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Adaptive learning in weighted network games

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  • Bayer, Péter
  • Herings, P. Jean-Jacques
  • Peeters, Ronald
  • Thuijsman, Frank

Abstract

This paper studies adaptive learning in the class of weighted network games. This class of games includes applications like research and development within interlinked firms, crime within social networks, the economics of pollution, and defense expenditures within allied nations. We show that for every weighted network game, the set of pure Nash equilibria is non-empty and, generically, finite. Pairs of players are shown to have jointly profitable deviations from interior Nash equilibria. If all interaction weights are either non-negative or non-positive, then Nash equilibria are Pareto inefficient. We show that quite general learning processes converge to a Nash equilibrium of a weighted network game if every player updates with some regularity.

Suggested Citation

  • Bayer, Péter & Herings, P. Jean-Jacques & Peeters, Ronald & Thuijsman, Frank, 2019. "Adaptive learning in weighted network games," Journal of Economic Dynamics and Control, Elsevier, vol. 105(C), pages 250-264.
    • Handle: RePEc:eee:dyncon:v:105:y:2019:i:c:p:250-264
    DOI: 10.1016/j.jedc.2019.06.004
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    Cited by:

    1. Bayer, Péter & Herings, P. Jean-Jacques & Peeters, Ronald, 2021. "Farsighted manipulation and exploitation in networks," Journal of Economic Theory, Elsevier, vol. 196(C).
    2. Orlando, Giuseppe, 2022. "Simulating heterogeneous corporate dynamics via the Rulkov map," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 32-42.
    3. Péter Bayer & György Kozics & Nóra Gabriella Szőke, 2020. "Best-Response Dynamics in Directed Network Games," CEU Working Papers 2020_1, Department of Economics, Central European University.
    4. Meléndez-Jiménez, Miguel A. & Polanski, Arnold, 2020. "Dirty neighbors — Pollution in an interlinked world," Energy Economics, Elsevier, vol. 86(C).
    5. Péter Bayer & György Kozics & Nóra Szőke, 2019. "Best-Response Dynamics in Directed Network Games," CEU Working Papers 2019_3, Department of Economics, Central European University.
    6. P'eter Bayer & Gyorgy Kozics & N'ora Gabriella SzH{o}ke, 2021. "Best-response dynamics in directed network games," Papers 2101.03863, arXiv.org.

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    More about this item

    Keywords

    Networks; Learning; Public goods; Potential games;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D74 - Microeconomics - - Analysis of Collective Decision-Making - - - Conflict; Conflict Resolution; Alliances; Revolutions
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods

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