Game Perspective Research Articles

Online Car-Hailing Market Regulation Strategy in China: From the Perspective of Quadrilateral Evolutionary Games

Online Car-Hailing Market Regulation Strategy in China: From the Perspective of Quadrilateral Evolutionary Games

Incentive mechanism design for Federated Learning with Stackelberg game perspective in the industrial scenario

Federated Learning (FL) is a typical decentralized Machine Learning framework in which clients invest resources to train their local models without sharing their data and then transmit the model parameters to the server for parameter aggregation. Therefore, Incentive Mechanism Design (IMD) is a basic and important research direction in FL that stimulates clients to invest more resources in model training. In this paper, we designed the incentive mechanism as a Stackelberg game in which the server acts as the leader and the clients act as followers. We leveraged modified NSGA-II to find the Nash equilibrium. A real industrial scenario of pre-baked carbon anode quality prediction is applied to verify the high performance of the proposed method.

Distributed Formation Control Algorithms for QUAVs Based on Aggregative Games

In this article, the formation problems of multiple quadrotor unmanned aerial vehicles (QUAVs) are studied. In contrast to existing results, the problems are investigated from the perspective of aggregative games (AGs). An interesting discovery is that the desired formation can be constituted by the Nash equilibrium of a quadratic AG. To realize the desired formation for QUAVs, we design two distributed game-based formation algorithms. With the developed algorithms, the QUAVs do not need to measure the (relative) positions and (relative) velocities of neighboring QUAVs, which differs from existing algorithms. By constructing suitable Lyapunov functions, we rigorously analyze the two formation algorithms. The first formation algorithm converges exponentially to the desired formation for the QUAVs without parameter uncertainties. The other algorithm converges asymptotically to the desired formation, even if the QUAVs contain uncertain parameters. Finally, we provide illustrative examples.

How to promote the adoption of intelligent spray technology in farmers’ cooperatives? ——Based on the perspective of evolutionary game

In order to accelerate the development of smart agriculture and realize the green transformation of agriculture, the coupling of prospect theory and evolutionary game theory is introduced. Construct a two-party evolutionary game model for the adoption of intelligent spray technology in farmers' cooperatives, analyze the evolution of farmers' cooperatives and government strategy selection and its influencing factors according to the replication dynamic equation, and conduct numerical simulation experiments through Matlab software. The results show that the adoption of intelligent spray technology by farmers' cooperatives and the government's choice of subsidies are the two optimal stable states of the evolutionary system. The government's subsidy policy can effectively stimulate farmers' cooperatives to adopt intelligent spray technology, but when the government subsidy coefficient is greater than or equal to 70%, the decision-making evolution of the government and farmers' cooperatives is unstable. The increase in farmers' awareness of pesticide hazards, the scale of operation of farmers' cooperatives, the price of high-quality agricultural products, and the sensitivity of farmers' cooperatives to profits and losses contribute to the promotion of intelligent spray technology, and the damage probability of intelligent spray technology has a restraining effect.

Competition Anxiety in Combat Sports and the Importance of Mental Toughness.

Combat sports require participants to engage in potentially dangerous forms of contact-based competition. Pressure to succeed, coupled with the risk of severe injury can induce significant levels of anxiety, which if uncontrolled, can negatively impact performance and possibly promote unsporting conduct. The present study examined competitive anxiety levels of combat sports athletes and determined whether self-reported scores were associated with mental toughness and Sportspersonship attitudes. A cross-sectional survey design was used whereby participants (N = 194) completed a battery of questionnaires measuring competitive combat sport experiences, demographic details, Sportspersonship traits (compliance towards rules, respect for opponents, and game perspective), and competition anxiety (somatic, cognitive, and self-confidence; reported retrospectively). Results suggest that mentally tough athletes experience lower levels of cognitive and somatic anxiety, and higher self-confidence, prior to competitions. Findings also found that athletes endorsing more altruistic and respectful attitudes in sport (Sportspersonship) reported higher levels of competition anxiety. The findings demonstrate that mental toughness is allied to positive attributes and could potentially be operationalized to improve both the retention and performance of combat sports athletes. Thus, the authors advocate the use of mental toughness coaching interventions within combat sports.

