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On minmax theorems for multiplayer games

Published: 23 January 2011 Publication History

Abstract

We prove a generalization of von Neumann's minmax theorem to the class of separable multiplayer zero-sum games, introduced in [Bregman and Fokin 1998]. These games are polymatrix---that is, graphical games in which every edge is a two-player game between its endpoints---in which every outcome has zero total sum of players' payoffs. Our generalization of the minmax theorem implies convexity of equilibria, polynomial-time tractability, and convergence of no-regret learning algorithms to Nash equilibria. Given that Nash equilibria in 3-player zero-sum games are already PPAD-complete, this class of games, i.e. with pairwise separable utility functions, defines essentially the broadest class of multi-player constant-sum games to which we can hope to push tractability results. Our result is obtained by establishing a certain game-class collapse, showing that separable constant-sum games are payoff equivalent to pairwise constant-sum polymatrix games---polymatrix games in which all edges are constant-sum games, and invoking a recent result of [Daskalakis, Papadimitriou 2009] for these games.
We also explore generalizations to classes of non-constant-sum multi-player games. A natural candidate is polymatrix games with strictly competitive games on their edges. In the two player setting, such games are minmax solvable and recent work has shown that they are merely affine transformations of zero-sum games [Adler, Daskalakis, Papadimitriou 2009]. Surprisingly we show that a polymatrix game comprising of strictly competitive games on its edges is PPAD-complete to solve, proving a striking difference in the complexity of networks of zero-sum and strictly competitive games. Finally, we look at the role of coordination in networked interactions, studying the complexity of polymatrix games with a mixture of coordination and zero-sum games. We show that finding a pure Nash equilibrium in coordination-only polymatrix games is PLS-complete; hence, computing a mixed Nash equilibrium is in PLS ∩ PPAD, but it remains open whether the problem is in P. If, on the other hand, coordination and zero-sum games are combined, we show that the problem becomes PPAD-complete, establishing that coordination and zero-sum games achieve the full generality of PPAD.

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  • (2023)The best of both worlds in network population gamesProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669496(77149-77173)Online publication date: 10-Dec-2023
  • (2023)Computing optimal nash equilibria in multiplayer gamesProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667050(21230-21254)Online publication date: 10-Dec-2023
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cover image ACM Conferences
SODA '11: Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete algorithms
January 2011
1785 pages

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Society for Industrial and Applied Mathematics

United States

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Published: 23 January 2011

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SODA '11
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SODA '11: 22nd ACM-SIAM Symposium on Discrete Algorithms
January 23 - 25, 2011
California, San Francisco

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Overall Acceptance Rate 411 of 1,322 submissions, 31%

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Cited By

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  • (2024)Adaptively perturbed mirror descent for learning in gamesProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692072(31-80)Online publication date: 21-Jul-2024
  • (2023)The best of both worlds in network population gamesProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669496(77149-77173)Online publication date: 10-Dec-2023
  • (2023)Computing optimal nash equilibria in multiplayer gamesProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667050(21230-21254)Online publication date: 10-Dec-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)Complexity of efficient outcomes in binary-action polymatrix games and implications for coordination problemsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/294(2642-2650)Online publication date: 19-Aug-2023
  • (2022)Multiagent Q-learning with sub-team coordinationProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602404(29427-29439)Online publication date: 28-Nov-2022
  • (2022)Optimistic mirror descent either converges to nash or to strong coarse correlated equilibria in bimatrix gamesProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601466(16439-16454)Online publication date: 28-Nov-2022
  • (2020)Converging to team-maxmin equilibria in zero-sum multiplayer gamesProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525961(11033-11043)Online publication date: 13-Jul-2020
  • (2020)From chaos to orderProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525604(7186-7196)Online publication date: 13-Jul-2020
  • (2020)Tight last-iterate convergence rates for no-regret learning in multi-player gamesProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3497468(20766-20778)Online publication date: 6-Dec-2020
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