Iterated Prisoner's Dilemma with Choice and Refusal of Partners: Evolutionary Results
Proceedings of the Third European Conference on Advances in Artificial Life
How to Explore your Opponent's Strategy (almost) Optimally
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Crossover and Evolutionary Stability in the Prisoner's Dilemma
Evolutionary Computation
Evolving behaviors in the iterated prisoner's dilemma
Evolutionary Computation
The Iterated Prisoners' Dilemma: 20 Years on
The Iterated Prisoners' Dilemma: 20 Years on
IEEE Transactions on Evolutionary Computation
Finite iterated prisoner's dilemma revisited: belief change and end-game effect
Proceedings of the Behavioral and Quantitative Game Theory: Conference on Future Directions
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In recent iterated prisoner's dilemma tournaments, the most successful strategies were those that had identification mechanisms. By playing a predetermined sequence of moves and learning from their opponents' responses, these strategies managed to identify their opponents. We believe that these identification mechanisms may be very useful in evolutionary games. In this paper one such strategy, which we call collective strategy, is analyzed. Collective strategies apply a simple but efficient identification mechanism (that just distinguishes themselves from other strategies), and this mechanism allows them to only cooperate with their group members and defect against any others. In this way, collective strategies are able to maintain a stable population in evolutionary iterated prisoner's dilemma. By means of an invasion barrier, this strategy is compared with other strategies in evolutionary dynamics in order to demonstrate its evolutionary features. We also find that this collective behavior assists the evolution of cooperation in specific evolutionary environments.