Robust optimal energy management with dynamic price response: A non-cooperative multi-community aggregative game perspective

Energy management system gradually becomes an effective means in smart grid for paving the way to low carbon economy. However, information privacy, a large population of users, and the uncertainties of renewable energy sources and consumers’ behaviors have led to significant challenges for energy management system operation in terms of security and robustness. To conquer such difficulties, a bilevel energy management scheme for the day-ahead optimal scheduling of multi-community system combined with distributed energy sources is first proposed. Meanwhile, uncertainties induced by renewable energy sources generation and load consumption are handled through adjustable robust optimization. Secondly, a non-cooperative multi-community aggregative game is formulated to describe the interaction of numerous residential users which are coupled through the dynamic electricity price. Then, to seek the ɛ-Nash Equilibrium of the proposed game, an improved decentralized iterative algorithm based on Mean-Field control and consensus is presented which is benchmarked with centralized algorithm and a decentralized optimization method based on quadratic programming. Since each player in the proposed algorithm does not need to exchange information directly with other players, the information privacy is fully preserved. Also, the convergence of the proposed algorithm is provided. Case studies of a five-community system are conducted and the comparison results show that our proposed approach has better performance in terms of computational time and electricity cost.

Research on the synergy mechanism of multiple subjects in global climate governance: Based on the perspective of the evolutionary game of carbon neutral strategy between China, the US, and the EU

It is difficult to achieve ideal results only based on a single subject for governing global climate issues, and the synergy co-governance of multiple subjects may address this difficulty. Based on the different resource advantages and interests of China, the US, and the EU, the roles they assume and play in the governance of global climate issues are different. In this study, we explore the competition and interaction between the carbon neutral strategies of China, the US, and the EU under different scenarios through the dynamic evolution game model and use simulation analysis to simulate the impact of changes in the initial values of the main parameters on the evolution trend of the three-party game strategy under a variety of scenarios, in order to explore the dynamic evolution path of the behavior of the three parties in the game and the stabilization strategy. The result of this study suggests that: (1) The strategic choices of different participants in global climate governance interact with each other. (2) The ideal strategy combination may be achieved when the carbon neutral strategy game is carried out between China, the US, and the EU under certain conditions. (3) If the initial willingness to cooperate changes, with the Evolutionary Stable Strategy (ESS) unchanged, only the convergence rate of the evolutionary game subject's strategy choices will be affected. (4) The result of the sensitivity analysis suggests that certain policy conflicts exist in the evolution process of carbon neutrality strategies in China, the US, and the EU. Besides, the evolution path will deviate from the ideal state with the change in the benefits of different entities. The conclusion of this study can provide a valuable reference for China, the US, and the EU to divide responsibilities and form a consensus in the synergy governance mechanism, such that the effectiveness of global climate governance can be increased.

EV grid-connected control and economic benefit analysis based on V2G technology

With the large-scale use of EV around the world, it has become a popular research direction to integrate EV into the power grid to achieve the purpose of peaking and valley filling for the power grid. With V2G technology as the core, this paper firstly analyzes the V2G grid-connected control technology, namely active power and reactive power control and clustering control at the theoretical level. From the perspective of cooperative game, it elaborates the commercial operation mode of V2G after grid-connected. Finally it puts forward relevant assumptions on the future development trend of V2G based on the current development status of V2G technology.

Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective

Cross-silo federated learning (FL) is a distributed learning approach where clients of the same interest train a global model cooperatively while keeping their local data private. While there has been some work on incentivizing clients to join FL, the analysis of clients long-term selfish participation behaviors in cross-silo FL remains largely unexplored. In this paper, we model clients long-term selfish participation behaviors as an infinitely repeated game, with the stage game being a selfish participation game in one cross-silo FL process (SPFL). For the stage game SPFL, we derive the unique Nash equilibrium (NE), and propose a distributed algorithm for each client to calculate its equilibrium participation strategy. We show that at the NE, some clients may choose to be free riders. The existence of free riders has a detrimental effect on sustaining other clients long-term participation. For the long-term interactions among clients, we derive the optimal SPNE which minimizes the number of free riders while maximizing the amount of local data for model training. Simulation results show that our derived optimal SPNE can effectively reduce the number of free riders by up to 99.3% and increase the amount of local data for model training by up to 82.3%.

