Multilinear Games

In many games, players' decisions consist of multiple sub-decisions, and hence can give rise to an exponential number of pure strategies. However, this set of pure strategies is often structured, allowing it to be represented compactly, as in network congestion games, security games, and extensive form games. Reduction to the standard normal form generally introduces exponential blow-up in the strategy space and therefore are inefficient for computation purposes. Although individual classes of such games have been studied, there currently exists no general purpose algorithms for computing solutions. equilibrium. To address this, we define multilinear games generalizing all. Informally, a game is multilinear if its utility functions are linear in each player's strategy, while fixing other players' strategies. Thus, if pure strategies, even if they are exponentially many, are vectors in polynomial dimension, then we show that mixed-strategies have an equivalent representation in terms of marginals forming a polytope in polynomial dimension. The canonical representation for multilinear games can still be exponential in the number of players, a typical obstacle in multi-player games. Therefore, it is necessary to assume additional structure that allows computation of certain sub-problems in polynomial time. Towards this, we identify two key subproblems: computation of utility gradients, and optimizing linear functions over strategy polytope. Given a multilinear game, with polynomial time subroutines for these two tasks, we show the following: a We can construct a polynomially computable and continuous fixed-point formulation, and show that its approximate fixed-points are approximate NE. This gives containment of approximate NE computation in PPAD, and settles its complexity to PPAD-complete. b Even though a coarse correlated equilibrium can potentially have exponential representation , through LP duality and a carefully designed separation oracle, we provide a polynomial-time algorithm to compute one with polynomial representation. c We show existence of an approximate NE with support-size logarithmic in the strategy polytope dimensions.

[1]  J. Nash NON-COOPERATIVE GAMES , 1951, Classics in Game Theory.

[2]  L. Khachiyan,et al.  The polynomial solvability of convex quadratic programming , 1980 .

[3]  Martin Grötschel,et al.  The ellipsoid method and its consequences in combinatorial optimization , 1981, Comb..

[4]  Katta G. Murty,et al.  Linear complementarity, linear and nonlinear programming , 1988 .

[5]  Christos H. Papadimitriou,et al.  On the Complexity of the Parity Argument and Other Inefficient Proofs of Existence , 1994, J. Comput. Syst. Sci..

[6]  Yurii Nesterov,et al.  Interior-point polynomial algorithms in convex programming , 1994, Siam studies in applied mathematics.

[7]  D. Koller,et al.  Efficient Computation of Equilibria for Extensive Two-Person Games , 1996 .

[8]  Michael L. Littman,et al.  Graphical Models for Game Theory , 2001, UAI.

[9]  Daphne Koller,et al.  Multi-Agent Influence Diagrams for Representing and Solving Games , 2001, IJCAI.

[10]  Robert Wilson,et al.  Structure theorems for game trees , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[11]  J. Geanakoplos Nash and Walras equilibrium via Brouwer , 2003 .

[12]  Martin Zinkevich,et al.  Online Convex Programming and Generalized Infinitesimal Gradient Ascent , 2003, ICML.

[13]  Christos H. Papadimitriou,et al.  The Game World Is Flat: The Complexity of Nash Equilibria in Succinct Games , 2006, ICALP.

[14]  Xi Chen,et al.  Computing Nash Equilibria: Approximation and Smoothed Complexity , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[15]  Kousha Etessami,et al.  On the Complexity of Nash Equilibria and Other Fixed Points (Extended Abstract) , 2010, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[16]  Anna Gál,et al.  Lower Bounds on Streaming Algorithms for Approximating the Length of the Longest Increasing Subsequence , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[17]  Computing correlated equilibria in multi-player games , 2008, JACM.

[18]  Mechanism design and analysis using simulation-based game models , 2008 .

[19]  Paul W. Goldberg,et al.  The Complexity of Computing a Nash Equilibrium , 2009, SIAM J. Comput..

[20]  N. Jennings,et al.  Generalised Fictitious Play for a Continuum of Anonymous Players , 2009, IJCAI.

[21]  Vincent Conitzer,et al.  Complexity of Computing Optimal Stackelberg Strategies in Security Resource Allocation Games , 2010, AAAI.

[22]  Vincent Conitzer,et al.  A double oracle algorithm for zero-sum security games on graphs , 2011, AAMAS.

[23]  Adam Tauman Kalai,et al.  Dueling algorithms , 2011, STOC '11.

[24]  Kevin Leyton-Brown,et al.  Action-Graph Games , 2011, Games Econ. Behav..

[25]  Kevin Leyton-Brown,et al.  Polynomial computation of exact correlated equilibrium in compact games , 2011, SECO.

[26]  Maurice Queyranne,et al.  Rational Generating Functions and Integer Programming Games , 2008, Oper. Res..

[27]  Kevin Leyton-Brown,et al.  Polynomial-time computation of exact correlated equilibrium in compact games , 2010, EC '11.

[28]  Milind Tambe,et al.  TRUSTS: Scheduling Randomized Patrols for Fare Inspection in Transit Systems , 2012, IAAI.

[29]  Paul R. Milgrom,et al.  Designing Random Allocation Mechanisms: Theory and Applications , 2013 .

[30]  Mohammad Taghi Hajiaghayi,et al.  From Duels to Battlefields: Computing Equilibria of Blotto and Other Games , 2016, AAAI.

[31]  Albert Xin Jiang,et al.  Congestion Games with Polytopal Strategy Spaces , 2016, IJCAI.

[32]  Juliane Hahn,et al.  Security And Game Theory Algorithms Deployed Systems Lessons Learned , 2016 .

[33]  Yakov Babichenko,et al.  Empirical Distribution of Equilibrium Play and Its Testing Application , 2013, Math. Oper. Res..

[34]  Alberto Del Pia,et al.  Totally Unimodular Congestion Games , 2015, SODA.