Search Results (293)

Modern traffic management requires sophisticated approaches to address the complexities of urban road networks, which continue to grow in complexity due to increasing urbanization and vehicle usage. Traditional methods often fall short in mitigating congestion and optimizing traffic flow, inducing the exploration of innovative traffic control strategies based on advanced theoretical frameworks. In this sense, we explore different game theory-based control strategies in an eight-intersection traffic network modeled by means of hybrid systems and graph theory, using a software simulator that combines the multi-modal traffic simulation software VISSIM and MATLAB to integrate traffic network parameters and population game criteria. Across five distinct network scenarios with varying saturation conditions, we explore a fixed-time scheme of signaling by means of fictitious play dynamics and adaptive schemes, using dynamics such as Smith, replicator, Logit and Brown–Von Neumann–Nash (BNN). Results show better performance for Smith and replicator dynamics in terms of traffic parameters both for fixed and variable signaling times, with an interesting outcome of fictitious play over BNN and Logit. Full article

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

Credit supervision of practitioners in the water conservancy construction market, a vital pillar of national infrastructure development, significantly impacts project safety and the maintenance of order in the industry. From the perspective of indirect supervision, this study constructs a tripartite evolutionary game model involving government departments, enterprises, and practitioners to analyze the dynamic evolution mechanism of credit supervision. By examining the strategic interactions among the three parties under different regulatory scenarios, we identify key factors influencing the stable equilibrium of evolution and verify the theoretical conclusions through numerical simulations. The study yields several key insights. First, while government regulation and social supervision can substantially increase the likelihood of practitioners’ integrity, relying solely on administrative regulation has an efficiency limit. Second, the effectiveness of the reward and punishment mechanism of the direct manager plays a crucial leveraging role in credit evolution. Lastly, under differentiated regulatory strategies, high-credit practitioners respond more strongly to long-term cost optimization, while low-credit practitioners are more effectively deterred by short-term, high-intensity disciplinary actions. Based on these findings, this study proposes a systematic governance framework of “regulatory model innovation–corporate responsibility enhancement–social supervision deepening.” Unlike previous studies, this framework adopts a comprehensive approach from three dimensions: regulatory model innovation, corporate responsibility enhancement, and social supervision deepening. It offers a more holistic and systematic solution for refining the credit system in the water conservancy construction market, providing both theoretical support and practical approaches. Full article

Multi-agent reinforcement learning has demonstrated excellent performance in complex decision-making tasks such as electronic games, power grid management, and autonomous driving. However, its vulnerability to adversarial attacks may impede its widespread application. Currently, research on adversarial attacks in reinforcement learning primarily focuses on single-agent scenarios, while studies in multi-agent settings are relatively limited, especially regarding how to achieve optimized attacks with fewer steps. This paper aims to bridge the gap by proposing a heuristic exploration-based attack method named the Search for Key steps and Key agents Attack (SKKA). Unlike previous studies that train a reinforcement learning model to explore attack strategies, our approach relies on a constructed predictive model and a T-value function to search for the optimal attack strategy. The predictive model predicts the environment and agent states after executing the current attack for a certain period, based on simulated environment feedback. The T-value function is then used to evaluate the effectiveness of the current attack. We select the strategy with the highest attack effectiveness from all possible attacks and execute it in the real environment. Experimental results demonstrate that our attack method ensures maximum attack effectiveness while greatly reducing the number of attack steps, thereby improving attack efficiency. In the StarCraft Multi-Agent Challenge (SMAC) scenario, by attacking 5–15% of the time steps, we can reduce the win rate from 99% to nearly 0%. By attacking approximately 20% of the agents and 24% of the time steps, we can reduce the win rate to around 3%. Full article

