Adaptive learning in weighted network games

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

Journal of Economic Dynamics and Control, 2019, vol. 105, issue C, 250-264

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.

Keywords: Networks; Learning; Public goods; Potential games (search for similar items in EconPapers)
JEL-codes: C72 D74 D83 D85 H41 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (10)

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Related works:
Working Paper: Adaptive Learning in Weighted Network Games (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:105:y:2019:i:c:p:250-264

DOI: 10.1016/j.jedc.2019.06.004

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