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Snapshots of the spatial arrangements of strategies at four representative moments for different reputation thresholds. (a) R 0 = 0, (b) R 0 = 4, (c) R 0 = 12, and (d) R 0 = 16, and fixed enhancement factor r = 2.5 and b = 0.2. Each row from top to bottom represents a situation corresponding to a different reputation threshold R 0. Each column from left to right represents a different time step t. White pixels stand for cooperators and blue pixels for defectors. It is noteworthy that defectors can survive in an environment where no punishment is introduced ( R 0 = 0). However, in an environment with a higher reputation threshold ( R 0 = 4, 12, and 16), after 100 time steps, cooperators gradually gather to form stable clusters. Thus, a strict reputation evaluation environment can induce the formation and development of cooperator clusters.
Source publication
Reputation and punishment are significant guidelines for regulating individual behavior in human society, and those with a good reputation are more likely to be imitated by others. In addition, society imposes varying degrees of punishment for behaviors that harm the interests of groups with different reputations. However, conventional pairwise int...
Citations
... As a further step, the punishment is used as an incentive mechanism, and a new classification form of punishment is introduced into the spatial public goods game, 53 where the probability of punishment changes dynamically according to the number of consecutive defections in the game. Furthermore, Zhang et al. 46 studied the impact of the reputation threshold based tolerant punishment mechanism on the evolution of cooperation in spatial public goods games. Specifically, if an individual's reputation fell below the threshold, he would be punished after he chose to defect; whereas he was not punished even though he adopted defective strategy, if his reputation exceeded the threshold. ...
Trust holds a pivotal position in contemporary society. Yet, the question of how to elevate and sustain trust among selfish individuals poses a formidable challenge. To delve into this issue, we incorporate a graded punishment strategy into a networked N-player trust game, aiming to observe the progression of trust-related behavior. Within this game framework, punishers uphold a certain degree of trust among the participants by incurring an extra expense to exclude those who betray trust. By conducting numerous Monte Carlo simulation experiments, we uncover that the graded punishment strategy can effectively curtail untrustworthy conduct to a significant degree, potentially even eliminating such behavior, thereby fostering an improvement in the overall trust level within the population. However, to effectively deploy this strategy, it is imperative to strike a balance between the penalty cost and the penalty amount, ensuring that the natural evolution of the system is not unduly disrupted. This balance is crucial for preserving the stability and sustainability of the system while safeguarding trust. Broadly speaking, our study offers fresh insights and approaches for enhancing and maintaining trust in the networked society, while also highlighting the avenues and challenges for future research, particularly in the realm of applying graded punishment strategies.
