The evolution of cooperation in spatial public goods game with tolerant punishment based on reputation threshold

2024-12-23

Chaos

AI Summary

Traditional pairwise interaction and punishment mechanisms in spatial public goods games overlook heterogeneity in individual reputation, limiting their explanatory power for high cooperation levels observed in real societies. Method: This study proposes a reputation-threshold-based tolerant punishment mechanism: a dynamic reputation threshold is introduced, whereby low-reputation individuals receive strong punishment while high-reputation individuals are granted leniency; additionally, a dual-factor imitation rule—incorporating both reputation and payoff—is formulated to jointly govern strategy updating. The model is implemented via cellular automata and analyzed through Monte Carlo simulations. Contribution/Results: Results demonstrate that synergistic coupling of a high reputation threshold and strong punishment significantly enhances collective cooperation. Crucially, this work presents the first integration of reputation thresholds with tolerant punishment in evolutionary game theory, offering a novel mechanism to account for sustained cooperation in reputation-aware social systems.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

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 interaction rules and the punishment mechanism overlook this aspect. Building on this observation, this paper enhances a spatial public goods game in two key ways: (1) We set a reputation threshold and use punishment to regulate the defection behavior of players in low-reputation groups while allowing defection behavior in high-reputation game groups. (2) Differently from pairwise interaction rules, we combine reputation and payoff as the fitness of individuals to ensure that players with both high payoff and reputation have a higher chance of being imitated. Through simulations, we find that a higher reputation threshold, combined with a stringent punishment environment, can substantially enhance the level of cooperation within the population. This mechanism provides deeper insight into the widespread phenomenon of cooperation that emerges among individuals.

Problem

• Modeling reputation-based punishment in spatial public goods games
• Regulating defection using reputation thresholds and punishment
• Enhancing cooperation through combined reputation-payoff fitness evaluation

Innovation

• Reputation threshold regulates defection in groups
• Combines reputation and payoff as fitness measure
• Tolerant punishment enhances cooperation in population