Exploring the evolution of thermal power market participants' strategies under carbon trading policy: An evolutionary game and system dynamics approach

Exploring the Evolution of Thermal Power Market Participants' Strategies Under Carbon Trading Policy: An Evolutionary Game and System Dynamics Approach

Urban food waste management with multi-agent participation: A combination of evolutionary game and system dynamics approach

We consider a spatial generalization of evolutionary game theory in which strategies are distributed over a spatial array of sites. We assume that the strategy corresponding to a given site has local interactions with the strategies sitting on neighbouring sites, and that the strategies change if neighbouring strategies are doing better. After briefly setting the stage with a formal definition of spatial evolutionary game theory, we consider the spatial extension of the Hawk-Dove game, and we show that the results are qualitatively different from those obtained from classical evolutionary game theory. For example, the proportion of Hawks in the population is in general lower in the spatial game than in the classical one. We also consider spatial generalizations of the extensions of the Hawk-Dove game obtained by including strategies such as Retaliator and Bully. Here, too, the results from the spatial game are very different from the classical results. In particular, with space Retaliator is a much more successful strategy than one would expect from classical considerations. This suggests that, in general, spatial structure may facilitate the evolution of strategies such as Retaliator, which do not necessarily prosper classically, and which are reminiscent of the \`nice', \`provokable' and `forgiving' strategies which seem to play a central role in the evolution of cooperation. The results indicate that including spatial structure in evolutionary game theory is a fruitful extension.

Simulating policy interventions for different quota targets of renewable portfolio standard: A combination of evolutionary game and system dynamics approach

Social or biochemical networks can often divide into two opposite alliances in response to structural conflicts between positive (friendly, activating) and negative (hostile, inhibiting) interactions. Yet, the underlying dynamics on how the opposite alliances are spontaneously formed to minimize the structural conflicts is still unclear. Here, we demonstrate that evolutionary game dynamics provides a felicitous possible tool to characterize the evolution and formation of alliances in signed networks. Indeed, an evolutionary game dynamics on signed networks is proposed such that each node can adaptively adjust its choice of alliances to maximize its own fitness, which yet leads to a minimization of the structural conflicts in the entire network. Numerical experiments show that the evolutionary game approach is universally efficient in quality and speed to find optimal solutions for all undirected or directed, unweighted or weighted signed networks. Moreover, the evolutionary game approach is inherently distributed. These characteristics thus suggest the evolutionary game dynamic approach as a feasible and effective tool for determining the structural conflicts in large-scale on-line signed networks.

Information collaboration is a core driver of digital transformation and efficiency improvement in agri-food supply chains. This study constructs a quadripartite evolutionary game model involving the government, an information service platform, farmers, and agri-food enterprises. By integrating system dynamics, it analyzes stakeholders’ strategic interactions and evolutionary pathways while exploring the regulatory effects of key parameters in reward and penalty mechanisms on system convergence. The key findings are as follows: (1) The system reaches a stable equilibrium regardless of initial strategy combinations. (2) The reward–penalty mechanism is essential for equilibrium stability, but the reward amount and allocation ratios must meet threshold constraints. (3) Given the significant path-dependent lock-in effect in agri-food enterprises’ strategy convergence under static parameters, a dynamic parameter configuration scheme is proposed to reshape convergence and optimize equilibrium. The simulation results indicate that dynamic parameter regulation sacrifices the regulatory efficiency of the information service platform to enhance the overall collaboration. A joint dynamic reward–penalty strategy improves efficiency but delays platform convergence, whereas a single dynamic incentive offers a balanced trade-off. Based on this, an incentive framework is developed to guide government incentive design. This study expands the theoretical framework of information collaboration in AFSCs and provides practical guidance for policymakers.

Railway accidents pose a significant threat to the industry, necessitating enhanced research into railway transportation safety. This study integrated a public oversight framework into the existing safety governance structure of railway transport operators, utilizing a four-party evolutionary game model and system dynamics for enhancement. Simulations conducted with Vensim software demonstrate that increased public supervision increases safety operation rates and improves the safety-related productivity of auxiliary enterprises. However, uncertainties in the evolutionary strategy process were identified. To address equilibrium fluctuations, a dynamic reward-punishment mechanism was developed. The optimized system achieved a safety operation rate of 99.7%, enhanced the safety-related productivity of the auxiliary enterprises to 93.2%, and increased the public supervision rate to 87.2%. These findings indicate that effective public participation and dynamic incentives can significantly improve safety management and prevent losses in railway sectors, offering valuable theoretical and practical insights for global railway enterprises.

The rapid development of generative artificial intelligence (GenAI) has generated significant economic and social value, alongside risks to user privacy. For this purpose, this study investigates privacy protection in human-AI interaction by employing a combined approach of evolutionary game and system dynamics. A three-party game model was developed to analyze the interactive effects and evolution of privacy protection strategies among the government, GenAI company, and users. Sensitivity analysis through system dynamics simulations was conducted on four kinds of factors—government, company, users, and incentive mechanisms, to reveal how these factors influence the strategy choices of the three parties. The results suggest that the government’s reputation, subsidies, free-riding benefits, fines, rewards from GenAI company to users, and the cost–benefit considerations of all three parties are key factors affecting strategic decisions. Moderate fine and subsidy policies can effectively promote privacy protection, with subsidy policies proving to be more effective than penalty policies. This paper provides theoretical support and decision-making guidance for balancing technological development and privacy protection in human–AI interaction, contributing to the regulated and orderly development of Generative Artificial Intelligence.

