Search Results (61)

AI-powered health assistants offer promising opportunities to enhance health management among older adults. However, real-world uptake remains limited, not only due to individual hesitation, but also because of complex interactions among users, platforms, and public policies. This study investigates the dynamic behavioral mechanisms behind adoption in aging populations using a tripartite evolutionary game model. Based on replicator dynamics, the model simulates the strategic behaviors of older adults, platforms, and government. It identifies evolutionarily stable strategies, examines convergence patterns, and evaluates parameter sensitivity through a Jacobian matrix analysis. Results show that when adoption costs are high, platform trust is low, and government support is limited, the system tends to converge to a low-adoption equilibrium with poor service quality. In contrast, sufficient policy incentives, platform investment, and user trust can shift the system toward a high-adoption state. Trust coefficients and incentive intensity are especially influential in shaping system dynamics. This study proposes a novel framework for understanding the co-evolution of trust, service optimization, and institutional support. It emphasizes the importance of coordinated trust-building strategies and layered policy incentives to promote sustainable engagement with AI health technologies in aging societies. Full article

Green building materials play a vital role in mitigating the significant carbon emissions produced by the construction industry. However, the widespread presence of greenwashing, where firms falsely portray their products or practices as environmentally friendly, presents a critical obstacle to the adoption of genuinely sustainable materials. The risk of collusion between building material enterprises and certification institutions further exacerbates this challenge by undermining trust in green certification processes. To investigate these issues, this study develops an evolutionary game model that captures the strategic interactions between building material enterprises and certification institutions. The model incorporates the behavioral assumptions of prospect theory, specifically bounded rationality, loss aversion, and diminishing sensitivity, to reflect the real-world decision-making behavior of the involved actors. The findings reveal three evolutionarily stable strategies (ESS) within the system. First, a higher initial willingness by both enterprises and certifiers to engage in ethical practices increases the likelihood of convergence to an optimal and stable outcome. Second, a greater degree of diminishing sensitivity in the value function promotes the adoption of authentic green behavior by enterprises. In contrast, a lower degree of diminishing sensitivity encourages certification institutions to refrain from collusion. Third, although the loss aversion coefficient does not directly affect strategy selection, higher levels of loss aversion lead to stronger preferences for green behavior among enterprises and noncollusive behavior among certifiers. This research makes a novel theoretical contribution by introducing prospect theory into the analysis of greenwashing behavior in the building materials sector. It also provides actionable insights for improving regulatory frameworks and certification standards to mitigate greenwashing and enhance institutional accountability. Full article

Antibiotic pressure exerts profound effects on bacterial physiology, not limited to classical genetic resistance mechanisms. Increasing evidence highlights the ability of pathogens to undergo metabolic rewiring—an adaptive, reversible reorganization of core metabolic pathways that promotes survival under antimicrobial stress. This review provides a comprehensive analysis of antibiotic-induced metabolic adaptations, encompassing glycolysis, the tricarboxylic acid cycle, fermentation, redox balance, amino acid catabolism, and membrane biosynthesis. We critically examine how diverse antibiotic classes—including β-lactams, aminoglycosides, quinolones, glycopeptides, polymyxins, and antimetabolites—interact with bacterial metabolism to induce tolerance and persistence, often preceding stable resistance mutations. In parallel, we explore the ecological and host-derived signals—such as immunometabolites and quorum sensing—that modulate these metabolic responses. Therapeutically, targeting metabolic pathways offers promising strategies to potentiate antibiotic efficacy, including enzyme inhibition, metabolic adjuvants, and precision-guided therapy based on pathogen metabolic profiling. By framing metabolic plasticity as a dynamic and evolutionarily relevant phenomenon, this review proposes a unifying model linking transient tolerance to stable resistance. Integrating metabolic rewiring into antimicrobial research, clinical diagnostics, and therapeutic design represents a necessary paradigm shift in combating bacterial persistence and resistance. Full article

This study employs an evolutionary game theory framework to analyze the interactive learning, imitation, and strategic evolution among multiple actors within China’s aquatic product traceability system. It focuses on four types of strategic interactions: between fishers and the government, fishers and consumers, fishers who adopt the traceability system and those who do not, and between consumers who purchase traceable aquatic products and those who do not. The evolutionarily stable strategies and equilibrium outcomes in each game depend on the net benefits obtained and the various costs borne by each party. Among these factors, transaction costs within the traceability system play a particularly critical role in shaping stakeholder behavior. The lower the transaction costs, the more likely stakeholders are to adopt strategies that support or enhance the functioning of the system. Therefore, reducing the operational and transaction costs of the traceability system should be a key policy focus for the government. This includes efforts in policy and regulatory development, platform and infrastructure construction, and the improvement of information exchange mechanisms to foster sustainable development in aquaculture. Full article

