Evolutionary Game Theory Research Articles

Research on Stakeholder Behavior Strategy in Green PPP Project Financing

In the context of green development, while promoting green industry and sustainable development, green PPP faces the challenge of financing difficulties. Based on the evolutionary game theory of bounded rationality and taking government, social capital and financial institutions as the game players, this paper constructs an evolutionary game model for the behavioral decision-making of stakeholders in green PPP project financing. Using MATLAB software, we simulate and analyze the key factors affecting the behavioral strategies of each game player. The results indicate that: (1) The additional income obtained from participation in green PPP projects is a key factor affecting the decision-making of social capital and financial institutions, so it is necessary to improve the reasonable return mechanism of green PPP projects and attach importance to the construction of reputation mechanism; (2) Government subsidies can effectively encourage social capital and financial institutions to participate in green PPP projects, but the amount of subsidies should be within a reasonable range; (3) The government's reward and punishment intensity is a significant factor affecting the decision-making of social capital behavior. To this end, it is imperative for the government to enhance the performance evaluation system for green PPP projects and establish a balanced reward and punishment mechanism. This paper puts forward some suggestions on the decision-making of stakeholders in the financing process of green PPP projects, and provides references for the policy formulation of encouraging social capital and financial institutions to participate in green PPP projects.

The Impact of the Industrial Internet on the Innovation and Development Level of China’s Manufacturing Industry: Under the Perspective of Government Incentives

In the era of the digital economy, it has become an inevitable trend for manufacturing enterprises to establish industrial Internet platforms toward achieving transformation and innovative development. However, the current development model of industrial Internet platforms is still imperfect, wherein the application scenario is complex, the investment cost is too high, the return-on-investment cycle is too long, and other factors have hindered the willingness of manufacturing enterprises to employ cloud services and capital investment. For this reason, governments have introduced a series of relevant incentive policies to promote the development of industrial Internet platforms and the transformation and upgrading of manufacturing enterprises. Considering the role of government incentives, this study first constructs an evolutionary game model with local governments, manufacturing enterprises, and industrial Internet platforms as the main players. Then, the dynamic change process of each game player’s strategy choice and the stable state of the system evolution under multiple scenarios are analyzed, and the validity of the conclusions is verified through a numerical simulation analysis. Finally, the statistical data of 28 provinces in China from 2018 to 2020 are used to conduct an empirical test to explore the impact of the industrial Internet on the transformation and innovation development of the manufacturing industry and the role of government incentives. The results show that the development of the industrial Internet has a significant role in promoting the innovation and development of the manufacturing industry; government incentives can promote the innovation and development of the industrial Internet and manufacturing industry, but incentives should not be too generous; and the impact of developing the industrial Internet on the level of innovation input/output of the manufacturing industry shows obvious regional differences. This study takes the local government as an independent game participant into consideration, which enriches the research field of combining evolutionary game theory with the transformation and innovative development of the manufacturing industry. In addition, this study provides theoretical guidance and practical references for the government to formulate incentive policies to promote the development of industrial Internet platforms and for manufacturing enterprises to utilize these platforms to carry out innovation and perform upgrades.

Unveiling Agricultural Shifts through Stakeholder Interactions and Strategic Dynamics in Green Rice Production

This study explores the strategic interactions among the government, growers, and the public within the context of green rice production, employing an evolutionary game theory framework. Recognizing the intricate dynamics of agricultural sustainability, we construct a three-party evolutionary game model to investigate the strategic decision-making processes and stability conditions of each stakeholder. The model assesses how various strategies evolve under the influence of economic incentives, regulatory measures, and public engagement. Through analytical and numerical methods, including stability analysis and MATLAB 2020b simulations, we identify the Evolutionarily Stable Strategies (ESS) that signify sustainable practices in green rice production. The results reveal that as government incentives for green production increase and fines for non-green practices are enforced, the likelihood of growers adopting sustainable practices significantly rises. Furthermore, the strategic enhancement of economic incentives and reputational factors not only bolsters governmental regulatory commitment but also reduces the necessity for public supervision, demonstrating a systemic shift towards self-regulation and market-driven sustainability. The simulations demonstrate the system’s evolution towards a stable state where governmental regulation is fully enforced, growers adopt green production universally, and public supervision becomes redundant. The findings underscore the importance of designing policy interventions that harmonize economic and environmental objectives, suggesting that well-structured incentives and regulatory measures can catalyze the transition towards sustainable agricultural practices. Additionally, our study highlights the pivotal role of establishing effective incentive structures that ensure the economic benefits of green production outweigh the costs, facilitating an autonomous regulatory system. This study contributes to the understanding of how strategic interactions shaped by policy and market forces can foster agricultural sustainability.

