Rationality authority for provable rational behavior

Players in a game are assumed to be totally rational and absolutely smart. However, in reality all players may act in non-rational ways and may fail to understand and find their best actions. In particular, participants in social interactions, such as lotteries and auctions, cannot be expected to always find by themselves the "best-reply" to any situation. Indeed, agents may consult with others about the possible outcome of their actions. It is then up to the counselee to assure the rationality of the consultant's advice. We present a distributed computer system infrastructure, named rationality authority, that allows safe consultation among (possibly biased) parties. The parties' advices are adapted only after verifying their feasibility and optimality by standard formal proof checkers. The rationality authority design considers computational constraints, as well as privacy and security issues, such as verification methods that do not reveal private preferences. Some of the techniques resembles zero-knowledge proofs. A non-cooperative game is presented by the game inventor along with its (possibly intractable) equilibrium. The game inventor advises playing by this equilibrium and offers a checkable proof for the equilibrium feasibility and optimality. Standard verification procedures, provided by trusted (according to their reputation) verification procedures, are used to verify the proof. Thus, the proposed rationality authority infrastructure facilitates the applications of game theory in several important real-life scenarios by the use of computing systems.

[1]  Dean P. Foster,et al.  Regret in the On-Line Decision Problem , 1999 .

[2]  Frank Guerin An Algorithmic Approach to Specifying and Verifying Subgame Perfect Equilibria , 2006 .

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

[4]  Paul G. Spirakis,et al.  Strategies for repeated games with subsystem takeovers implementable by deterministic and self-stabilising automata , 2011, Int. J. Auton. Adapt. Commun. Syst..

[5]  Frank Guerin,et al.  Applying game theory mechanisms in open agent systems with complete information , 2007, Autonomous Agents and Multi-Agent Systems.

[6]  Silvio Micali,et al.  The knowledge complexity of interactive proof-systems , 1985, STOC '85.

[7]  Constantin Enea,et al.  Abstractions of Multi-agent Systems , 2007, CEEMAS.

[8]  R. Aumann Subjectivity and Correlation in Randomized Strategies , 1974 .

[9]  Paul G. Spirakis,et al.  Atomic congestion games among coalitions , 2008, TALG.

[10]  Paul G. Spirakis,et al.  Game authority for robust and scalable distributed selfish-computer systems , 2010, Theor. Comput. Sci..

[11]  Emmanuel M. Tadjouddine Complexity of Verifying Game Equilibria , 2007, CEEMAS.

[12]  Paul G. Spirakis,et al.  Strategies for repeated games with subsystem takeovers: implementable by deterministic and self-stabilizing automata (extended abstract) , 2008, Autonomics 2008.

[13]  Yoav Freund,et al.  Game theory, on-line prediction and boosting , 1996, COLT '96.

[14]  Paul G. Spirakis,et al.  Robust and scalable middleware for selfish-computer systems , 2011, Comput. Sci. Rev..

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

[16]  Frank Guerin,et al.  Realising Common Knowledge Assumptions in Agent Auctions , 2006, 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[17]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[18]  Yves Bertot,et al.  Interactive Theorem Proving and Program Development: Coq'Art The Calculus of Inductive Constructions , 2010 .

[19]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[20]  Frank Guerin,et al.  Verification and Compliance Testing , 2003, Communication in Multiagent Systems.

[21]  Vahab S. Mirrokni,et al.  Quasi-Proportional Mechanisms: Prior-Free Revenue Maximization , 2009, LATIN.

[22]  Marc Pauly,et al.  Programming and Verifying Subgame-Perfect Mechanisms , 2002, J. Log. Comput..

[23]  R. Thaler,et al.  Nudge: Improving Decisions About Health, Wealth, and Happiness , 2008 .

[24]  Frank Guerin,et al.  Verifying Dominant Strategy Equilibria in Auctions , 2007, CEEMAS.