Evolutionary Game Theory Research Articles

Simulation Analysis of Artificial Intelligence Technology Diffusion under Market Competition and Policy Incentives Based on Complex Network Evolutionary Game Models

The relationship network between enterprises will change their adoption behavior of AI technology and this micro-decision-making mechanism will eventually decide whether AI technology can diffuse and the extent of diffusion on the macro level. However, the existing AI technology diffusion research mostly focuses on the integration of AI technology with other industries from the industrial level, ignoring the complexity of the micro-complex game process and interactions within the enterprise network on the macro technology diffusion. In this regard, this paper builds a game model of AI technology diffusion in core and non-core enterprises from the levels of market competition and policy incentives based on complex network evolutionary game theory. It does this through simulation analysis that examines the mechanism of key factors affecting the diffusion of AI technology, as well as the influence and combination effects of pertinent policies. The study shows that (1) AI technology diffuses more effectively in non-core enterprises than it does in core enterprises; (2) changes in parameters like technology cost and policy regimes have a more evident impact on core enterprises than non-core ones; (3) in market competition, increasing the network average degree, the proportion of AI technology products in the mainstream market, the opportunity cost, the cost reduction factor, or decreasing the cost of AI technology can all promote the diffusion of AI technology; (4) under policy incentives, increasing the proportion of AI technology subsidies and the influence of high-tech identification of enterprises can both promote the diffusion of AI technology.

Analyzing the use of non-pharmaceutical personal protective measures through self-interest and social optimum for the control of an emerging disease

Non-pharmaceutical personal protective (NPP) measures such as face masks use, and hand and respiratory hygiene can be effective measures for mitigating the spread of aerosol/airborne diseases, such as COVID-19, in the absence of vaccination or treatment. However, the usage of such measures is constrained by their inherent perceived cost and effectiveness for reducing transmission risk. To understand the complex interaction of disease dynamics and individuals decision whether to adopt NPP or not, we incorporate evolutionary game theory into an epidemic model such as COVID-19. To compare how self-interested NPP use differs from social optimum, we also investigated optional control from a central planner’s perspective. We use Pontryagin’s maximum principle to identify the population-level NPP uptake that minimizes disease incidence by incurring the minimum costs. The evolutionary behavior model shows that NPP uptake increases at lower perceived costs of NPP, higher transmission risk, shorter duration of NPP use, higher effectiveness of NPP, and shorter duration of disease-induced immunity. Though social optimum NPP usage is generally more effective in reducing disease incidence than self-interested usage, our analysis identifies conditions under which both strategies get closer. Our model provides new insights for public health in mitigating a disease outbreak through NPP.

Rethinking climate agreements: self-enforcing strategies for emission reduction through sanctions

Although climate change is expected to cause significant negative impacts, climate treaties give hope for reducing greenhouse gas (GHG) emissions, potentially leading to less severe climate change. However, climate change is a public bad: If each country aims to maximize its own benefit, some countries might free ride and continue to emit GHGs. Consequently, climate treaties have seen limited success, and a central question is how future treaties can achieve GHG emission reduction and also be ‘self-enforcing’—where participating countries have no incentive to withdraw or modify their contributions. Here we examine a dynamic negotiation process involving multiple countries, each deciding whether and when to join the agreement and whether to penalize non-participants. In particular, we distinguish between (1) indirect punishment, in which countries invest less in emission reduction in response to non-compliance of other countries, and (2) direct punishment, in which countries impose sanctions, such as punitive tariffs, against countries that do not comply. We analyze the negotiation process using evolutionary game theory. We show that how the two types of punishment are implemented greatly affects the agreement’s outcome. In particular, an efficient combination of the punishments could lead to more desirable self-enforcing agreements. These findings indicate that integrating punitive measures with an effective negotiation framework could result in more desirable climate agreements.

Preferential selection based on aspiration and memory in spatial evolutionary prisoner’s dilemma game

When studying the spontaneous emergence of cooperation within the framework of evolutionary game theory, the process of strategy selection plays a crucial role in evolutionary dynamics. In most previous studies, it has been assumed that individuals randomly select learning targets from their neighbors. However, by considering the heterogeneity of individuals’ influence in real-life social networks, a preference for more realistic choices should be taken into account. Here, we propose a preferential selection mechanism based on individuals’ payoff aspiration in memory-based evolutionary game on square lattice networks. For each iteration, players in the regular lattices will prioritize selecting neighbors with the highest satisfaction rate over the most recent runs. The simulation results demonstrate that both the level of aspiration and the length of memory can elevate the level of cooperation to a certain extent. For the moderate aspiration level, when the value of betrayal temptation is less than threshold, cooperation level monotonously decreases with the increment of memory length. However, when betrayal temptation is above threshold, memory mechanism can facilitates the evolution of cooperation, while the absence of such a mechanism leads collaborators toward extinction.