Both eyes open: Vigilant Incentives help auditors improve AI safety

Auditors can play a vital role in ensuring that tech companies develop and deploy AI systems safely, taking into account not just immediate, but also systemic harms that may arise from the use of future AI capabilities. However, to support auditors in evaluating the capabilities and consequences of cutting-edge AI systems, governments may need to encourage a range of potential auditors to invest in new auditing tools and approaches. We use evolutionary game theory to model scenarios where the government wishes to incentivise auditing but cannot discriminate between high and low-quality auditing. We warn that it is alarmingly easy to stumble on ‘Adversarial Incentives’, which prevent a sustainable market for auditing AI systems from forming. Adversarial Incentives mainly reward auditors for catching unsafe behaviour. If AI companies learn to tailor their behaviour to the quality of audits, the lack of opportunities to catch unsafe behaviour will discourage auditors from innovating. Instead, we recommend that governments always reward auditors, except when they find evidence that those auditors failed to detect unsafe behaviour they should have. These ‘Vigilant Incentives’ could encourage auditors to find innovative ways to evaluate cutting-edge AI systems. Overall, our analysis provides useful insights for the design and implementation of efficient incentive strategies for encouraging a robust auditing ecosystem.

Energy-efficiency and safety-driven multidimensional evolutionary game for AGVs transportation at automated container terminals

The velocity, acceleration, and number (VAN11VAN: The velocity, acceleration, and number.) of AGVs are crucial factors that impact the efficiency, energy consumption, and safety (EES22EES: The efficiency, energy consumption, and safety.3 ACT: Automated container terminal.) of horizontal transport in automated container terminals (ACT3s). This study utilizes multidimensional and evolutionary game theories to address these factors. Specifically, it considers the probability of AGV fault, treats the resulting faults as input costs, and regards the managers' decision preferences as benefits. According to different strategy combinations, a multidimensional evolutionary game based AGVs transportation model is established to analyze their effects on EES. Stability analysis and numerical simulation show that various strategy choices influence the evolutionary trends in EES of horizontal transportation. The findings demonstrate that reasonably setting the VAN of AGVs in the right range for a certain number of tasks can maximize the benefits of EES. The increases in efficiency and energy consumption benefits would accelerate the modification of velocity and acceleration strategies. The rise in safety benefit would also enhance the probability of adjusting AGV number. As the fault probability increases, the benefits for EES decreases. This study provides management insights into scheduling optimization and strategic adjustment for ACTs.

Evolutionary dynamic of division of labor games

The ubiquity of labor division within diverse social collectives is a topic well captured by evolutionary game theory. This work offers an integrative review of the evolutionary dynamics underpinning such division of labor from a tripartite standpoint —commencing with a theoretical exposition on numerous archetypes of labor division games. Subsequently, we delineate a suite of control strategies formulated to not only realize but also sustain the phenomenon of division of labor. This is followed by an elucidation of practical implementations pertaining to the allocation of tasks and labor division, grounded in the principles of game theory. We culminate with the proposition of prospective avenues and insightful trajectories for future investigations, cultivating a frontier for the continued exploration within this field.

Mixed strategy approach destabilizes cooperation in finite populations with clustering coefficient.

Evolutionary game theory, encompassing discrete, continuous, and mixed strategies, is pivotal for understanding cooperation dynamics. Discrete strategies involve deterministic actions with a fixed probability of one, whereas continuous strategies employ intermediate probabilities to convey the extent of cooperation and emphasize expected payoffs. Mixed strategies, though akin to continuous ones, calculate immediate payoffs based on the action chosen at a given moment within intermediate probabilities. Although previous research has highlighted the distinct impacts of these strategic approaches on fostering cooperation, the reasons behind the differing levels of cooperation among these approaches have remained somewhat unclear. This study explores how these strategic approaches influence cooperation in the context of the prisoner's dilemma game, particularly in networked populations with varying clustering coefficients. Our research goes beyond existing studies by revealing that the differences in cooperation levels between these strategic approaches are not confined to finite populations; they also depend on the clustering coefficients of these populations. In populations with nonzero clustering coefficients, we observed varying degrees of stable cooperation for each strategic approach across multiple simulations, with mixed strategies showing the most variability, followed by continuous and discrete strategies. However, this variability in cooperation evolution decreased in populations with a clustering coefficient of zero, narrowing the differences in cooperation levels among the strategies. These findings suggest that in more realistic settings, the robustness of cooperation systems may be compromised, as the evolution of cooperation through mixed and continuous strategies introduces a degree of unpredictability.

