The Evolution of Strategies in the Iterated Prisoner's Dilemma

By Robert Axelrod. 2001

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Citations in the corpus

Empirically Evaluating Multiagent Learning Algorithms (2014, background): “We also mention as an aside that MAL experiments have been conducted in settings that are neither generalizations nor restrictions of our setting, such as the population-based work by Axelrod (1987) and Airiau et al. (2007).”
Behaviour in commons dilemmas: Homo economicus and Homo psychologicus in an ecological-economic model (2000, methodology): “…of other automata (Lindgren and Nordahl, 1995; Molander, 1985; Nowak and Sigmund, 1992), or by equipping the automata with evolutionary strategies (genetic algorithms), which allowed them to adapt their choice behaviour to the behaviour of the other agents (e.g., Axelrod, 1987; Macy, 1996).”
How to Solve It: Modern Heuristics (2000, methodology): “In 1987, Axelrod [15] conducted a simulation in which strategies in the IPD were represented as look-up tables based on the three previous moves made in the game using the payoff matrix shown in table 14.”
Strategy Generation and Evaluation for Meta-Game Playing (2011, background): “Most of these approaches can be considered as self-play [Samuels, 1959; Angeline and Pollack, 1993; Epstein, 1992; Tesauro, 1993; Axelrod, 1987], in which either a single player or a population of players evolves during competition on large numbers of contests.”
Evolutionary Tournament-Based Comparison of Learning and Non-Learning Algorithms for Iterated Games (2007, methodology): “Our work is similar to Axelrod's work with a genetic algorithm in the context of the prisoners' dilemma tournament (Axelrod 1987).”
The road to everywhere : Evolution, complexity and progress in natural and artificial systems (2008, methodology): “The application of coevolutionary methods to optimisation problems was bolstered by initial successes such as those reported by Axelrod [8] (on the coevolution of strategies for the Iterated Prisoner’s Dilemma), and Hillis [80] (on the coevolutionary optimisation of sorting networks).”
Cooperative Multi-Agent Learning: The State of the Art (2005, background): “The Iterated Prisoner’s Dilemma is considered acooperativegame because the better pairs of agents tend to cooperate with one another [7, 8].”
Family Control and the Rent–Seeking Society (2003, unknown): “Moreover, Axelrod (1987) shows that behavior closely resembling tit-for-tat emerges spontaneously in repeated sequences of Prisoners’ Dilemma games where survival into the next period depends on a player’s payoff this period and strategies are randomly modified each period by a genetic algorithm.”
Chaotic sequences to improve the performance of evolutionary algorithms (2003, methodology): “the implementation presented in [25] and [26] an IPD strategy depends on the history of three previous moves, together with”