Evolution of altruistic punishments among heterogeneous conditional cooperators

Abstract It has been known that altruistic punishments solve the free rider problem in public goods games. Considering spatial structure and considering pure strategies significant advances have made understanding evolution of punishments. However, these models not considered key behavior regularities observed experimental field settings, where individuals behave like conditional cooperators who are more willing to donate also punish riders. behavioral regularities, without imposing a on population, I propose an evolutionary agent-based model which agents cooperators, each agent’s donation difference between number donations past threshold value propensity agent. Altruistic punishment depends focal agent randomly matched another The simulations show that, for certain inflicted costs punishments, generous evolve stabilize cooperation. results unlike previous models, it is necessary all riders equally; do so case selfish but negative reciprocators.