Imitation dynamics and the replicator equation

Evolutionary game theory has impacted many fields of research by providing a mathematical framework for studying the evolution and maintenance of social and moral behaviors. This success is owed in large part to the demonstration that the central equation of this theory —the replicator equation— is the deterministic limit of a stochastic imitation (social learning) dynamics. Here we offer an alternative elementary proof of this result, which holds for the scenario where players compare their instantaneous (not average) payoffs to decide whether to maintain or change their strategies, and only more successful individuals can be imitated.

An evolutionary game to describe doping dynamics

The objective of this research is to delve deeply into the complex dynamics of doping in various sports disciplines, seeking to uncover the underlying mechanisms that contribute to its proliferation. Our approach involves employing ecological and biological models in conjunction with evolutionary game theory, an original aspect of this study. These models will be instrumental in simulating and understanding the intricate interactions and competitive strategies that drive athletes toward doping.The study seeks to propose effective and efficient policies and measures that can be implemented to combat the spread of doping and to identify innovative approaches that could be more effective at deterring athletes from doping, thereby ensuring a fair and level playing field in competitive sports.

Deterministic theory of evolutionary games on temporal networks.

Recent empirical studies have revealed that social interactions among agents in realistic networks merely exist intermittently and occur in a particular sequential order. However, it remains unexplored how to theoretically describe evolutionary dynamics of multiple strategies on temporal networks. Herein, we develop a deterministic theory for studying evolutionary dynamics of any [Formula: see text] pairwise games in structured populations where individuals are connected and organized by temporally activated edges. In the limit of weak selection, we derive replicator-like equations with a transformed payoff matrix characterizing how the mean frequency of each strategy varies over time, and then obtain critical conditions for any strategy to be evolutionarily stable on temporal networks. Interestingly, the re-scaled payoff matrix is a linear combination of the original payoff matrix with an additional one describing local competitions between any pair of different strategies, whose weights are solely determined by network topology and selection intensity. As a particular example, we apply the deterministic theory to analysing the impacts of temporal networks in the mini-ultimatum game, and find that temporally networked population structures result in the emergence of fairness. Our work offers theoretical insights into the subtle effects of network temporality on evolutionary game dynamics.

Competition between self- and other-regarding preferences in resolving social dilemmas

Evolutionary game theory assumes that individuals maximize their benefits when choosing strategies. However, an alternative perspective proposes that individuals seek to maximize the benefits of others. To explore the relationship between these perspectives, we develop a model where self- and other-regarding preferences compete in public goods games. We find that other-regarding preferences are more effective in promoting cooperation, even when self-regarding preferences are more productive. Cooperators with different preferences can coexist in a new phase where two classic solutions invade each other, resulting in a dynamical equilibrium. As a consequence, a lower productivity of self-regarding cooperation can provide a higher cooperation level. Our results, which are also valid in a well-mixed population, may explain why other-regarding preferences could be a viable and frequently observed attitude in human society.

The impact of penalties, incentives, and monitoring costs on the stakeholders’ decision-making behaviors in non-compliance drone operations

As an automated assistive tool, drones have revolutionized industrial activities and brought numerous potential benefits to society. However, irresponsible drone users often disregard compliance with regulations, leading to new challenges in drone usage. Although governments have implemented punishment and incentive mechanisms to prevent non-compliant drone operations, the extent to which they can effectively deter such activities remains unclear. To address this gap, the study employed evolutionary game theory to assess the impacts of penalties for non-compliance, incentives for public monitoring, and monitoring costs for the government on the multiple stakeholders' decision-making behaviors (SDBs). The study also used the Chinese construction market data to simulate how penalties, incentives, and monitoring costs influence SDBs. The numerical simulations reveal that penalties and incentives could reduce drone users' non-compliant operations, but this effect is useful only if the penalties and incentives exceed a certain value. In China, drone users' non-compliant operations can be controlled when penalties for drone users exceed 12,000 yuan, and incentives for the public's monitoring exceed 170 yuan/day. The current Chinese government's penalties that were administered for non-compliant drone operations have not achieved a deterrent effect, but the incentive is feasible. These findings provide a fresh insight into the decision-making behaviors of stakeholders in non-compliant drone operations. Additionally, the tripartite evolutionary game model developed in this study can assist other countries in determining optimal values for penalties, incentives, and monitoring costs to mitigate non-compliant drone operations effectively.

