Cooperation Commons: Interdisciplinary study of cooperation and collective action.
The Evolution of Strategies in the Iterated Prisoner's Dilemma
Summary of: The Evolution of Strategies in the Iterated Prisoner's Dilemma
The genetic algorithm uses computer simulations to evolve different strategies for playing Prisoner's Dilemma games, and by observing the interactions of populations of agents over many runs, it is possible to make useful observations that could generalize to human behavior – such as the tendency of reciprocation to establish itself and spread if cooperating agents are able to encounter one another.
John Holland at University of Michigan developed a means of testing computer problem-solving methods by applying a method based on Darwinian evolution: agents (program) have a phenotype (the strategy the program uses for problem solving) and a genotype (the way strategies are represented in their programming code). Means of reproduction and mutation are specified. Agents interact with each other in a rigorously specified simulation, and the effectiveness of each agent is evaluated in a particular environment in relation to its interactions with other agents; successful strategies are reproduced at a higher rate than less successful strategies; pairs of successful offspring strategies are mated by combining genetic material; mutation is introduced. Simulations can be halted after specified numbers of runs and analyzed, then restarted. In about a quarter of simulation runs with sexual reproduction, better strategies than Tit-for-Tat evolved, and after a random start, populations tend to first evolve away from cooperation as less cooperative rules succeed more often, but can evolve back toward stable cooperation states if cooperative strategies encounter one another and reciprocate.
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