Breeding Hybrid Strategies: Optimal Behaviour for Oligopolists
AbstractOligopolistic pricing decisions--in which the choice variable is not dichotomous, as in the simple prisoner's dilemma, but continuous--have been modeled as a generalized prisoner's dilemma (GPD) by Fader and Hauser, who sought, in the two MIT Computer Strategy Tournaments, to obtain an effective generalization of Rapoport's Tit for Tat for the three-person repeated game. Holland's genetic algorithm and Axelrod's representation of contingent strategies provide a means of generating new strategies in the computer, through machine learning, without outside submissions. This paper discusses how findings from two-person tournaments can be extended to the GPD, in particular how the author's winning strategy in the Second MIT Competitive Strategy Tournament could be bettered. The paper provides insight into how oligopolistic pricing competitors can successfully compete, and underlines the importance of "niche" strategies, successful against a particular environment of competitors.
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