Spatial multi-games under myopic update rule

Considering the population diversity and the limitation of individual information in repeated N-person games, we study a spatial multi-games model under the myopic rule in this paper, in which two distinct types of players participate in snowdrift game (SG) and prisoner’s dilemma game (PDG), respectively. Monte Carlo simulation method is used to study: the effects of game intensity parameters b and $$\delta $$ δ , noise parameter k and mixing ratio p on the frequency of cooperators; the difference between learning update rule and myopic update rule. The results demonstrate that: (1) when the values of b and $$\delta $$ δ are small, noise parameter k can promote the emergence of cooperation in SG with myopic update rule; (2) different from learning mechanism, the effect of the parameters p on the frequency of cooperators is nonmonotonic under myopic mechanism; (3) cooperators can form clusters to resist the invasion of defectors under learning update rule, while cooperators and defectors tend to form the chessboard-like patterns to increase individual payoff under myopic update rule. Graphic Abstract