Anshelevich E., Dasgupta A., Kleinberg J., Tardos É., Wexler T. & Roughgarden T. 2004. The price of stability for network design with fair cost allocation. In Foundations of Computer Science (FOCS), 295–304. IEEE.

Ashlagi I., Monderer D. & Tennenholtz M. 2008. On the value of correlation. Journal of Artificial Intelligence Research 33, 575–613.

Aumann R. J. 1974. Subjectivity and correlation in randomized strategies. Journal of mathematical Economics 1(1), 67–96.

Aumann R. J. & Hart S. 2003. Long cheap talk. Econometrica 71(6), 1619–1660.

Barman S. & Ligett K. 2015. Finding any nontrivial coarse correlated equilibrium is hard. In ACM Conference on Economics and Computation (EC).

Blum A., Even-Dar E. & Ligett K. 2010. Routing without regret: on convergence to Nash equilibria of regret-minimizing algorithms in routing games. Theory of Computing 6(1), 179–199.

Blum A., Hajiaghayi M., Ligett K. & Roth A. 2008. Regret minimization and the price of total anarchy. In Proceedings of the Fortieth Annual ACM Symposium on Theory of Computing, 373–382. ACM.

Bowling M. 2005. Convergence and no-regret in multiagent learning. Advances in Neural Information Processing Systems 17, 209–216.

Bradonjic M., Ercal-Ozkaya G., Meyerson A. & Roytman A. 2009. On the price of mediation. In Proceedings of the 10th ACM Conference on Electronic Commerce, 315–324. ACM.

Brafman R. I. & Tennenholtz M. 2004. Efficient learning equilibrium. Artificial Intelligence 159(1), 27–47.

Conitzer V. & Sandholm T. 2007. Awesome: a general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents. Machine Learning 67(1–2), 23–43.

Daskalakis C., Goldberg P. W. & Papadimitriou C. H. 2009. The complexity of computing a Nash equilibrium. SIAM Journal on Computing 39(1), 195–259.

Foster D. P. & Vohra R. V. 1997. Calibrated learning and correlated equilibrium. Games and Economic Behavior 21(1), 40–55.

Friedman J. W. 1971. A non-cooperative equilibrium for supergames. The Review of Economic Studies 38(1), 1–12.

Greenwald A. & Jafari A. 2003. A general class of no-regret learning algorithms and game-theoretic equilibria. In Learning Theory and Kernel Machines, 2–12. Springer.

Hart S. & Mansour Y. 2007. The communication complexity of uncoupled Nash equilibrium procedures. In Proceedings of the Thirty-Ninth Annual ACM Symposium on Theory of Computing, 345–353. ACM.

Hoeffding W. 1963. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58(301), 13–30.

Kleinberg R., Piliouras G. & Tardos É. 2009. Multiplicative updates outperform generic no-regret learning in congestion games. In ACM Symposium on Theory of Computing (STOC).

Kleinberg R., Piliouras G. & Tardos É. 2011. Load balancing without regret in the bulletin board model. Distributed Computing 24(1), 21–29.

Koutsoupias E. & Papadimitriou C. H. 1999. Worst-case equilibria. In STACS, 404–413.

Littman M. L. & Stone P. 2005. A polynomial-time Nash equilibrium algorithm for repeated games. Decision Support Systems 39(1), 55–66.

Nash J. 1951. Non-cooperative games. Annals of Mathematics 54, 286–295.

Palaiopanos G., Panageas I. & Piliouras G. 2017. Multiplicative weights update with constant step-size in congestion games: convergence, limit cycles and chaos. CoRR, abs/1703.01138, http://arxiv.org/abs/1703.01138.

Roughgarden T. 2009. Intrinsic robustness of the price of anarchy. In Proceedings of STOC, 513–522.

Sandholm W. H. 2010. Population Games and Evolutionary Dynamics. MIT press.

Shoham Y., Powers R. & Grenager T. 2007. If multi-agent learning is the answer, what is the question? Artificial Intelligence 171(7), 365–377.

Young H. 2004. Strategic Learning and Its Limits. Arne Ryde memorial lectures, Oxford University Press. https://books.google.fr/books?id=3oUBoQEACAAJ.