com.raphtory.algorithms.generic.gametheory.PrisonersDilemma

PrisonersDilemma

PrisonersDilemma(proportionCoop: Float = 0.5f, benefit: Float, cost: Float = 1.0f, noGames: Int = 100): run iterative game of prisoners dilemma

An iterative game of Prisoners’ dilemma is played on the network. In each round, vertices simultaneously play a round of Prisoners’ dilemma with their neighbours, getting a payoff according to whether each player chose to cooperate (C) or defect(D). After the game is played, a vertex adopts its strategy by choosing the highest scoring strategy in its direct neighbourhood. This carries on until either a pre-defined number of games is played or if none of the vertices change their strategy.

Parameters

proportionCoop: Float=0.5f: Proportion of vertices that start out as cooperators (uniformly sampled). Alternatively, vertices starting with a status “cooperator” set to 0 begin as cooperators.
benefit: Float: Benefit parameter for prisoners’ dilemma game. See [1]
cost: Float: Cost parameter for prisoners’ dilemma game. See [1]
noGames: Maximum number of games to be played.
seed: optional seed for testing purposes.

States

cooperator: Int: The latest status: cooperator (0), or defector (1), of the vertex at that iteration.
cooperationHistory: mutable.Queue[Int]: The full history of a vertex’ cooperation status for the games played.

Returns

vertex name	cooperation history
`name: String`	`cooperationHistory: Queue[Int]`

References

[1] Nowak, M. A., & May, R. M. (1992). Evolutionary games and spatial chaos. Nature, 359(6398), 826-829.