Adaptive learning of hypergame situations using a genetic algorithm

In this paper, we propose and examine adaptive learning procedures for supp orting a group of decision makers with a common set of strategies and prefe rences who face uncertain behaviors of "nature." First, we describe the dec ision situation as a hypergame situation, where each decision maker is expl icitly assumed to have misperceptions about the nature's set of strategies and preferences. Then. we propose three learning procedures about the natur e, each of,which consists of several activities. One of the activities is t o choose "rational"' actions based on current perceptions and rationality a dopted by the decision makers, while the other activities are represented b y the elements of a genetic algorithm (GA) to improve current perceptions. The three learning procedures are different from each other with respect to at least one of such activities as fitness evaluation, modified crossover, and action choice, though they use the same definition for the other GA el ements. Finally; we point out that examining the simulation results how to employ preference- and strategy-oriented information is critical to obtaini ng good performance in clarifying the nature's set of strategies and the ou tcomes most preferred by the nature.