基于信息熵风险和收益的纳什均衡选择

Nash equilibrium is the key conception of game theory and it’s the best decision-making result of rational players. But the multiple Nash equilibriums still makes the players into uncertainty and it constrains the application of game theory. In this thesis, we give a Nash equilibriums selection method based on information entropy risk and rewards. This method takes both risk and rewards into account according to the player’s risk characteristic and gives a selection. We give a 2×2 game example with two strict Nash equilibriums to justify this method and the result proves the given method is reasonable.