Mine water is both wastewater and a valuable unconventional water resource, and its recycling is crucial for the sustainable development of coal-resource-based cities. In response to the complex interactions among multiple stakeholders in the process of mine water recycling, this study innovatively develops a four-party evolutionary game model involving local government, coal mining enterprises, mine water operators, and water users. For the first time, key variables—mine water pricing, water volume, water rights trading, water resource taxation, and objective utility of water resources—are systematically integrated into a multi-agent game framework, extending the analysis beyond conventional policies, such as penalties and subsidies, to explore their impact on recycling behavior. The results show the following: (1) There are 10 possible evolutionary stabilization strategies in the system. The current optimal strategy includes supply, input, use, active support, while the ideal strategy under the market mechanism includes supply, input, use, passive support. (2) Local governments play a leading role in collaborative governance. The decisions of coal mining enterprises and mine water operators are highly interdependent, and these upstream actors significantly influence the water users’ strategies. (3) Government subsidies exhibit an inverted U-shaped effect, while punitive measures are more effective than incentives. The tax differential between recycled and discharged mine water incentivizes coal enterprises to adopt proactive measures, and water rights trading significantly enhances the users’ willingness. (4) Mine water should be priced significantly lower than fresh water and reasonably balanced between stakeholders. Industries with lower objective utility of water tend to prioritize its use. This study provides theoretical support for policy optimization and a market-based resource utilization of mine water. Full article

To address the limitations of the RRT-series algorithm and its variants, which have demonstrated a lack of dynamic adaptability in various environments, poor path quality, slow convergence rates, and a tendency to become trapped in local optima, this paper aims to propose a dynamic multi-strategy path-planning algorithm based on EG-DRRT* (Evolutionary Game-Theoretic Dynamic RRT*). The integration of evolutionary game theory allows the algorithm to use the concept of dynamically updating replicators to develop the payoff function for a fusion of multi-strategies. This enables a dynamic adjustment of the usage ratios of RRT*, Dijkstra, and Goal Bias algorithms. This approach directs the search process toward converging on the goal point, ultimately achieving an ESS (Evolutionarily Stable Strategy) equilibrium. The experimental results reveal that EG-DRRT* successfully establishes a dynamic balance between global exploration and local optimization across various environments, demonstrating remarkable adaptability and robustness. Additionally, EG-DRRT* shows substantial advantages compared to existing algorithms. Full article

This paper examines federated learning, a decentralized machine learning paradigm, focusing on privacy challenges. We introduce differential privacy mechanisms to protect privacy and quantify their impact on global model performance. Using evolutionary game theory, we establish a framework to analyze strategy dynamics and define utilities for different strategies based on Gaussian noise powers and training iterations. A differential privacy federated learning model (DPFLM) is analyzed within this framework. A key contribution is the thorough existence and stability analysis, identifying evolutionarily stable strategies (ESSs) and confirming their stability through simulations. This research provides theoretical insights for enhancing privacy protection in federated learning systems. Full article

Background: The majority of antigen-based SARS-CoV-2 (SCV2) vaccines utilized in the clinic have had the Spike protein or domains thereof as the immunogen. While the Spike protein is highly immunogenic, it is also subject to genetic drift over time, which has led to a series of variants of concern that continue to evolve, requiring yearly updates to the vaccine formulations. In this study, we investigate the potential of the N-terminal ectodomain of the ORF3a protein encoded by the orf3a gene of SCV2 to be an evolution-resistant vaccine antigen. This domain is highly conserved over time, and, unlike many other SCV2 conserved proteins, it is present on the exterior of the virion, making it accessible to antibodies. ORF3a is also important for eliciting robust anti-SARS-CoV-2 T-cell responses. Methods: We designed a DNA vaccine by fusing the N-terminal ectodomain of orf3a to macrophage-inflammatory protein 3α (MIP3α), which is a chemokine utilized in our laboratory that enhances vaccine immunogenicity by targeting an antigen to its receptor CCR6 present on immature dendritic cells. The DNA vaccine was tested in mouse immunogenicity studies, vaccinating by intramuscular (IM) electroporation and by intranasal (IN) with CpG adjuvant administrations. We also tested a peptide vaccine fusing amino acids 15–28 of the ectodomain to immunogenic carrier protein KLH, adjuvanted with Addavax. Results: The DNA IM route was able to induce 3a-specific splenic T-cell responses, showing proof of principle that the region can be immunogenic. The DNA IN route further showed that we could induce ORF3a-specific T-cell responses in the lung, which are critical for potential disease mitigation. The peptide vaccine elicited a robust anti-ORF3a antibody response systemically, as well as in the mucosa of the lungs and sinus cavity. Conclusions: These studies collectively show that this evolutionarily stable region can be targeted by vaccination strategies, and future work will test if these vaccines, alone or in combination, can result in reduced disease burden in animal challenge models. Full article

