Iterated snowdrift game among mobile agents with myopic expected-reward based decision rule: Numerical and analytical research

• Agents are myopic. Two kinds of decision-making rules are considered.
• In the utility-maximum model, cooperation is enhanced by the moving ability.
• The analytical results of the utility-maximum model are deduced.
• In the Fermi model, the increase of the cost-to-benefit ratio may improve cooperation.

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.