Evolutionary Dynamics of Passive Housing Initiatives in New Rural Construction

In the context of China’s ambitious dual carbon goals, this study introduces an innovative reward–penalty incentive mechanism, grounded in evolutionary game theory, to develop a tripartite evolutionary game model concerning the construction of passive rural housing. This research meticulously analyzes the equilibrium and stability strategies of all involved parties and employs data simulation to examine the influence of varying parameters on the game dynamics. Our findings reveal that the government’s cost–benefit calculations significantly influence its decisions regarding passive housing initiatives. The study identifies optimal cost and benefit strategies for various developmental phases. Furthermore, the level of governmental rewards and penalties plays a crucial role in determining whether enterprises and farmers opt for passive housing solutions. The study establishes the efficacy of different incentive schemes at various stages. Importantly, the economic interests of enterprises and farmers are pivotal in their decision-making process regarding passive housing. The study advocates for a comprehensive set of measures to safeguard these interests, with a special emphasis on protecting farmers. In conclusion, this research offers substantial guidance for policy decisions aimed at transforming existing rural housing into passive housing, thereby aligning with China’s environmental and sustainability objectives.

Analyzing evolutionary game theory in epidemic management: A study on social distancing and mask-wearing strategies.

When combating a respiratory disease outbreak, the effectiveness of protective measures hinges on spontaneous shifts in human behavior driven by risk perception and careful cost-benefit analysis. In this study, a novel concept has been introduced, integrating social distancing and mask-wearing strategies into a unified framework that combines evolutionary game theory with an extended classical epidemic model. To yield deeper insights into human decision-making during COVID-19, we integrate both the prevalent dilemma faced at the epidemic's onset regarding mask-wearing and social distancing practices, along with a comprehensive cost-benefit analysis. We explore the often-overlooked aspect of effective mask adoption among undetected infectious individuals to evaluate the significance of source control. Both undetected and detected infectious individuals can significantly reduce the risk of infection for non-masked individuals by wearing effective facemasks. When the economical burden of mask usage becomes unsustainable in the community, promoting affordable and safe social distancing becomes vital in slowing the epidemic's progress, allowing crucial time for public health preparedness. In contrast, as the indirect expenses associated with safe social distancing escalate, affordable and effective facemask usage could be a feasible option. In our analysis, it was observed that during periods of heightened infection risk, there is a noticeable surge in public interest and dedication to complying with social distancing measures. However, its impact diminishes beyond a certain disease transmission threshold, as this strategy cannot completely eliminate the disease burden in the community. Maximum public compliance with social distancing and mask-wearing strategies can be achieved when they are affordable for the community. While implementing both strategies together could ultimately reduce the epidemic's effective reproduction number ([Formula: see text]) to below one, countries still have the flexibility to prioritize either of them, easing strictness on the other based on their socio-economic conditions.

Does government policy matter in the digital transformation of farmers’ cooperatives?—A tripartite evolutionary game analysis

Farmers’ cooperatives, as one of the new types of agricultural businesses, are an effective carrier for the digital transformation of agriculture, yet existing studies have paid less attention to how farmers’ cooperatives realize digital transformation. As agricultural economic organizations embedded in social networks, the digital transformation of farmers’ cooperatives requires the joint efforts of governments and companies. Based on the evolutionary game theory, this paper constructs a tripartite evolutionary game model of the government, digital technology companies and farmers’ cooperatives, and simulates and analyzes the behavioral decisions of different participants in the digital transformation of farmers’ cooperatives from the perspective of government policy. The results show: Medium government subsidies can effectively promote the digital transformation of farmers’ cooperatives, and strong subsidy policies increase the government’s financial burden, which is not conducive to policy sustainability. Strong government regulation facilitates digital technology companies to actively provide high-quality services for the digital transformation of farmers’ cooperatives, but government regulation does not have a significant impact on the strategic choices of the government and farmers’ cooperatives. When farmer cooperatives and digital technology companies are in a medium or high level of cooperative trust or when the number of labors saved by digital transformation reaches a certain level, farmers’ cooperatives opt for digital transformation even if the government withdraws from policy intervention.