Manufacturers and consumers are boundedly rational and ultimately seek evolutionarily stable strategies through trial and error, imitation, and learning. It is important to study the pricing strategies of manufacturers and the purchasing channel decisions of consumers in the context of increasingly fierce competition in online channels, in addition to consumers’ loss aversion due to increasingly confusing promotional strategies; accordingly, in this paper, an evolutionary game including both parties is constructed, and the loss aversion factor from prospect theory is introduced. Based on data from Chinese media reports on the cosmetics industry, simulation and sensitivity analyses were conducted using Matlab R2024a. The results indicate that—in addition to channel services affecting the evolutionarily stable strategy for purchasing channel selection—a decrease in consumer loss aversion will help consumers reach the evolutionarily stable strategy faster. For manufacturers, channel services do not affect their evolution to a unified pricing strategy; however, when consumer loss aversion increases, manufacturers’ evolutionarily stable strategy will shift from a unified pricing strategy to a differentiated pricing strategy. Full article

Living organisms expend energy to sustain survival, a process which is reliant on consuming resources—termed here as the “cost of survival”. In the Prisoner’s Dilemma (PD), a classic model of social interaction, individual payoffs depend on choices to either provide benefits to others at a personal cost (cooperate) or exploit others to maximize personal gain (defect). We demonstrate that in an iterated Prisoner’s Dilemma (IPD), a simple “Always Cooperate” (ALLC) strategy evolves and remains evolutionarily stable when the cost of survival is sufficiently high, meaning exploited cooperators have a low probability of survival. We derive a rule for the evolutionary stability of cooperation, x/z >T/R, where x represents the duration of mutual cooperation, z the duration of exploitation, T the defector’s free-riding payoff, and R the payoff for mutual cooperation. This finding suggests that higher survival costs can enhance social welfare by selecting for cooperative strategies. Full article

The Machine-Managed Bidding (MMB) system is an innovative bidding mode implemented by the Chinese government to mitigate collusive bidding behavior. Prior studies have focused minimally on the bidding mechanism and the possible collusive bidding behavior under this mode. The objectives of this study are to analyze the bidding mechanism and the dynamic evolution of collusive bidding behavior under the MMB system and provide targeted regulation countermeasures. To this end, this study develops an evolutionary game model among collusion initiators, free bidders, and regulators, explores possible scenarios for evolutionarily stable strategies, and performs sensitivity analysis of critical parameters utilizing MATLAB software (Version R2024a) based on empirical data. Results indicate that: (1) The MMB model significantly mitigates vertical collusive bidding behavior but lacks measures for governing transverse collusive bidding; (2) The game model has five evolutionarily stable strategies, with the one where the collusion initiator adopting the “non-collude” strategy, the free bidder adopting the “bid” strategy, and the regulator adopting the “negative regulate” strategy being the optimal evolutionary stable strategy; (3) Decreasing the costs associated with preparing bid documents, enhancing supervision costs, increasing the technical complexity of collusive bidding, and expanding the total number of construction enterprises with high-credit and low-credit ratings can expedite the evolution of the three participants toward the optimal evolutionarily stable strategy. This study supplements current knowledge on the regulation of collusive bidding behavior and enriches the knowledge framework of the MMB model. This study also provides insights for policymakers to guarantee the smooth implementation of the MMB. Full article