To promote efficient construction waste recycling and reuse, a novel waste management approach based on blockchain technology was introduced to the industry. However, adopting blockchain platforms in construction waste recycling and reuse may impact the behavioral strategies of stakeholders and impede the prediction of the specific impacts of stakeholders’ decisions. Accordingly, this study addresses two primary questions: (1) What are the collaborative framework and the behavioral evolution trends of multiple stakeholders within the context of blockchain? (2) How can the behavioral strategies of multiple stakeholders be systematically coordinated to achieve efficient construction waste recycling and reuse driven by blockchain? To answer these questions, a tripartite game model combined with system dynamics was constructed. In this model, we aimed to elucidate the internal organizational framework, analyze the dynamic evolution process, and assess the influence of decisions made by multiple stakeholders at the individual level. It also offers corresponding policy recommendations for efficient construction waste recycling and reuse driven by blockchain at the system level. This study offers three innovations. First, it considers the decision-making of multiple stakeholders as an interdependent and coevolutionary process to overcome the defects of analyzing only one type of participant. Second, in contrast to the static analysis method, it employs a dynamic system approach to deeply analyze the evolving structures of blockchain-based projects. Third, it provides a theoretical framework for the practical implementation of blockchain-driven platforms in managing construction waste recycling and reuse, thus fostering effective policy development and management practices. This framework aims to promote sustainable development in construction waste recycling and reuse projects in China as well as globally.

An evolutionary game and system dynamics approach for the production and consumption of carbon-labeled products-based on a media monitoring perspective

IntroductionAddressing the issue of land abandonment in rural areas is a critical strategy for ensuring national food security and promoting high-quality agricultural and rural development.MethodsBased on the assumption of bounded rationality, this paper constructs an evolutionary game model involving three key stakeholders: farmers, village collectives, and local governments. By integrating system dynamics, we simulate and analyze the behavioral strategies of participants, examining the mechanisms of government regulation. Additionally, we explore the moderating role of agricultural social services and further investigate the impact of incentive and penalty measures.Results(1) under specific constraints, the system evolves toward the optimal equilibrium (1,1,1); (2) government policies influence the strategic choices of farmers and village collectives, with excessively high or low incentive and penalty measures proving ineffective in mitigating land abandonment; (3) compared to incentives, penalty measures exhibit a more substantial impact on addressing land abandonment; (4) enhancing agricultural social services can strengthen the effectiveness of incentive and penalty policies.DiscussionBased on these results, the paper offers policy recommendations from four perspectives: regulatory reform, the integration of incentives and penalties, flexible subsidies, and enhanced collaboration among key stakeholders, thereby providing both theoretical insights and practical guidance for addressing land abandonment.

In the children's game of rock-scissors-paper, players each choose one of three strategies. A rock beats a pair of scissors, scissors beat a sheet of paper and paper beats a rock, so the strategies form a competitive cycle. Although cycles in competitive ability appear to be reasonably rare among terrestrial plants, they are common among marine sessile organisms and have been reported in other contexts. Here we consider a system with three species in a competitive loop and show that this simple ecology exhibits two counter-intuitive phenomena. First, the species that is least competitive is expected to have the largest population and, where there are oscillations in a finite population, to be the least likely to die out. As a consequence an apparent weakening of a species leads to an increase in its population. Second, evolution favours the most competitive individuals within a species, which leads to a decline in its population. This is analogous to the tragedy of the commons, but here, rather than leading to a collapse, the 'tragedy' acts to maintain diversity.

How does feed-in tariff and renewable portfolio standard evolve synergistically? An integrated approach of tripartite evolutionary game and system dynamics

Moran process in evolutionary game dynamics with interval payoffs and its application

The carbon trading policy is a product of the combination of government power and market power in order to achieve my country's "carbon peak" and "carbon neutrality" as an important emission reduction tool for our party in combination with my country's actual situation. Based on the spatial Tobin double difference and other methods, this paper analyzes the emission reduction effect with the data since the implementation of my country's carbon trading policy from 2003 to 2019 and analyzes its transmission mechanism. The study found that the carbon trading policy can significantly reduce the carbon emission intensity of the pilot area, and the operation status of the carbon market can also significantly reduce the carbon emission intensity of the pilot area. The government can adjust the operation status of the carbon market to improve the regional emission reduction effect. The spatial interaction term also shows that the carbon trading policy has a positive spillover effect on the surrounding areas. The mediation effect analysis shows that the carbon trading policy mainly promotes the reduction of regional carbon emission intensity through technological innovation and industrial structure optimization. In the heterogeneity analysis, carbon trading policies have different effects on the eastern, central and western regions, so carbon trading policies should be implemented according to local conditions.