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On the convergence of regret minimization dynamics in concave games

Published: 31 May 2009 Publication History

Abstract

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 concave games. Specifically, we show that linear Cournot competition 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 equilibrium the games are socially-concave, and using our general methodology we show the convergence of a specific regret minimization dynamics.

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    cover image ACM Conferences
    STOC '09: Proceedings of the forty-first annual ACM symposium on Theory of computing
    May 2009
    750 pages
    ISBN:9781605585062
    DOI:10.1145/1536414
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    Published: 31 May 2009

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    Author Tags

    1. algorithmic game theory
    2. learning equilibrium
    3. regret minimization

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    STOC '09: Symposium on Theory of Computing
    May 31 - June 2, 2009
    MD, Bethesda, USA

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    Cited By

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    • (2023)Fast rates in time-varying strongly monotone gamesProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3620040(39139-39164)Online publication date: 23-Jul-2023
    • (2023)Doubly optimal no-regret learning in monotone gamesProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618551(3507-3524)Online publication date: 23-Jul-2023
    • (2023)Achieving Flexible and Lightweight Multipath Congestion Control Through Online LearningIEEE Transactions on Network and Service Management10.1109/TNSM.2022.320874720:1(46-59)Online publication date: Mar-2023
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    • (2023)End-to-End Congestion Control as Learning for Unknown Games with Bandit Feedback2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS57875.2023.00060(327-338)Online publication date: Jul-2023
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    • (2023)A unified stochastic approximation framework for learning in gamesMathematical Programming10.1007/s10107-023-02001-y203:1-2(559-609)Online publication date: 4-Aug-2023
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