The promotion of energy conservation and emission reduction involves a multi-party game among governments, enterprises, and other stakeholders. To explore the game relationships among governments, the public, and coal-fired power enterprises under the “dual carbon targets”, this paper constructs an evolutionary game model for energy conservation and emission reduction involving three parties: the government, coal-fired power enterprises, and the public. Through a theoretical analysis and simulation analysis of the case study involving a central Hebei energy enterprise in China, the impact of parameter variations on the strategic choices of all parties and the evolutionarily stable strategies of the system is thoroughly discussed. The research findings indicate that reducing public supervision costs, increasing government rewards, subsidies, and penalties, and enhancing government regulatory capabilities are crucial factors in promoting energy-saving and emission-reduction efforts by coal-fired power enterprises. After multiple evolutionary iterations, the tripartite evolutionary game system ultimately reaches an evolutionarily stable state of government regulation, public supervision, and energy-saving and emission-reduction by coal-fired power enterprises at the point $E_8(\mathrm{1,1},1)$. Based on these findings, we propose a series of policy recommendations aimed at providing theoretical support for the Chinese government to achieve its energy-saving and emission-reduction strategies under the dual-carbon targets. These recommendations also offer practical guidance for the government in formulating emission reduction policies, for enterprises in optimizing their operational strategies, and for the public in participating in emission reduction efforts. Full article

This paper delves into the evolutionary dynamics of dynamic games among governments, traditional automotive enterprises, and scientific research institutions during the new energy transition process by establishing a stochastic evolutionary game model. The research focuses on exploring the conditions for the formation of system stability and the key factors influencing strategic choices. MATLAB R2021a software is employed to simulate the game process, visually demonstrating the dynamic changes in the behaviors of each participant. The results indicate that research and development (R&D) costs are a crucial consideration for scientific research institutions when deciding whether to collaborate with traditional automotive enterprises. Traditional automotive enterprises exhibit significantly higher sensitivity to government incentives for cooperation than to potential penalties for non-cooperation. Furthermore, an increase in government support costs notably dampens its enthusiasm for promoting the development of the new energy transition. Reducing government support costs and R&D costs for scientific research institutions, as well as enhancing rewards for cooperative behavior and penalties for non-cooperative behavior, can effectively facilitate the formation of evolutionarily stable strategies among governments, traditional automotive enterprises, and scientific research institutions. Full article

This study investigates the evolutionary game dynamics among electric vehicle distribution entities in the context of shared charging facilities, addressing the critical issue of inadequate charging resources. To understand the behavior of different stakeholders under government incentive policies, we develop an evolutionary game model involving a government department and two logistics enterprises (A and B). Through stability analysis, we explore equilibrium conditions of evolutionarily stable strategies (ESSs) for the tripartite evolutionary game. To ensure the robustness of our findings, we conduct a MATLAB simulation analysis to validate the analytical results. Our findings highlight that government subsidies, the costs incurred by logistics enterprises to share charging facilities, and the additional distribution income derived from this sharing are critical in determining whether the evolutionary game can achieve a stable equilibrium state. This research enables logistics companies to optimize the use of charging resources, lower operating costs, and enhance delivery efficiency. Additionally, government subsidy policies play a crucial role in encouraging logistics enterprises to engage in charging facility sharing, thereby fostering the sustainable development of the entire logistics industry. Based on these insights, the paper offers practical recommendations to further promote the sharing of charging facilities in electric vehicle distribution. Full article

Forests provide goods and services while maintaining ecological security. However, the market does not adequately reflect their economic benefits, posing a significant challenge to the Payments for Forest Ecosystem Services (PFES). The involvement of multiple stakeholders with varying responsibilities and interests complicates collaboration and hinders effective governance. This study proposes an integrated approach using stakeholder analysis, social network analysis, and evolutionary game analysis to explore the collaborative governance of stakeholders in PFES. Through field surveys, the study empirically investigates PFES in China, demonstrating the effectiveness of this integrated approach. The results indicate the following: (i) Stakeholders are classified into three categories; the key stakeholders include the central and local governments, forest managers, and paying users. (ii) Stakeholders still need to strengthen collaboration. Local governments, forest managers, their employees, and communities exert widespread influence; paying users and research institutions have high efficiency in resource sharing. (iii) Five evolutionarily stable strategies are observed at different stages. Government intervention is crucial for changing the stagnant state. Benefits and government incentives have a positive impact on stakeholder collaborative governance. The research findings offer theoretical insights to enhance stakeholder collaboration and promote the development of the PFES. Key strategies include addressing key stakeholders’ needs, diversifying incentives, and establishing an accessible information platform. Full article