게임과 게이미피케션의 교육철학적 속성에 대한 융합적 고찰

This study started from the fact that I, especially as a researcher at a university, saw that students play games during breaks and talk while they are with their friends, and that games will become more and more common in our lives in the future, and that trying to understand their lives will be very helpful in finding educational meaning. By reinterpreting major philosophical and pedagogical concepts from the perspective of games and gamification, and developing a feasible lesson model by integrating each concept into the lesson, the educational properties of these concepts can be found. It can also be said to be a kind of study of game literacy. Therefore, focusing on gamification, which is one of the main features of the game, we will present the reasons for the educational philosophical attributes and conduct research that presents a multifaceted perspective that expands thinking from a convergent and humanities perspective.

Dose blockchain-based agri-food supply chain guarantee the initial information authenticity? An evolutionary game perspective.

Guarantee the initial information of the agri-food supply chain (AFSC) authenticity based on the blockchain is a complex problem. This paper develops an evolutionary game model of AFSC participants based on the blockchain and discusses the impacts of the key parameters on the dynamic evolution process of participants. To verify the theoretical results, simulation experiments and sensitivity analysis were conducted through Matlab 2022b. The study results show that: (1) Guaranteeing the initial information authenticity could become the common belief of all AFSC participants, with the scientific design of parameters; (2) Higher reward and synergistic effect, lower information cost and risk contribute to improving the probability of initial true information sharing. (3) when the default penalty is too severe, the enterprise will evolve into not sharing the initial true information. Finally, this study could provide some suggestions and countermeasures for the leading enterprise in the agricultural supply chain and local governments to guarantee initial information authenticity in China. That is the way to realize the sustainability of AFSC in the long run.

An Evolutionary Game Theory-Based Method to Mitigate Block Withholding Attack in Blockchain System

Consensus algorithms are the essential components of blockchain systems. They guarantee the blockchain’s fault tolerance and security. The Proof of Work (PoW) consensus algorithm is one of the most widely used consensus algorithms in blockchain systems, using computational puzzles to enable mining pools to compete for block rewards. However, this excessive competition for computational power will bring security threats to blockchain systems. A block withholding (BWH) attack is one of the most critical security threats blockchain systems face. A BWH attack obtains the reward of illegal block extraction by replacing full proof with partial mining proof. However, the current research on the BWH game could be more extensive, considering the problem from the perspective of a static game, and it needs an optimal strategy that dynamically reflects the mining pool for multiple games. Therefore, to solve the above problems, this paper uses the method of the evolutionary game to design a time-varying dynamic game model through the degree of system supervision and punishment. Based on establishing the game model, we use the method of replicating dynamic equations to analyze and find the optimal strategy for mining pool profits under different BWH attacks. The experimental results demonstrate that the mining pools will choose honest mining for the best profit over time under severe punishment and high supervision. On the contrary, if the blockchain system is supervised with a low penalty, the mining pools will eventually choose to launch BWH attacks against each other to obtain the optimal mining reward. These experimental results also prove the validity and correctness of our model and solution.

Protecting Innovation Sustainability: R&D Manipulation and Effective Regulation Based on a Two-Scenario Evolutionary Game Perspective

Enterprise innovation is the core content of national innovation and an important issue for sustainable development. Therefore, this paper focuses on the phenomenon of R&D manipulation in the declaration of high-tech enterprises, constructs a two-scenario two-party evolutionary game model in which central governments do or do not join in the supervision of local governments and the declaration enterprises and simulates and analyzes the direction of each key variable on the strategy selection of the game subject and degree of impact. The study finds that reducing tax rates is beneficial to avoid enterprise R&D manipulation, while innovation performance and regulatory costs drive local governments to reduce regulation. Further analysis shows that central government’s participation in supervision reduces enterprise R&D manipulation and strengthens local government regulation. The research results could promote the standardization of enterprise innovation behavior and mitigate local government slack, thereby fostering sustainable innovation and development.