The increasing number of natural and man-made disasters registered at the global level is causing a significant amount of damage. This represents one of the main sustainability challenges at the global level. The collapse of the Twin Towers, Hurricane Katrina, and the nuclear accident at the Fukushima power plant are some of the most representative disaster events that occurred at the beginning of the third millennium. These relevant disasters need an enhanced level of preparedness to reduce the gaps between the plan and its implementation. Among these actions, training and exercises play a relevant role because they increase the capability of planners, managers, and the people involved. By focusing on the exposure risk component, the general objective of the research is to obtain quantitative evaluations of the exercise’s contribution to risk reduction through evacuation. The paper aims to analyze serious games using a set of methods and models that simulate an urban risk reduction plan. In particular, the paper proposes a transparent framework that merges transport risk analysis (TRA) and transport system models (TSMs), developing serious game activities with the support of emerging information and communication technologies (e-ICT). Transparency is possible through the explicitation of reproducible analytical formulations and linked parameters. The core framework of serious games is constituted by a set of models that reproduce the effects of players’ choices, including planned actions of decisionmakers and travel users’ choices. The framework constitutes the prototype of a digital platform in a “non-stressful” context aimed at providing more insights about the effects of planned actions. The proposed framework is characterized by transparency, a feature that allows other analysts and planners to reproduce each risk scenario, by applying TRA and relative effects simulations in territorial contexts by means of TSMs and parameters updated by e-ICT. A basic experimentation is performed by using a game, presenting the main results of a prototype test based on a reproducible exercise. The prototype experiment demonstrates the efficacy of increasing preparedness levels and reducing exposure by designing and implementing a serious game. The paper’s methodology and results are useful for policymakers, emergency managers, and the community for increasing the preparedness level. Full article

The complexities inherent in resource management within cross-domain digital innovation ecosystems have significantly intensified, giving rise to heightened challenges in collaborative interactions among diverse stakeholders, thereby directly impacting systemic stability. Conventional governance frameworks for innovation ecosystems are inadequate in effectively managing the uncertainties and risks inherent in these environments. To address the collaborative governance dilemma and enhance governance efficiency, this paper aims to construct an effective collaborative governance mechanism for a cross-domain digital innovation ecosystem and explore the optimal strategy choices of key governance stakeholders, including the government, digital platform enterprises, and other relevant parties. This research utilizes evolutionary game theory to construct a model comprising three governing entities: the government, digital platform enterprises, and stakeholders. It investigates the evolutionary dynamics of collaborative governance strategies among these entities and the factors that influence governance. Following this, a system dynamics methodology is employed for simulation analysis. The results reveal the following: (1) As the initial intentions of the governing entities evolve, governance decisions within the system tend to stabilize, characterized by a strategic combination of proactive regulation, active cooperative governance, and engaged participation. This equilibrium governance strategy significantly fosters the stable advancement of cross-domain digital innovation ecosystems. (2) The punitive measures enacted by the government and the internal incentive structures of the system positively influence the evolution of governance decisions towards collaborative governance. (3) The cost–benefit assessment of the primary governing entity, the digital platform enterprise, demonstrates a detrimental effect on the evolution of governance decisions towards collaborative governance. These findings are vital for refining the collaborative governance frameworks of cross-domain digital innovation ecosystems and for promoting the robust and stable progression of the system. Full article

In the digital economy, information asymmetry among individuals, data users, and governments limits the full realization of personal data value. To address this, “symmetry enhancement” strategies aim to reduce information gaps, enabling more balanced decision-making and facilitating efficient data flow. This study establishes a tripartite evolutionary game model based on personal data collection and development, conducts simulations using MATLAB R2024a, and proposes countermeasures based on equilibrium analysis and simulation results. The results highlight that individual participation is pivotal, influenced by perceived benefits, management costs, and privacy risks. Meanwhile, data users’ compliance hinges on economic incentives and regulatory burdens, with excessive costs potentially discouraging adherence. Governments must carefully weigh social benefits against regulatory expenditures. Based on these findings, this paper proposes the following recommendations: use personal data application scenarios as a guide, rely on the construction of personal trustworthy data spaces, explore and improve personal data revenue distribution mechanisms, strengthen the management of data users, and promote the maximization of personal data value through multi-party collaborative ecological incentives. Full article

This study adopts an evolutionary game theory framework and focuses on the strategic interaction between brands and KOLs. It examines how the two parties interact under conditions of uncertainty and risk, especially when the KOLs’ contract fulfillment capability is low, and how they adjust strategies to achieve sustainable collaboration. Different from previous studies, this paper not only examines objective parameters such as commission rate, brand value, return cost, and reputation risk, but also introduces behavioral factors, including risk preference, loss aversion, and the psychological perception of gains and losses. By modeling the decision-making process of KOLs and brands under uncertainty and risk, the key factors affecting the evolution of cooperation strategies are identified. The simulation results show that although the cooperation strategy (such as information disclosure and truthful promotion) can achieve stability under certain conditions, the system is highly sensitive to external factors (such as environmental uncertainty) and internal psychological factors (such as risk preference and loss sensitivity). This study provides practical suggestions for brands and KOLs to promote long-term cooperation, emphasizing the importance of incentive coordination, reputation risk management, commission structure optimization, and psychological perception regulation. These findings provide practical guidance for enhancing the sustainability of brand–KOL collaborations. Full article

