Adaptive learning in weighted network games

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Pages (from-to)250-264
Number of pages15
JournalJournal of Economic Dynamics & Control
Volume105
DOIs
Publication statusPublished - Aug 2019

Keywords

  • Networks
  • Learning
  • Public goods
  • Potential games
  • PUBLIC-GOODS
  • BEHAVIOR

Cite this

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title = "Adaptive learning in weighted network games",
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.",
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Adaptive learning in weighted network games. / Bayer, Peter; Herings, P. Jean-Jacques; Peeters, Ronald; Thuijsman, Frank.

In: Journal of Economic Dynamics & Control, Vol. 105, 08.2019, p. 250-264.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Bayer, Peter

AU - Herings, P. Jean-Jacques

AU - Peeters, Ronald

AU - Thuijsman, Frank

N1 - data source: no data used

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N2 - 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.

AB - 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.

KW - Networks

KW - Learning

KW - Public goods

KW - Potential games

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KW - BEHAVIOR

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