This paper introduces a new game theoretic equilibrium which is based upon the Bayesian subjective view of probability, BEIC （Bayesian equilibrium iterative conjectures）. It requires players to make predictions, sta...This paper introduces a new game theoretic equilibrium which is based upon the Bayesian subjective view of probability, BEIC （Bayesian equilibrium iterative conjectures）. It requires players to make predictions, starting from first order uninformative predictive distribution functions （or conjectures） and keep updating with statistical decision theoretic and game theoretic reasoning until a convergence of conjectures is achieved. Information known by the players such as the reaction functions are thereby incorporated into their higher order conjectures and help to determine the convergent conjectures and the equilibrium. In a BEIC, conjectures are consistent with the equilibrium or equilibriums they supported and so rationality is achieved for actions, strategies and conjectures. The BEIC approach is capable of analyzing a larger set of games than current Nash Equilibrium based games theory, including games with inaccurate observations, games with unstable equilibrium and games with double or multiple sided incomplete information games. On the other hand, for the set of games analyzed by the current games theory, it generates far lesser equilibriums and normally generates only a unique equilibrium. It treats games with complete and perfect information as special cases of games with incomplete information and noisy observation whereby the variance of the prior distribution function on type and the variance of the observation noise term tend to zero. Consequently, there is the issue of indeterminacy in statistical inference and decision making in these games as the equilibrium solution depends on which variances tends to zero first. It therefore identifies equilibriums in these games that have so far eluded the classical theory of games. Finally, it also resolves inconsistencies in equilibrium results by different solution concepts in current games theory such as that between Nash Equilibrium and iterative elimination of dominated strategies and that between Perfect Bayesian Equilibrium and backward induction （Subgame Perfect Equilibrium）.展开更多
BEIC （Bayesian equilibrium by iterative conjectures） analyzes games with players forming their conjectures about what other players will do through iterative reasoning starting with first order uninformative conject...BEIC （Bayesian equilibrium by iterative conjectures） analyzes games with players forming their conjectures about what other players will do through iterative reasoning starting with first order uninformative conjectures and keep updating their conjectures iteratively with game theoretic reasoning until a convergence of conjectures is achieved. In a BEIC, beliefs about the other players＇ strategies are specified and they are consistent with the equilibrium strategies they supported. A BEIC is therefore a perfect Bayesian equilibrium and hence a refinement of Nash equilibrium. Through six examples, the BE1C solutions are compared with those obtained by the other refining criteria of payoff-dominance, risk-dominance, iterated admissibility, subgame perfect equilibrium, Bayesian Nash equilibrium, perfect Bayesian equilibrium and the intuitive criterion. The outstanding results from the comparisons are that the BEIC approach is able to pick the natural focal point of a game when the iterated admissibility criterion fails to, the BEIC approach rules out equilibrium depending upon non credible threat, and that in simultaneous and sequential games of incomplete information, the BEIC approach not only normally narrows down the equilibriums to one but it also picks the most compelling equilibrium compare with Bayesian Nash equilibrium or perfect Bayesian equilibrium or intuitive criterion.展开更多
文摘This paper introduces a new game theoretic equilibrium which is based upon the Bayesian subjective view of probability, BEIC （Bayesian equilibrium iterative conjectures）. It requires players to make predictions, starting from first order uninformative predictive distribution functions （or conjectures） and keep updating with statistical decision theoretic and game theoretic reasoning until a convergence of conjectures is achieved. Information known by the players such as the reaction functions are thereby incorporated into their higher order conjectures and help to determine the convergent conjectures and the equilibrium. In a BEIC, conjectures are consistent with the equilibrium or equilibriums they supported and so rationality is achieved for actions, strategies and conjectures. The BEIC approach is capable of analyzing a larger set of games than current Nash Equilibrium based games theory, including games with inaccurate observations, games with unstable equilibrium and games with double or multiple sided incomplete information games. On the other hand, for the set of games analyzed by the current games theory, it generates far lesser equilibriums and normally generates only a unique equilibrium. It treats games with complete and perfect information as special cases of games with incomplete information and noisy observation whereby the variance of the prior distribution function on type and the variance of the observation noise term tend to zero. Consequently, there is the issue of indeterminacy in statistical inference and decision making in these games as the equilibrium solution depends on which variances tends to zero first. It therefore identifies equilibriums in these games that have so far eluded the classical theory of games. Finally, it also resolves inconsistencies in equilibrium results by different solution concepts in current games theory such as that between Nash Equilibrium and iterative elimination of dominated strategies and that between Perfect Bayesian Equilibrium and backward induction （Subgame Perfect Equilibrium）.
文摘BEIC （Bayesian equilibrium by iterative conjectures） analyzes games with players forming their conjectures about what other players will do through iterative reasoning starting with first order uninformative conjectures and keep updating their conjectures iteratively with game theoretic reasoning until a convergence of conjectures is achieved. In a BEIC, beliefs about the other players＇ strategies are specified and they are consistent with the equilibrium strategies they supported. A BEIC is therefore a perfect Bayesian equilibrium and hence a refinement of Nash equilibrium. Through six examples, the BE1C solutions are compared with those obtained by the other refining criteria of payoff-dominance, risk-dominance, iterated admissibility, subgame perfect equilibrium, Bayesian Nash equilibrium, perfect Bayesian equilibrium and the intuitive criterion. The outstanding results from the comparisons are that the BEIC approach is able to pick the natural focal point of a game when the iterated admissibility criterion fails to, the BEIC approach rules out equilibrium depending upon non credible threat, and that in simultaneous and sequential games of incomplete information, the BEIC approach not only normally narrows down the equilibriums to one but it also picks the most compelling equilibrium compare with Bayesian Nash equilibrium or perfect Bayesian equilibrium or intuitive criterion.
