Aspiration-based learning promotes cooperation in spatial prisoner's dilemma games

Abstract

We study the evolution of cooperation in spatial prisoner's dilemma by proposing an aspiration-based reference learning, under which individuals switch the learning agents only if the achieved payoffs are lower than their aspirations. Both synchronous and asynchronous updates are considered. We find that the aspiration level can substantially influence the evolution of cooperation, with the moderate aspiration level leading to a plateau of the high cooperation level. There exist phase transitions for proper combinations of parameters and we give an analysis of the phase transition points. We also investigate the stationary configuration patterns and the stationary distributions of cooperators and defectors on the square lattice for a comprehensive understanding. The behavior of the well-mixed system of our model has also been discussed. Our results may provide further insights into understanding the role played by individual aspiration in the emergence of cooperation.