org, revised Nov 2022.
    11. Pangallo, Marco & Heinrich, Torsten & Jang, Yoojin & Scott, Alex & Tarbush, Bassel & Wiese, Samuel & Mungo, Luca, 2021. "Best-Response Dynamics, Playing Sequences, And Convergence To Equilibrium In Random Games," INET Oxford Working Papers 2021-23, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
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    13. Su-Jin Lee & Young-Jin Park & Han-Lim Choi, 2018. "Efficient sensor network planning based on approximate potential games," International Journal of Distributed Sensor Networks, , vol. 14(6), pages 15501477187, June.

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    More about this item

    Keywords

    Dynamics in games; Near-potential games; Best response dynamics; Logit response dynamics; Fictitious play;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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