Template-type: ReDif-Paper 1.0
Author-Name: Bayer, Péter
Author-workplace-name: Microeconomics & Public Economics, RS: GSBE ETBC
Author-Name: Herings, P. Jean-Jacques
Author-workplace-name: Microeconomics & Public Economics, RS: GSBE ETBC
Author-Name: Peeters, Ronald
Author-workplace-name: Microeconomics & Public Economics, RS: GSBE ETBC
Author-Name: Thuijsman, Frank
Author-workplace-name: DKE Scientific staff, RS: FSE DKE NSO
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.
Classification-JEL: c72,d74,d83,d85,h41
Series: GSBE Research Memoranda
Creation-Date: 20171024
Number: 025
File-URL: https://cris.maastrichtuniversity.nl/ws/files/16789230/RM17025.pdf
File-Format: application/pdf
File-Size: 510749
Handle: Repec:unm:umagsb:2017025
DOI: 10.26481/umagsb.2017025