Multiplicative updates outperform generic no-regret learning in congestion games

Proceedings of the Annual ACM Symposium on Theory of Computing

Volumn , Issue , 2009, Pages 533-542

  Kleinberg, Robert ^a   Piliouras, Georgios ^a   Tardos, Éva ^a  

^a Cornell University ^*  (United States)

Author keywords

Game theory; Learning theory; Price of anarchy

Indexed keywords

A-STABILITY; ALGEBRAIC GEOMETRY; CONGESTION COSTS; CONGESTION GAMES; CONSTANT PRICE; CONVERGENCE SPEED; CORRELATED EQUILIBRIA; LEARNING BEHAVIOR; LEARNING RATES; LEARNING THEORY; LOAD-BALANCING; LONG TERM; MULTIPLICATIVE UPDATES; NASH EQUILIBRIA; NASH EQUILIBRIUM; PRICE OF ANARCHY; PURE NASH EQUILIBRIUM; SOCIAL COST; STABLE EQUILIBRIUM; TRADE OFF;

ATOMS; COMPUTATION THEORY; CONVERGENCE OF NUMERICAL METHODS; COSTS; DYNAMICAL SYSTEMS; EDUCATION; GAME THEORY; LEARNING SYSTEMS; PROBABILITY DISTRIBUTIONS; SET THEORY; STABILITY CRITERIA; TELECOMMUNICATION NETWORKS; TRAFFIC CONGESTION;

LEARNING ALGORITHMS;

EID: 71049137803     PISSN: 07378017     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1536414.1536487     Document Type: Conference Paper

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