Adaptive Learning in Weighted Network Games

Peter Bayer (), P. Jean-Jacques Herings, Ronald Peeters and Frank Thuijsman
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Frank Thuijsman: DKE Scientific staff, RS: FSE DKE NSO

No 25, Research Memorandum from Maastricht University, Graduate School of Business and Economics (GSBE)

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.

JEL-codes: C72 D74 D83 D85 H41 (search for similar items in EconPapers)
Date: 2017-10-24
New Economics Papers: this item is included in nep-gth and nep-mic
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Adaptive learning in weighted network games (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:unm:umagsb:2017025

DOI: 10.26481/umagsb.2017025

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