Probabilistic punishment and reward under rule of trust-based decision-making in continuous public goods game

Abstract

Altruistic punishment and reward have been proved to promote the evolution of cooperation in the public goods game(PGG), but the punishers and the rewarders have to pay a price for these behaviors and that results in overall loss of interest. In present work, probabilistic punishment and reward are introduced to PGG. Probabilistic punishment and reward mean that punishment and reward are executed with a certain probability. Although that will reduce unnecessary costs, occasional absence of execution can lead to distrust. Thus we focus on how to implement punishment and reward efficiently within the structured populations. Numerical simulations are performed and prove that probabilistic punishment and reward can promote the evolution of cooperation more effectively. Further researches indicate that there is an optimal executing probability to promote cooperation and maximize reduction of cost. In addition, when the unit cost is high, the PGG with probabilistic punishment and reward still helps the evolution of altruistic punishers and rewarders, thereby avoiding collapse of cooperation.