Depression affects millions of people worldwide and remains a significant challenge in mental health care. Despite advances in pharmacological and psychotherapeutic treatments, there is a critical need for accessible and engaging tools that help individuals manage their mental health in real time. This paper presents a novel gamified, AI-driven system embedded within Internet of Things (IoT)-enabled environments to address this gap. The proposed platform combines micro-games, adaptive surveys, sensor data, and AI analytics to support personalized and context-aware depression monitoring and self-regulation. Unlike traditional static models, this system continuously tracks behavioral, cognitive, and environmental patterns. This data is then used to deliver timely, tailored interventions. One of its key strengths is a research-ready design that enables real-time simulation, algorithm testing, and hypothesis exploration without relying on large-scale human trials. This makes it easier to study cognitive and emotional trends and improve AI models efficiently. The system is grounded in metacognitive principles. It promotes user engagement and self-awareness through interactive feedback and reflection. Gamification improves the user experience without compromising clinical relevance. We present a unified framework, robust evaluation methods, and insights into scalable mental health solutions. Combining AI, IoT, and gamification, this platform offers a promising new approach for smart, responsive, and data-driven mental health support in modern living environments. Full article

Global climate change has altered precipitation patterns, leading to an increased frequency and intensity of extreme rainfall events and introducing greater uncertainty to flood risk in river basins. Traditional assessments often rely on static indicators and single-design scenarios, failing to reflect the dynamic evolution of floods under varying intensities. Additionally, oversimplified topographic representations compromise the accuracy of high-risk-zone identification, limiting the effectiveness of precision flood management. To address these limitations, this study constructs multi-return-period flood scenarios and applies a coupled 1D/2D hydrodynamic model to analyze the spatial evolution of flood hazards and extract refined hazard indicators. A multi-source weighting framework is proposed by integrating the triangular fuzzy analytic hierarchy process (TFAHP) and the entropy weight method–criteria importance through intercriteria correlation (EWM-CRITIC), with game-theoretic strategies employed to achieve optimal balance among different weighting sources. These are combined with the technique for order preference by similarity to an ideal solution (TOPSIS) to develop a continuous flood risk assessment model. The approach is applied to the Georges River Basin in Australia. The findings support data-driven flood risk management strategies that benefit policymakers, urban planners, and emergency services, while also empowering local communities to better prepare for and respond to flood risks. By promoting resilient, inclusive, and sustainable urban development, this research directly contributes to the achievement of United Nations Sustainable Development Goal 11 (Sustainable Cities and Communities). Full article

As human-driven vehicles (HDVs) and autonomous vehicles (AVs) coexist on the road, the asymmetry between their driving behaviors, decision-making processes, and responses to traffic scenarios introduces new safety challenges, especially in complex merging areas where frequent interactions occur. The existing traffic safety analysis of mixed traffic is mainly to analyze each safety index separately, lacking comprehensive evaluation. To investigate the safety risk more broadly, this study proposes a comprehensive safety evaluation framework for mixed traffic flows in merging areas from the perspective of traffic conflicts, emphasizing the asymmetry between HDVs and AVs. Firstly, an indicator of Emergency Lane Change Risk Frequency is introduced, considering the interaction characteristics of the merging area. A safety evaluation index system is established from lateral, longitudinal, temporal, and spatial dimensions. Then, indicator weights are determined using a modified game theory approach that combines the entropy weight method with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, ensuring a balanced integration of objective data and expert judgment. Subsequently, a cloud model enhanced with the fuzzy mean value method is then developed to evaluate comprehensive safety. Finally, a simulation experiment is designed to simulate traffic operation of different traffic scenarios under various traffic flow rates, AV penetration rates, and ramp flow ratios, and the traffic safety of each scenario is estimated. Moreover, the evaluation results are compared against those derived from the fuzzy comprehensive evaluation (FCE) method to verify the reliability of the comprehensive evaluation model. The findings indicate that safety levels deteriorate with increasing total flow rates and ramp flow ratios. Notably, as AV penetration rises from 20% to 100%, safety conditions improve significantly, especially under high-flow scenarios. However, at AV penetration rates below 20%, an increase of the AV penetration rate may worsen safety. Overall, the proposed integrated approach provides a more robust and accurate assessment of safety risks than single-factor evaluations, providing deeper insights into the asymmetries in traffic interactions and offering valuable insights for traffic management and AV deployment strategies. Full article

