Addressing both natural and societal challenges requires collective cooperation. Studies on collective-risk social dilemmas have shown that individual decisions are influenced by the perceived risk of collective failure. However, existing feedback-evolving game models often focus on a single feedback mechanism, such as the coupling between cooperation and risk or between cooperation and cost. In many real-world scenarios, however, the level of cooperation, the cost of cooperating, and the collective risk are dynamically interlinked. Here, we present an evolutionary game model that considers the interplay of these three variables. Our analysis shows that the worst-case scenario, characterized by full defection, maximum risk, and the highest cost of cooperation, remains a stable evolutionary attractor. Nevertheless, cooperation can emerge and persist because the system also supports stable equilibria with non-zero cooperation. The system exhibits multistability, meaning that different initial conditions lead to either sustained cooperation or a tragedy of the commons. These findings highlight that initial levels of cooperation, cost, and risk collectively determine whether a population can avert a tragic outcome.

Crowdsourcing, which decomposes tasks or projects through the internet platform and outsources them to a large number of people, has gained significant academic and industry interest due to its efficiency. Although prior theoretical studies have employed evolutionary game theory to study crowdsourcing systems, existing studies have predominantly focused on the game interactions between crowdsourcing worker and requester, often neglecting the platform as a stakeholder. Here we propose an evolutionary game model for tripartite crowdsourcing, accounting for bounded rationality and potential collusion between platform and crowdsourcing worker, with incentives designed to counteract free-riding and deceptive feedback. Our theoretical results indicate that the system can achieve a desired state where worker is certain to choose to provide high-quality work, platform is certain to implement diligent monitoring, and requester is certain to adopt an integrity strategy. Moreover, when the platform consistently adopts the monitoring strategy, the system exhibits oscillatory dynamics, with the probabilities of the worker providing high-quality work and the requester adopting an integrity strategy fluctuating within a bounded interval. Finally, we present numerical examples to validate our theoretical results.

Promoting cooperation among conflicting entities in human society and intelligent systems is a formidable task. One potential solution could involve the formulation of incentives designed to decrease the benefits of noncooperators and/or increase the rewards for cooperators. We put forth a novel incentive approach, specifically, a guidance strategy where certain cooperators willingly bear a cost to alter the actions of agents who intend to defect prior to the actual commencement of a game. We introduce an innovative game-theoretical framework that sheds light on the dynamics of guidance strategies, encompassing both peer guidance and pool guidance. Under the peer guidance scheme, each guider independently incurs the cost to influence agents intending to defect, whereas in the pool guidance scheme, guiders organically establish an institution to influence agents prone to free riding. Regardless of whether a peer or pool guidance scheme is utilized, the implementation of a guidance strategy has proven to be remarkably effective in reducing the instances of pure cooperation, also known as second-order free riding. Intriguingly, our result suggests that the pool guidance strategy demonstrates a more potent deterrent effect on second-order free-riding behavior than the peer guidance strategy, particularly when the cost of guidance is exceptionally high. These findings underscore the significance of preguidance in fostering cooperation in human and multiagent AI systems and could offer valuable insights for the development of a regulatory mechanism for preemptive guidance and subsequent punishment.