The weighted majority algorithm
Information and Computation
Journal of the ACM (JACM)
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Journal of the ACM (JACM)
Selfish traffic allocation for server farms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Tight bounds for worst-case equilibria
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Path Kernels and Multiplicative Updates
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Proceedings of the twenty-second annual symposium on Principles of distributed computing
Designing Networks for Selfish Users is Hard
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Adaptive routing with end-to-end feedback: distributed learning and geometric approaches
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Bounds for the convergence rate of randomized local search in a multiplayer load-balancing game
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
Fast convergence of selfish rerouting
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Adaptive routing with stale information
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
Distributed selfish load balancing
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Fast convergence to Wardrop equilibria by adaptive sampling methods
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Convergence time to Nash equilibria
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Perspectives on multiagent learning
Artificial Intelligence
Convergence to approximate Nash equilibria in congestion games
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Regret based dynamics: convergence in weakly acyclic games
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Inapproximability of pure nash equilibria
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
No-regret learning and a mechanism for distributed multiagent planning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Atomic Congestion Games: Fast, Myopic and Concurrent
SAGT '08 Proceedings of the 1st International Symposium on Algorithmic Game Theory
The Price of Stochastic Anarchy
SAGT '08 Proceedings of the 1st International Symposium on Algorithmic Game Theory
The Price of Malice in Linear Congestion Games
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Intrinsic robustness of the price of anarchy
Proceedings of the forty-first annual ACM symposium on Theory of computing
On the convergence of regret minimization dynamics in concave games
Proceedings of the forty-first annual ACM symposium on Theory of computing
Multiplicative updates outperform generic no-regret learning in congestion games: extended abstract
Proceedings of the forty-first annual ACM symposium on Theory of computing
Multiagent learning in large anonymous games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Management of Variable Data Streams in Networks
Algorithmics of Large and Complex Networks
Adaptive routing with stale information
Theoretical Computer Science
Load balancing without regret in the bulletin board model
Proceedings of the 28th ACM symposium on Principles of distributed computing
Concurrent imitation dynamics in congestion games
Proceedings of the 28th ACM symposium on Principles of distributed computing
On Fixed Convex Combinations of No-Regret Learners
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Bayesian Auctions with Friends and Foes
SAGT '09 Proceedings of the 2nd International Symposium on Algorithmic Game Theory
From optimization to regret minimization and back again
SysML'08 Proceedings of the Third conference on Tackling computer systems problems with machine learning techniques
The limits of smoothness: a primal-dual framework for price of anarchy bounds
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Multiagent learning in large anonymous games
Journal of Artificial Intelligence Research
Fast Convergence to Wardrop Equilibria by Adaptive Sampling Methods
SIAM Journal on Computing
Taming traffic dynamics: Analysis and improvements
Computer Communications
Minimum delay load-balancing via nonparametric regression and no-regret algorithms
Computer Networks: The International Journal of Computer and Telecommunications Networking
The price of anarchy in games of incomplete information
Proceedings of the 13th ACM Conference on Electronic Commerce
Approximating wardrop equilibria with finitely many agents
DISC'07 Proceedings of the 21st international conference on Distributed Computing
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There has been substantial work developing simple, efficient no-regret algorithms for a wide class of repeated decision-making problems including online routing. These are adaptive strategies an individual can use that give strong guarantees on performance even in adversarially-changing environments. There has also been substantial work on analyzing properties of Nash equilibria in routing games. In this paper, we consider the question: if each player in a routing game uses a no-regret strategy, will behavior converge to a Nash equilibrium? In general games the answer to this question is known to be no in a strong sense, but routing games have substantially more structure.In this paper we show that in the Wardrop setting of multicommodity flow and infinitesimal agents, behavior will approach Nash equilibrium (formally, on most days, the cost of the flow will be close to the cost of the cheapest paths possible given that flow) at a rate that depends polynomially on the players' regret bounds and the maximum slope of any latency function. We also show that price-of-anarchy results may be applied to these approximate equilibria, and also consider the finite-size (non-infinitesimal) load-balancing model of Azar [2].