Promoting Construction Labor Professionalization: An Evolutionary Game Perspective

A shortage of skilled laborers has constrained the new development path called Construction 4.0, which is proposed to improve the construction industry with advanced technologies. It should be noted that the mismatch between labor skills and technological progress will harm the sustainable development of the construction sector. In China, a three-tier management structure consisting of contractors, labor subcontractors, and laborers is widespread in the labor market. Considering the relationship between laborers and construction firms, promoting construction labor professionalization depends on the cooperation of general contractors and labor subcontractors. Previous studies have focused on training techniques and methods, but have neglected to investigate whether stakeholders are willing to cooperate in training labor. Considering that the standard workforce is more likely to receive human resource investment, this paper aims to analyze the strategic choice of related stakeholders in cultivating the standard workforce. Since evolutionary game theory has proven to be an effective method to study the symbiotic evolution between stakeholders, this paper will apply it to develop an evolutionary game model and conduct analysis. Moreover, this research examines how government incentive policy affects the dynamic evolution process of cultivating a standard workforce. The results suggest that without government incentives, general contractors and labor subcontractors tend to choose a contingent workforce. When the government subsidy reaches a certain level, general contractors and labor subcontractors are willing to cooperate in cultivating a standard workforce. Additionally, it is worth noting that compensation for labor subcontractors is relatively more effective than that for general contractors in promoting construction labor professionalization. Furthermore, some suggestions and countermeasures are proposed to help to cultivate a standard workforce in China.

Research on Technology Governance of ChatGPT from the Perspective of Tripartite Evolutionary Game

With science and technology such as ChatGPT integrating into society, the standardization of technology governance becomes the key to consolidating government power, promoting enterprise development, and enhancing people’s livelihood and well-being. Based on a tripartite game model of government, enterprises, and consumers, this paper analyzes their interaction mechanism and evolutionary stability strategy through the dynamic evolutionary game. The results show that there is a stable strategy up against the high risk, that is, enterprises will “use” ChatGPT, consumers will “participate in” ChatGPT, and the government will “advocate” ChatGPT. When the risk is low, two stable strategies exist, in which enterprises still “use” ChatGPT, while the government and consumers have different choices under different strategies. The research results can be used for reference to standardize ChatGPT and promote technical governance.

Allocation strategy of medical supplies during a public health emergency: a tripartite evolutionary game perspective

Ensuring the rational and orderly circulation of medical supplies during a public health emergency is crucial to quickly containing the further spread of the epidemic and restoring the order of rescue and treatment. However, due to the shortage of medical supplies, there are challenges to rationalizing the allocation of critical medical supplies among multiple parties with conflicting interests. In this paper, a tripartite evolutionary game model is constructed to study the allocation of medical supplies in the rescue environment of public health emergencies under conditions of incomplete information. The game’s players include Government-owned Nonprofit Organizations (GNPOs), hospitals, and the government. By analyzing the equilibrium of the tripartite evolutionary game, this paper makes an in-depth study on the optimal allocation strategy of medical supplies. The findings indicate that: (1) the hospital should reasonably increase its willingness to accept the allocation plan of medical supplies, which can help medical supplies allocate more scientifically. (2) The government should design a reasonable reward and punishment mechanism to ensure the rational and orderly circulation of medical supplies, which can reduce the interference of GNPOs and hospitals in the allocation process of medical supplies. (3) Higher authorities should strengthen the supervision of the government and the accountability for loose supervision. The findings of this research can guide the government in promoting better circulation of medical supplies during public health emergencies by formulating more reasonable allocation schemes of emergency medical supplies, as well as incentives and penalties. At the same time, for GNPOs with limited emergency medical supplies, the equal allocation of emergency supplies is not the optimal solution to improve the efficiency of emergency relief, and it is simpler to achieve the goal of maximizing social benefits by allocating limited emergency resources to the demand points that match the degree of urgency. For example, in Corona Virus Disease 2019, emergency medical supplies should be prioritized for allocation to government-designated fever hospitals that are have a greater need for medical supplies and greater treatment capacity.

Government regulation strategy, leading firms' innovation strategy, and following firms imitation strategy: An analysis based on evolutionary game theory.

In the innovation ecosystem, the knowledge-based game behavior of each subject not only pertains to its own survival and development but also affects evolution of the innovation ecosystem. The present study investigates the choice of government's regulation strategy, leading firms' innovation protection strategy and following firms' imitation strategy from the perspective of group evolutionary game. Based on the cost-benefit perspective, an asymmetric tripartite evolutionary game model and a simulation model are constructed to analyze the strategies and stability of the evolutionary equilibrium of each subject. We focus mainly on the protection intensity of innovation achievements by leading enterprises and the difficulty of imitation and substitution by following enterprises. The cost of patent operation and maintenance, government subsidies, and the relative difficulty of technology substitution and imitation were identified as the key factors affecting the evolutionary equilibrium of the system. Based on different scenarios resulting from the aforementioned factors, four equilibrium states are observed in the system, namely {no government regulation, technology secrecy, substitution}, {no government regulation, technology secrecy, imitation}, {no government regulation, patent application, imitation}, and {government regulation, patent application, imitation}. Finally, the study suggests corresponding recommendations for the three parties, which can help governments as well as the leading and following firms to choose appropriate behavioral strategies. At the same time, this study offers positive insights to participants in the global innovation ecosystem.

