Critical dynamics in the evolution of stochastic strategies for the iterated prisoner's dilemma.
The observed cooperation on the level of genes, cells, tissues, and individuals has been the object of intense study by evolutionary biologists, mainly because cooperation often flourishes in biological systems in apparent contradiction to the selfish goal of survival inherent in Darwinian evolution. In order to resolve this paradox, evolutionary game theory has focused on the Prisoner's Dilemma (PD), which incorporates the essence of this conflict. Here, we encode strategies for the iterated Prisoner's Dilemma (IPD) in terms of conditional probabilities that represent the response of decision pathways given previous plays. We find that if these stochastic strategies are encoded as genes that undergo Darwinian evolution, the environmental conditions that the strategies are adapting to determine the fixed point of the evolutionary trajectory, which could be either cooperation or defection. A transition between cooperative and defective attractors occurs as a function of different parameters such as mutation rate, replacement rate, and memory, all of which affect a player's ability to predict an opponent's behavior. These results imply that in populations of players that can use previous decisions to plan future ones, cooperation depends critically on whether the players can rely on facing the same strategies that they have adapted to. Defection, on the other hand, is the optimal adaptive response in environments that change so quickly that the information gathered from previous plays cannot usefully be integrated for a response.
Keck Graduate Institute for Applied Life Sciences, Claremont, California, USA.
Dimitris
D
Iliopoulos
Arend
A
Hintze
Christoph
C
Adami
D002468:Cell Physiological Phenomena / Q000235:genetics / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005716:Game Theory; D005796:Genes; D005828:Genetics, Population; D008957:Models, Genetic; D009154:Mutation; D025341:Principal Component Analysis; D013269:Stochastic Processes