Fuzzy Rule-Based Strategy for a Market Selection Game

In this chapter, we describe how fuzzy rule-based systems can be applied to a market selection game with many players (e.g., 100 players) and several markets (e.g., five markets). Our market selection game is a non-cooperative repeated game where every player is supposed to simultaneously choose a single market for maximizing its own payoff obtained by selling its product at the selected market. It is assumed that the market price of the product is determined by a linear function of the total supply at each market. For example, if many players choose a particular market to sell their products, the market price at that market is low. On the other hand, the market price is high if only a small number of players choose that market. In this manner, the market price at each market is determined by the actions of all players. In our market selection game, the point is to choose a market with a high market price, i.e., a market that is not chosen by many other players. In this chapter, we explain how fuzzy rule-based systems can be automatically trained for choosing an appropriate market through the iterative execution of our market selection game. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.