Zealous cooperation does not always promote cooperation in public goods games.

There is a conventional belief that prosocial behaviors cannot arise through selfish human nature, because defection always exploits cooperation to achieve a higher payoff at an individual level. Unyieldingly, some people hope to move society to cooperation through their zealous cooperation, regardless of payoffs. From the perspective of spatial evolutionary games, however, such zealous behavior is unnecessary because cooperation can emerge from selfish human nature by aggregating in evolution. Yet, to what extent can zealous cooperation induce others to cooperate? We assume a fraction of zealous agents in spatial public goods games who always cooperate. The results show that a moderate proportion of these zealous cooperators can diminish the cooperation level in the system, and cooperation is only promoted when zealots are many. Regarding spatial behaviors, the areas of zealous cooperation in a medium density can prevent evolutionary cooperation from passing through and aggregating. The phenomenon of zealous cooperation impeding cooperation becomes more pronounced when agents become less random and more selfish. This is because dotted zealous cooperation provides significant payoffs to neighboring defection, making them more solid in fitness. In this way, we also find that when zealous cooperators have low productivity, the neighbors receive fewer benefits by exploitation, thus allowing cooperation to spread. We also study replicator dynamics in unstructured populations where zealous cooperation always promotes cooperation, agreeing that zealous cooperation hindering cooperation is a spatial effect.

The mystery of choice of equity incentive model: based on the game theory analysis between shareholders and top executives

PurposeBased on the background of the restricted stock has gradually replaced stock option and has become the mainstream equity incentive model in China, this paper aims to investigate the which factors affect the choice of equity incentive model, and the impacts of equity incentive model.Design/methodology/approachThe theoretical analysis is based on the game theory between shareholders and top executives. The empirical analysis is based on the detailed data of equity incentives in China’s listed companies from 2006 to 2017; the logit method and least square method are implemented to estimate the regression coefficients and Black–Scholes options pricing model to estimate the value of restricted stock/option granted to the CEO.FindingsThis paper documents that enterprises with serious agency problems, high investment risks, high stock price synchronicity and great executive power are significantly and positively related to restricted stock. The main empirical findings still hold after several robust tests. In addition, restricted stock can significantly improve corporate performance when the performance evaluation index is strict and the validity period is long, while for the sample group with loose performance index and short validity period, restricted stock significantly reduces corporate performance.Originality/valueThis paper analyzes the “black box” of equity incentive model selection from the stakeholders’ game perspective by constructing a game theory model to investigate the reasons for the choice of equity incentive model in various situations, which enriches the research in this field. Moreover, this paper finds that restricted stock has both incentive and welfare characteristics, and the rationality of performance appraisal goals is the key factor leading to the difference in incentive effects. Overall, the research indicates that only well-designed equity incentive plans can improve corporate performance, which contributes to regulators and practitioners to form a rational understanding of restricted stock model and provides a reference for their decision-making.

Optimal decision for the satellite navigation system under navigation countermeasures based on spoofing effect evaluation results

AbstractThis paper realizes the evaluation and optimal decision of the spoofing effect on the global navigation satellite system (GNSS) under navigation countermeasures based on the game perspective, to overcome the limitations of current relevant research findings and maintain the secure application of the GNSS and maximize its defensive benefit during countering. An evaluation system based on the perspective of the game between spoofing strategies and defense measures is created to build the benefit matrix, which can solve the limitation in building a benefit matrix (the vital element in a game) that conforms to the confrontation‐oriented GNSS spoofing properties. Based on the built benefit matrix, a method integrating the grey expert‐based system with the hybrid evolutionary method is developed to realize quantifying the matrix and further make the optimal decision stably, which can solve the uncertainties of qualitative indices during quantifying and no global optimal solution for benefit equations deduced from the quantified matrix. Finally, based on various judgment principles, simulation and testing results verify the effectiveness of the obtained results and the superiority of the proposed method by comparing the results solved by the method in this paper with the results obtained by other decision‐making processing methods.