Automatic fuzzy decision making system with learning for competing and connected businesses

We study uncertainties surrounding competition on business networks and board games. We investigate these uncertainties using concepts of fuzzy logic and game theory. We investigate how the payoff of the players is affected by a number of factors. These include the level of connectivity or number of links, the number of competitors, possible constraints on the networks and on the boards, as well as choice of strategy adopted by competitors. We introduce one fuzzy player in the game. This player uses fuzzy rules to make strategic decisions. We introduce learning to train and analyze how the fuzzy player adapts over time during the game.

► We study uncertainties in competitions on business networks and as board games using fuzzy logic concepts and game theory.
► Investigate how payoff is affected by factors including connectivity, no of players, strategy, constraints on network & board.
► We observed that on business networks, the higher the connectivity among players, the higher the payoff of the fuzzy player.
► Fuzzy rule base helps fuzzy player wins more often as the number of players (competitors) in the game increases on networks.
► Board results show that the higher the level of connectivity on the board, the higher the payoff of players and vice versa.