Algorithms for Closed Under Rational Behavior (CURB) Sets

We provide a series of algorithms demonstrating that solutions according to the fundamental game-theoretic solution concept of closed under rational behavior (CURB) sets in two-player, normal-form games can be computed in polynomial time (we also discuss extensions to n-player games). First, we describe an algorithm that identifies all of a player's best responses conditioned on the belief that the other player will play from within a given subset of its strategy space. This algorithm serves as a subroutine in a series of polynomial-time algorithms for finding all minimal CURB sets, one minimal CURB set, and the smallest minimal CURB set in a game. We then show that the complexity of finding a Nash equilibrium can be exponential only in the size of a game's smallest CURB set. Related to this, we show that the smallest CURB set can be an arbitrarily small portion of the game, but it can also be arbitrarily larger than the supports of its only enclosed Nash equilibrium. We test our algorithms empirically and find that most commonly studied academic games tend to have either very large or very small minimal CURB sets.

[1]  Bernhard von Stengel,et al.  Exponentially many steps for finding a Nash equilibrium in a bimatrix game , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.

[2]  H. Kuk On equilibrium points in bimatrix games , 1996 .

[3]  Sjaak Hurkens Learning by Forgetful Players , 1995 .

[4]  Vitaly Pruzhansky,et al.  On finding curb sets in extensive games , 2003, Int. J. Game Theory.

[5]  Daniel M. Kane,et al.  On the complexity of two-player win-lose games , 2005, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).

[6]  David Pearce Rationalizable Strategic Behavior and the Problem of Perfection , 1984 .

[7]  Yinyu Ye,et al.  Improved complexity results on solving real-number linear feasibility problems , 2006, Math. Program..

[8]  Felix A. Fischer,et al.  Computational aspects of Shapley's saddles , 2009, AAMAS.

[9]  Vincent Conitzer,et al.  A Generalized Strategy Eliminability Criterion and Computational Methods for Applying It , 2005, AAAI.

[10]  Eitan Zemel,et al.  The Complexity of Eliminating Dominated Strategies , 1993, Math. Oper. Res..

[11]  D. Knuth,et al.  A note on strategy elimination in bimatrix games , 1988 .

[12]  Vincent Conitzer,et al.  A technique for reducing normal-form games to compute a Nash equilibrium , 2006, AAMAS '06.

[13]  Henk Norde,et al.  An axiomatization of minimal curb sets , 2005, Int. J. Game Theory.

[14]  Michael P. Wellman,et al.  Algorithms for Finding Approximate Formations in Games , 2010, AAAI.

[15]  J. Weibull,et al.  Strategy subsets closed under rational behavior , 1991 .

[16]  Andrew McLennan,et al.  Gambit: Software Tools for Game Theory , 2006 .

[17]  Xiaotie Deng,et al.  Settling the Complexity of Two-Player Nash Equilibrium , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[18]  B. Bernheim Rationalizable Strategic Behavior , 1984 .

[19]  Yoav Shoham,et al.  Run the GAMUT: a comprehensive approach to evaluating game-theoretic algorithms , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[20]  Yoav Shoham,et al.  Simple search methods for finding a Nash equilibrium , 2004, Games Econ. Behav..

[21]  Eitan Zemel,et al.  Nash and correlated equilibria: Some complexity considerations , 1989 .

[22]  T. Sandholm,et al.  Finding All Minimal sCURB Sets in Finite Games , 2010 .

[23]  Vincent Conitzer,et al.  Complexity of (iterated) dominance , 2005, EC '05.

[24]  Pierpaolo Battigalli,et al.  Rationalizable bidding in first-price auctions , 2003, Games Econ. Behav..

[25]  Vincent Conitzer,et al.  Mixed-Integer Programming Methods for Finding Nash Equilibria , 2005, AAAI.

[26]  Paul W. Goldberg,et al.  The complexity of computing a Nash equilibrium , 2006, STOC '06.