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The iterated spatial game among the mobile population is an interesting problem in the fields of biological, social and economic sciences. Inspired by some recent works, this paper concentrates on the iterated snowdrift game among movable and myopic agents. Two different decision-making schemes, namely the utility-maximum rule and the Fermi rule, are applied and examined. In the former case, cooperation is found to be enhanced by moving velocity with an upper bound. The analytical results of the model are deduced at two extreme cases when agents cannot move or move quickly. In the latter case, the influences of velocity and temptation-to-defect are much more complicated. These results allow a deeper insight of the related model as well as the emergence of cooperation. [ABSTRACT FROM AUTHOR]

Abstract: The evolutionary spatial game in a mobile population has attracted many researchers of biological, social and economic sciences. Considering some facts observed in the real world, this paper proposes a novel spatial evolutionary snowdrift game model with movable players. In this model, one player interacts only with the nearest neighbor in each turn, and makes decision in a reinforcement learning way. In a very large range of the parameters moving ability enhances cooperation, but under some special condition, velocity heavily depresses cooperation. Some explanations have also been given out by investigating the strategy-change behavior of players. The findings may be helpful in understanding cooperative behavior in natural and social systems consisting of mobile agents. [Copyright &y& Elsevier]