Nuclear power plants (NPPs) under construction are required to meet the stringent safety standards of operational facilities while also facing the heightened risk characteristics of construction projects. The combination of dense worker populations and generally low safety awareness presents serious challenges for ensuring construction safety. To address this, the present study proposes a BN–Game–SD multi-algorithm fusion model that systematically examines the evolution of behavioral risks from both group and individual perspectives. First, a behavioral indicator system was constructed using Bayesian Networks (BNs) to identify key risk factors. Then, a dynamic payoff matrix game model was introduced to analyze the incentive mechanisms between individuals and groups. Finally, a BN–Game–SD model was developed to capture the dynamic evolution of worker behaviors in NPP construction. Simulation results reveal that, under fixed probabilities of safety strategy selection, clear thresholds exist in group resistance to individual behavioral deviation and vice versa. Applied to a real NPP construction site, the model helped achieve a 10.39% reduction in safety violations. This study provides a theoretical foundation for promoting self-organized safety behavior evolution in nuclear enterprises and presents an innovative methodological framework for safety management in nuclear engineering. Full article

The Fourth Industrial Revolution has introduced “shared manufacturing” as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless interactions between parcel and warehouse agents. Shared Warehouse game simulations were conducted to evaluate the performance of LLM-driven agents in managing warehouse services, including direct and pooled offers, in a competitive environment. The simulation results show that the LLM-controlled agent clearly outperformed traditional random strategies in decentralized warehouse management. In particular, it achieved higher warehouse utilization rates, more efficient resource allocation, and improved profitability in various competitive scenarios. The LLM agent consistently ensured optimal warehouse allocation and strategically selected offers, reducing empty capacity and maximizing revenue. In addition, the integration of LLMs improves the robustness of decision-making under uncertainty by mitigating the impact of randomness in the environment and ensuring consistent, contextualized responses. This work represents a significant advance in the application of AI to decentralized systems. It provides insights into the complexity of shared manufacturing networks and paves the way for future research in distributed production systems. Full article

This paper focuses on the carbon emission problem of new energy vehicle (NEV) battery recycling, constructs a tripartite evolutionary game model of battery manufactures, new energy vehicle original equipment manufacturers (NEV OEMs) and certified recyclers, analyzes the stability of each party’s strategy selection and the relationship between the influence of the elements, and simulates to verify the validity of the conclusions, and arrives at the conditions for the occurrence of the lowest carbon emission stabilizing strategy combinations, and puts forward countermeasure suggestions accordingly, and analyzes the effects of the changes of the key parameters on the equilibrium results, and the study shows that (1) Carbon emission cost, battery decomposition cost, recycling channel construction cost and R&D cost are the main factors affecting the equilibrium results. (2) Under the carbon emission reduction policy, the battery manufacturer’s investment in low-carbon production can help other actors in the supply chain to reduce the negative impact of the policy so that they can reduce their costs. (3) The cooperative recycling model based on the recycling network constructed by vehicle manufacturers can maximize the interests of all parties in the supply chain. The findings of the study provide management insights for governments, battery manufacturers, NEV OEMs, and certified recyclers. Full article

This study employs evolutionary game theory to analyze the tripartite interaction among government regulators, media publishers, and self-media participants in emergency public opinion management. We establish an evolutionary game model incorporating strategic motivations and key influencing factors; then, we validate the model through systematic simulations. Key findings demonstrate the following: ① the system exhibits dual stable equilibria: regulated equilibrium and autonomous equilibrium. ② Sensitivity analysis identifies critical dynamics: ① self-media behavior is primarily driven by penalty avoidance ($g_3$) and losses ($w_2$); ② media participation hinges on revenue incentives ($m_2$) versus regulatory burdens ($k$); ③ government intervention efficacy diminishes on emergencies when resistance ($v_1$ + $v_3$) exceeds control benefits. The study reveals that effective governance requires the following: ① adaptive parameter tuning of punishment–reward mechanisms; ② dynamic coordination between information control and market incentives. This framework advances emergency management by quantifying how micro-level interactions shape macro-level opinion evolution, providing actionable insights for balancing stability and information freedom in digital governance. Full article