TY - GEN
T1 - On the convergence of regret minimization dynamics in concave games
AU - Dar, Eyal Even
AU - Mansour, Yishay
AU - Nadav, Uri
PY - 2009
Y1 - 2009
N2 - We study a general sub-class of concave games which we call socially concave games. We show that if each player follows any no-external regret minimization procedure then the dynamics will converge in the sense that both the average action vector will converge to a Nash equilibrium and that the utility of each player will converge to her utility in that Nash equilibrium. We show that many natural games are indeed socially con- cave games. Speci cally, we show that linear Cournot com- petition and linear resource allocation games are socially- concave games, and therefore our convergence result applies to them. In addition, we show that a simple best response dynamics might diverge for linear resource allocation games, and is known to diverge for linear Cournot competition. For the TCP congestion games we show that \near" the equilib- rium the games are socially-concave, and using our general methodology we show the convergence of a speci c regret minimization dynamics.
AB - We study a general sub-class of concave games which we call socially concave games. We show that if each player follows any no-external regret minimization procedure then the dynamics will converge in the sense that both the average action vector will converge to a Nash equilibrium and that the utility of each player will converge to her utility in that Nash equilibrium. We show that many natural games are indeed socially con- cave games. Speci cally, we show that linear Cournot com- petition and linear resource allocation games are socially- concave games, and therefore our convergence result applies to them. In addition, we show that a simple best response dynamics might diverge for linear resource allocation games, and is known to diverge for linear Cournot competition. For the TCP congestion games we show that \near" the equilib- rium the games are socially-concave, and using our general methodology we show the convergence of a speci c regret minimization dynamics.
KW - Algorithmic game theory
KW - Learning equilibrium
KW - Regret minimization
UR - http://www.scopus.com/inward/record.url?scp=71049159689&partnerID=8YFLogxK
U2 - 10.1145/1536414.1536486
DO - 10.1145/1536414.1536486
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:71049159689
SN - 9781605585062
T3 - Proceedings of the Annual ACM Symposium on Theory of Computing
SP - 523
EP - 532
BT - STOC'09 - Proceedings of the 2009 ACM International Symposium on Theory of Computing
T2 - 41st Annual ACM Symposium on Theory of Computing, STOC '09
Y2 - 31 May 2009 through 2 June 2009
ER -