4.6 Article

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

期刊

ELECTRONICS
卷 11, 期 2, 页码 -

出版社

MDPI
DOI: 10.3390/electronics11020244

关键词

information mining; decision rules; rough set; game theory

资金

  1. National Foundation for Philosophy and Social Sciences of China [19BG234, 21BGL243]
  2. Shanghai Foundation for Philosophy and Social Sciences [2020BGL005]

向作者/读者索取更多资源

This study proposes a rough set-game theory model (RS-GT) to predict opponent behavior and help enterprises obtain greater profits.
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.

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