Adaptive learning in weighted network games

Peter Bayer, P.J.J. Herings*, Ronald Peeters, Frank Thuijsman

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


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. (C) 2019 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)250-264
JournalJournal of Economic Dynamics & Control
Publication statusPublished - Aug 2019
Externally publishedYes


  • Networks
  • Learning
  • Public goods
  • Potential games


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