Rough Set-Game Theory Information Mining Model Considering Opponents’ Information

In multi-strategy games, the increase in the number of strategies makes it difficult to make a solution. To maintain the competition advantage and obtain maximal profits, one side of the game hopes to predict the opponent’s behavior. Building a model to predict an opponent’s behavior is helpful. In this paper, we propose a rough set-game theory model (RS-GT) considering uncertain information and the opponent’s decision rules. The uncertainty of strategies is obtained based on the rough set method, and an accurate solution is obtained based on game theory from the rough set-game theory model. The players obtain their competitors’ decision rules to predict the opponents’ behavior by mining the information from repeated games in the past. The players determine their strategy to obtain maximum profits by predicting the opponent’s actions, i.e., adopting a first-mover or second-mover strategy to build a favorable situation. The result suggests that the rough set-game theory model helps enterprises avoid unnecessary losses and allows them to obtain greater profits.

The Folk Theorem for Repeated Games with Time-Dependent Discounting

This paper defines a general framework to study infinitely repeated games with time-dependent discounting in which we distinguish and discuss both time-consistent and -inconsistent preferences. To study the long-term properties of repeated games, we introduce an asymptotic condition to characterize the fact that players become more and more patient; that is, the discount factors at all stages uniformly converge to one. Two types of folk theorems are proven without the public randomization assumption: the asymptotic one, that is, the equilibrium payoff set converges to the feasible and individual rational set as players become patient, and the uniform one, that is, any payoff in the feasible and individual rational set is sustained by a single strategy profile that is an approximate subgame perfect Nash equilibrium in all games with sufficiently patient discount factors. We use two methods for the study of asymptotic folk theorem: the self-generating approach and the constructive proof. We present the constructive proof in the perfect-monitoring case and show that it can be extended to time-inconsistent preferences. The self-generating approach applies to the public-monitoring case but may not extend to time-inconsistent preferences because of a nonmonotonicity result.