An Underwater Source Location Privacy Protection Scheme Based on Game Theory in a Multi-Attacker Cooperation Scenario.

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP.

Evolutionary Game Analysis of Green Supply Chain Management Diffusion under Environmental Regulation

The continuous deterioration of the ecological environment and the increasing scarcity of resources have posed a serious challenge to the survival and development of human beings, and the implementation of green supply chain management (GSCM) in this context is an effective means to ensure the sustainable development of society and the economy. In order to seek the optimal strategy of evolutionary game in the implementation of green supply chain management and explore the influence of environmental regulation intensity and public preference degree on the evolution process of green supply chain management diffusion development, this paper takes the study of green supply chain management diffusion as the core innovation point, and under the premise of environmental regulation, selects the government, the core enterprise, and the public as the participating bodies of green supply chain management diffusion, and uses the theory of evolutionary game to construct a diffusion model of green supply chain management. Using evolutionary game theory to construct a diffusion model, and with the help of MATLAB and other mathematical tools for numerical simulation analysis, we discuss the diffusion of the green supply chain and derive the optimal combination strategy. The results of the study show that: (1) there are four evolutionary stable states in the process of green supply chain management diffusion: preliminary diffusion, extinction, semi-diffusion, and full diffusion; (2) it will be beneficial for the government to promote the evolutionary diffusion of green supply chain management by implementing a higher intensity of pollution tax policy while implementing green supply chain incentive strategies; (3) the government, while implementing environmental regulation policies, should also pay attention to the guidance of the public’s awareness of environmental friendliness and greenness, and focus on the role of the comprehensive strategy selection of the three parties of the game in reaching the optimal state. The conclusions of the study provide theoretical guidance and decision support for the implementation and diffusion of green supply chain management under environmental regulation.

Government subsidy and benefit distribution mechanisms for transportation PPP projects: An evolutionary game perspective

Public-private partnerships (PPP), as an important model for collaboration between the public and private sectors, is an urgent and critical topic due to the serious financial losses of governments involved in transportation PPP projects in recent years. Current research focuses on the government subsidy model, in which the effective implementation of government subsidies relies on the design of incentives for stakeholder behavior. Although the positive externalities are strong, they are prone to the problem of “free riding,” which leads to low project performance and challenges in compensating for the government's financial losses. Therefore, this study proposes a novel dynamic subsidy mechanism that can be adjusted based on actual changes in transportation demand and that is linked to project performance. We use evolutionary game theory to construct a two-party evolutionary game model of the government and social capital, focusing on the stability and influencing factors of these interactions. Our research unveils that reaching specific thresholds in both the incentive coefficient and benefit distribution ratio induces an “positive management–negative management” shift in the behavior of involved parties, leading to enhanced project outcomes. Notably, fluctuations in operational quality substantially enhance the efficiency of the active management of private sector, with no discernible impact on the subsidy efficiency of the government. Therefore, our study provides a theoretical framework for improving the revenue allocation and government subsidy mechanism, which has theoretical and practical implications for enhancing the effect of government incentives and improving the quality of operational social capital.

Evolutionary game based interest forwarding in information centric vehicular networks

In Information Centric Network (ICN) a client generates an interest packet when it is in need of a content. The interest packet carries the content name and propagates through the network till it reaches an appropriate cached content store or the producer of the content. Retrieval of the searched content is faster if the interest packet is forwarded in right direction towards a probable content location. An efficient and optimal interest forwarding technique is the key requirement for the success of any ICN implementation. This paper describes an interest forwarding approach for information centric vehicular networks based on evolutionary game theory. It uses the Public Goods Gaming (PGG) strategy to forward interest packets so that the content can be located at the earliest. The interest packets are modeled as goods and various participating entities are categorized either as cooperator or defector. The purpose of the game is to deliver the goods (the interest packet) to the rightful content store. Cooperator tries to optimally deliver the interest packets and hence they are rewarded with certain incentives. However, defectors are not rewarded as they do not participate in the game. At the beginning, a player selects a strategy and confined to it till the completion of the game. During interest forwarding, a node selects its neighbor(s) having higher credits as next level forwarder. The scheme is analyzed mathematically to establish various claims made in this paper. The mathematically established claims are also validated through simulation in ns-3 based ndnSIM-2.0. The model is compared with four other existing ICN forwarding approaches and simulation results depict that the new algorithm performs better.

