# Evolutionary Prisoner's Dilemma Tournaments

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The prisoner's dilemma is a two-player game in which each player (prisoner) can either "cooperate" (stay silent) or "defect" (betray the other prisoner). If both players cooperate, they each get a reward ; if both defect, they get a punishment payoff ; if one player defects and the other cooperates, the defecting player gets a temptation payoff , while the cooperating player receives a sucker payoff . In the standard form of the game,

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, , and .

This Demonstration illustrates an evolutionary tournament based on the prisoner's dilemma that proceeds as follows: In the initial generation there are five players, each playing a particular strategy. These players then conduct a tournament in which each pair of players plays a large number of rounds of the prisoner's dilemma game using their respective strategies. The second generation consists of the same set of player types and player strategies, but redistributed according to the payoffs in this tournament. For example, if player 1's payoff was twice that of player 2, the second generation will include twice as many copies of player 1 (with each playing the strategy of player 1) as of player 2. Continuing this process over many generations, we find that some strategies tend to become dominant, while others die out. This Demonstration shows the evolution of player populations over 20 generations.

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Contributed by: Jiusi Li (April 2020)
Open content licensed under CC BY-NC-SA

## Details

All of the strategies implemented in this Demonstration are "memory-one" strategies; that is, the action of a player in a given round depends only on the actions by the two players in the previous round. Memory-one strategies can be described by a strategy vector whose components represent the probabilities that the player cooperates given a particular pair of actions by the two players in the previous round. For example, in a strategy with vector the player always cooperates if both players cooperated in the previous round, cooperates with 50% probability if one player cooperated and the other player defected in the previous round, and cooperates with 25% probability if both players defected in the previous round.

Memory-one strategies can be modeled by a Markov chain on the four states CC, CD, DC, DD, and the stationary distribution of this Markov chain determines the average payoffs for the two players over a large number of games [2, 4]. The stationary distribution is an eigenvector with eigenvalue 1 of the transition matrix of the Markov chain.

Controls

All-c: the player always cooperates. Strategy vector .

All-d: the player always defects. Strategy vector .

TFT (tit for tat [1]): the player cooperates if the opponent cooperated in the previous round, and defects if the opponent defected in the previous round. Strategy vector .

GTFT (generous tit for tat [3]): the player cooperates after every instance of the opponent’s cooperations and after 25% of the opponent’s defections. Strategy vector .

SET-2 (an example of an equalizer strategy [3, 4]): this strategy forces the opponent’s payoff to be 2 regardless of what strategy the opponent uses. Strategy vector: .

EXTORT-2 (an example of an extortionate strategy [3, 4]): a strategy that guarantees the player a higher or equal payoff no matter what the opponent plays. Strategy vector: .

GEN-2 (generous zero-determinant strategy [3]): strategy vector .

Spiteful [3]: the player cooperates if both players cooperated in the previous round and defects otherwise. Strategy vector .

Pavlov [3]: the player cooperates if both players made the same decision in the previous move, and defects otherwise. Strategy vector .

Random: a random strategy, given by a vector whose components are random numbers in .

References

[1] R. M. Axelrod, The Evolution of Cooperation, New York: Basic Books, 1984.

[2] S. Kuhn. "Prisoner's Dilemma." The Stanford Encyclopedia of Philosophy. (Apr 14, 2020) plato.stanford.edu/entries/prisoner-dilemma.

[3] P. Mathieu and J.-P. Delahaye, "New Winning Strategies for the Iterated Prisoner's Dilemma," Journal of Artificial Societies and Social Simulation, 20(4), 2017 pp. 1–12. doi:10.18564/jasss.3517.

[4] W. H. Press and F. J. Dyson, "Iterated Prisoner’s Dilemma Contains Strategies That Dominate Any Evolutionary Opponent," Proceedings of the National Academy of Sciences, 109(26), 2012 pp. 10409–10413. doi:10.1073/pnas.1206569109.

## Permanent Citation

Jiusi Li

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