Evolving learning rules and emergence of cooperation in spatial Prisoner's Dilemma - Quantitative Biology > Populations and EvolutionReport as inadecuate




Evolving learning rules and emergence of cooperation in spatial Prisoner's Dilemma - Quantitative Biology > Populations and Evolution - Download this document for free, or read online. Document in PDF available to download.

Abstract: In the evolutionary Prisoner-s Dilemma PD game, agents play with each otherand update their strategies in every generation according to some microscopicdynamical rule. In its spatial version, agents do not play with every otherbut, instead, interact only with their neighbors, thus mimicking the existingof a social or contact network that defines who interacts with whom. In thiswork, we explore evolutionary, spatial PD systems consisting of two types ofagents, each with a certain update reproduction, learning rule. Weinvestigate two different scenarios: in the first case, update rules remainfixed for the entire evolution of the system; in the second case, agents updateboth strategy and update rule in every generation. We show that in a well-mixedpopulation the evolutionary outcome is always full defection. We subsequentlyfocus on two-strategy competition with nearest-neighbor interactions on thecontact network and synchronized update of strategies. Our results show that,for an important range of the parameters of the game, the final state of thesystem is largely different from that arising from the usual setup of a single,fixed dynamical rule. Furthermore, the results are also very different ifupdate rules are fixed or evolve with the strategies. In these respect, we havestudied representative update rules, finding that some of them may becomeextinct while others prevail. We describe the new and rich variety of finaloutcomes that arise from this co-evolutionary dynamics. We include examples ofother neighborhoods and asynchronous updating that confirm the robustness ofour conclusions. Our results pave the way to an evolutionary rationale formodelling social interactions through game theory with a preferred set ofupdate rules.



Author: Luis G. Moyano, Angel Sánchez

Source: https://arxiv.org/







Related documents