Information dissemination behavior of self-media in emergency: Considering the impact of information synergistic-hedging effect on public utility

In the context of emergency, besides handling the incident itself, controlling public opinion is also crucial. Self-media, as a crucial channel for the public to access information, controlling its dissemination behavior is key to public opinion management. Given the complexity of self-media information dissemination, we propose an evolutionary model for social media information dissemination behavior based on complex network theory and evolutionary game theory. Firstly, aiming at the synergistic and hedging effects of information dissemination, we use the utility function to depict the public's information demand. Next, based on the public's information needs, evolutionary game theory is introduced to analyze the information dissemination behavior between two self-media entities. Lastly, we map the dissemination behavior of self-media onto network space, analyzing the information propagation behavior of self-media within a complex network. The study shows that the probability of self-media disseminating positive information increases with the rise of the positive information synergistic effect and decreases with the increase of the negative information synergistic effect. When positive and negative information coexists, the dissemination behavior of self-media depends on the influence size of positive and negative information. With the improvement of government credibility, the probability of self-media disseminating positive information decreases slightly at first, and then increases rapidly. When the government's penalties increase, the probability of self-media disseminating positive information quickly rises. The changes in noise intensity have an uncertain impact on the positive information dissemination behavior of self-media. Compared with other factors, self-media is more sensitive to government credibility and punishment.

Research on Credit Regulation Mechanism of E-commerce Platform Based on Evolutionary Game Theory

Research on Credit Regulation Mechanism of E-commerce Platform Based on Evolutionary Game Theory

Newborn vaccination game model with potentially periodic behavior

We explore the complex dynamics of epidemics, considering the significant effects of disease transmission, newborn individuals, and mortality rate. Employing an evolutionary game theory approach, we model the vaccination aspect for newborns, incorporating potential periodic behavior. The goal is to create an engaging simulation that underscores the criticality of timely infant vaccinations. By introducing periodic elements, the game adds complexity, fostering player participation and awareness of vaccination schedules. In contrast to traditional intervention game models focused on adult vaccine behavior, our approach recognizes the unique challenges posed by newborn vaccination. We address the limitations of existing models by developing a generalized compartmental model, accommodating infection duration distribution, and accurately capturing both periodic and non-periodic disease trajectories. The results show that higher efficacy in a newborn vaccine program can potentially eradicate the disease, but higher effectiveness and cost may deter vaccination. Increased motivation cost boosts vaccination rates without impacting epidemic size, whereas lower costs stabilize vaccination rates. Lower vaccine costs call for increased government investment to counter hesitancy. However, reduced efficacy and lower motivation lead to higher Vaccine Fear Scores, discouraging participation. Our work also offers a unique perspective on the dilemma of newborn vaccination, providing solutions with closed-form expressions that may exhibit periodic or non-periodic behavior. This approach facilitates disease modelers in exploring the potential periodicity of newborn vaccination programs, aiding efforts to prevent recurrent outbreaks.

Evolution Process of Urban Industrial Land Redevelopment in China: A Perspective of Original Land Users

The crucial role of urban industrial land redevelopment in sustainable urban renewal has garnered widespread attention. While some scholars have explored the interest game among stakeholders in industrial land redevelopment, they primarily focus on the government-led model. Moreover, there remains a research gap concerning the impact of government intervention on the redevelopment of industrial land. This article utilizes evolutionary game theory to investigate the interest game between local governments and original land users in the model of urban industrial land redevelopment dominated by original land users. We establish evolutionary game models considering incentives and the combination of incentives and regulations, explore the interest balance strategy, and examine the impact of positive incentives and mandatory regulations on industrial land redevelopment. Furthermore, we employ a numerical simulation to unveil the impact of initial strategies and parameter adjustments on game strategy. The research results are as follows: (1) Under the original land user-led redevelopment model, only two evolutionary stability strategies exist: either the original land users implement industrial land redevelopment with positive responses from local governments, or neither party advances the process. (2) Government intervention is pivotal in facilitating the redevelopment of inefficient industrial land as economic subsidies and punitive measures motivate more participants to adopt proactive strategies. (3) The increase in government support positively correlates with the likelihood of industrial land redevelopment implementation by original land users. (4) The interests and costs of original land users emerge as crucial parameters influencing strategic decisions. This study enriches the understanding of the interests of core participants in industrial land redevelopment and provides valuable insights for sustainable urban renewal.