Adaptive learning in weighted network games

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 ev...

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Bibliographic Details
Published in:Journal of economic dynamics & control 2019-08, Vol.105, p.250-264
Main Authors: Bayer, Péter, Herings, P. Jean-Jacques, Peeters, Ronald, Thuijsman, Frank
Format: Article
Language:English
Subjects:
Learning
Networks
Potential games
Public goods
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Summary: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.
ISSN:0165-1889
1879-1743
DOI:10.1016/j.jedc.2019.06.004