Equilibria in Ordinal Games: A Framework based on Possibility Theory

The present paper proposes the first definition of mixed equilibrium for ordinal games. This definition naturally extends possibilistic (single agent) decision theory. This allows us to provide a unifying view of single and multi-agent qualitative decision theory. Our first contribution is to show that ordinal games always admit a possibilistic mixed equilibrium, which can be seen as a qualitative counterpart to mixed (probabilistic) equilibrium.Then, we show that a possibilistic mixed equilibrium can be computed in polynomial time (wrt the size of the game), which contrasts with pure Nash or mixed probabilistic equilibrium computation in cardinal game theory.The definition we propose is thus operational in two ways: (i) it tackles the case when no pure Nash equilibrium exists in an ordinal game; and (ii) it allows an efficient computation of a mixed equilibrium.

[1]  Didier Dubois,et al.  Possibility Theory as a Basis for Qualitative Decision Theory , 1995, IJCAI.

[2]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[3]  S. Sorin A First Course on Zero Sum Repeated Games , 2002 .

[4]  O. Bagasra,et al.  Proceedings of the National Academy of Sciences , 1914, Science.

[5]  Roger B. Myerson,et al.  Game theory - Analysis of Conflict , 1991 .

[6]  Leslie Pack Kaelbling,et al.  Acting Optimally in Partially Observable Stochastic Domains , 1994, AAAI.

[7]  U. Rieder,et al.  Markov Decision Processes , 2010 .

[8]  J. Nash Equilibrium Points in N-Person Games. , 1950, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Didier Dubois,et al.  Decision-theoretic foundations of qualitative possibility theory , 2001, Eur. J. Oper. Res..

[10]  Ajith Abraham,et al.  Engineering applications of artificial intelligence: A bibliometric analysis of 30 years (1988-2018) , 2019, Eng. Appl. Artif. Intell..

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

[12]  L. Shapley,et al.  Potential Games , 1994 .

[13]  Chunhui Xu Computation of noncooperative equilibria in ordinal games , 2000, Eur. J. Oper. Res..

[14]  Tim Roughgarden,et al.  Algorithmic Game Theory , 2007 .

[15]  J. Neumann,et al.  Theory of Games and Economic Behavior. , 1945 .

[16]  Régis Sabbadin,et al.  Possibilistic Markov decision processes , 2001 .

[17]  Didier Dubois,et al.  Possibility theory , 2018, Scholarpedia.

[18]  Martine De Cock,et al.  Multilateral Negotiation in Boolean Games with Incomplete Information Using Generalized Possibilistic Logic , 2015, IJCAI.

[19]  中山 幹夫,et al.  Games and Economic Behavior of Bounded Rationality , 2016 .

[20]  Verzekeren Naar Sparen,et al.  Cambridge , 1969, Humphrey Burton: In My Own Time.

[21]  Jose B. Cruz,et al.  Ordinal Games and Generalized Nash and Stackelberg Solutions , 2000 .

[22]  Jérôme Lang,et al.  Towards qualitative approaches to multi-stage decision making , 1998, Int. J. Approx. Reason..

[23]  John C. Harsanyi,et al.  Games with Incomplete Information Played by "Bayesian" Players, I-III: Part I. The Basic Model& , 2004, Manag. Sci..

[24]  G. Saridis,et al.  Journal of Optimization Theory and Applications Approximate Solutions to the Time-invariant Hamilton-jacobi-bellman Equation 1 , 1998 .

[25]  J. Filar,et al.  Competitive Markov Decision Processes , 1996 .

[26]  Hans Haller,et al.  Ordinal Games , 2007, IGTR.

[27]  A. Copeland Review: John von Neumann and Oskar Morgenstern, Theory of games and economic behavior , 1945 .

[28]  Mohammad Rajabi,et al.  An Ordinal Game Theory Approach to the Analysis and Selection of Partners in Public–Private Partnership Projects , 2016, J. Optim. Theory Appl..

[29]  Ariel Rubinstein,et al.  A Course in Game Theory , 1995 .

[30]  M. Shubik,et al.  Classification of two-person ordinal bimatrix games , 1992 .

[31]  Martine De Cock,et al.  Possibilistic Boolean Games: Strategic Reasoning under Incomplete Information , 2014, JELIA.