Effects of individual and collective decision rule on cooperation in public goods game

Research on public goods game has traditionally focused on studying the effects of punishment and reward in experimental settings, commonly known as standard games. However, these standard games often examine isolated interactions among individuals, leading to varying results based on factors such as the population studied, number of participants, and geographical context. Consequently, the understanding of cooperation evolution remains imprecise. To address this limitation, we are trying to generalize these experimental studies, we employ an agent-based model in this study to simulate interactions in public goods game and explore the influence of punishment and reward on the evolution of cooperation. Unlike standard games, evolutionary games offer a more comprehensive framework to capture and analyze a broader range of strategic interactions and behaviors. In our approach, we introduce two methods for applying punishment or reward: Individual Punishment or Reward and Collective Punishment or Reward. Under the first method, actors can individually reward or punish each other based on their contributions or free-riding behavior towards the collective good. In the second method, decisions for punishment or reward are made collectively after achieving a consensus or agreement among the members of the group. We provide a model for comparing Individual Decision Rule and Collective Decision Rule under punishment and reward, to study their effects on the evolution of contributions. Consistent with recent experimental findings, our results indicate that these mechanisms lead to a significant increase in contributions compared to the outcomes observed in standard public goods game. Additionally, we demonstrate the significant role of rewards in promoting contribution within public goods game.