Adaptive Learning in Weighted Network Games

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

Research output: Working paper / PreprintWorking paper

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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.
Original languageEnglish
PublisherMaastricht University, Graduate School of Business and Economics
Publication statusPublished - 24 Oct 2017

Publication series

SeriesGSBE Research Memoranda

JEL classifications

  • c72 - Noncooperative Games
  • d74 - "Conflict; Conflict Resolution; Alliances"
  • d83 - "Search; Learning; Information and Knowledge; Communication; Belief"
  • d85 - Network Formation and Analysis: Theory
  • h41 - Public Goods


  • Networks
  • Learning
  • Public Goods
  • Potential Games

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