Adaptive learning in weighted network games
P. Jean-Jacques Herings (),
Ronald Peeters () and
Journal of Economic Dynamics and Control, 2019, vol. 105, issue C, 250-264
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)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Adaptive Learning in Weighted Network Games (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:105:y:2019:i:c:p:250-264
Access Statistics for this article
Journal of Economic Dynamics and Control is currently edited by J. Bullard, C. Chiarella, H. Dawid, C. H. Hommes, P. Klein and C. Otrok
More articles in Journal of Economic Dynamics and Control from Elsevier
Bibliographic data for series maintained by Nithya Sathishkumar ().