An innovative fractional-order evolutionary game theoretical study of personal protection, quarantine, and isolation policies for combating epidemic diseases

This study uses imposed control techniques and vaccination game theory to study disease dynamics with transitory or diminishing immunity. Our model uses the ABC fractional-order derivative mechanism to show the effect of non-pharmaceutical interventions such as personal protection or awareness, quarantine, and isolation to simulate the essential control strategies against an infectious disease spread in an infinite and uniformly distributed population. A comprehensive evolutionary game theory study quantified the significant influence of people’s vaccination choices, with government forces participating in vaccination programs to improve obligatory control measures to reduce epidemic spread. This model uses the intervention options described above as a control strategy to reduce disease prevalence in human societies. Again, our simulated results show that a combined control strategy works exquisitely when the disease spreads even faster. A sluggish dissemination rate slows an epidemic outbreak, but modest control techniques can reestablish a disease-free equilibrium. Preventive vaccination regulates the border between the three phases, while personal protection, quarantine, and isolation methods reduce disease transmission in existing places. Thus, successfully combining these three intervention measures reduces epidemic or pandemic size, as represented by line graphs and 3D surface diagrams. For the first time, we use a fractional-order derivate to display the phase-portrayed trajectory graph to show the model’s dynamics if immunity wanes at a specific pace, considering various vaccination cost and effectiveness settings.

Strategic Management of Workforce Diversity: An Evolutionary Game Theory Approach as a Foundation for AI-Driven Systems

In the complex organisational landscape, managing workforce diversity effectively has become crucial due to rapid technological advancements and shifting societal values. This study explores strategic workforce management through the novel methodological framework consisting of the evolutionary game theory concept integrated with replicator dynamics and traditional game theory, addressing a notable gap in the literature and suggesting an evolutionarily stable workforce structure. Key findings indicate that targeted rewards for the most Enthusiastic employee type can reduce overall costs and enhance workforce efficiency, although managing a diverse team remains complex. The study reveals that while short-term incentives boost immediate productivity, long-term rewards facilitate favourable behavioural changes, which are crucial for sustaining organisational performance. Additionally, the role of artificial intelligence (AI) is highlighted, emphasising its potential to integrate with these theoretical models, thereby enhancing decision-making processes. The study underscores the importance of strategic leadership in navigating these dynamics, suggesting that leaders must tailor their approaches to balance short-term incentives and long-term rewards to maintain an optimal workforce structure.

Analysis of production decision-making evolution of steel enterprises under carbon border adjustment mechanism

This work explored the changes in production decision-making trends of Chinese steel enterprises under the influence of the carbon border adjustment mechanism. First, using evolutionary game theory, the interactive mechanism of complex production strategies among steel enterprises considering the carbon border adjustment mechanism was studied, including the impact of government subsidy coefficients, additional profits and carbon tax prices on enterprise decision-making. Second, the influence of key parameters on the dynamic evolutionary process was analysed. On this basis, the empirical simulation method was used to verify the game model and the main conclusions. Finally, the sensitivity analysis of the selected parameters was determined using Matlab software. The results showed that additional profits from green investment, government subsidy coefficients, input-output values and carbon tax prices had a higher impact on the evolution of enterprise production strategies. The results of this study provide a decision-making basis for the selection of future production methods for steel enterprises.

The Analysis of Pricing of Non-fungible Token from a Game Theory Perspective

ith the development of the metaverse, the deals of non-fungible Token(NFT) became hot. However, it is very difficult to evaluate the value of NFT because of the influence of its own nature. Although there are many valuation models for NFT, investors still cannot get the accurate and true value of NFT. Such inaccurate estimates will influence the strategy of investors. To understand and alleviate this effect. This article will use evolutionary game theory to construct model to analyze how should sellers and purchasers choose their strategy with the help of valuation models so that they can avoid risk. The conclusion shows that for the sellers, when the earnings of pricing over valuation overcomes is higher than pricing equal to the valuation, it is better to choose the strategy of pricing over valuation. For the purchasers, when the net profit of purchase is positive, it is better to choose the strategy of purchase.

Phase diagrams of bone remodeling using a 3D stochastic cellular automaton.

We propose a 3D stochastic cellular automaton model, governed by evolutionary game theory, to simulate bone remodeling dynamics. The model includes four voxel states: Formation, Quiescence, Resorption, and Environment. We simulate the Resorption and Formation processes on separate time scales to explore the parameter space and derive a phase diagram that illustrates the sensitivity of these processes to parameter changes. Combining these results, we simulate a full bone remodeling cycle. Furthermore, we show the importance of modeling small neighborhoods for studying local bone microenvironment controls. This model can guide experimental design and, in combination with other models, it could assist to further explore external impacts on bone remodeling. Consequently, this model contributes to an improved understanding of complex dynamics in bone remodeling dynamics and exploring alterations due to disease or drug treatment.

The impact of vaccination and social distancing on COVID-19: A compartmental model and an evolutionary game theory approach

The spread of COVID-19 disease is a worldwide issue that has impacted many countries. Mathematical modeling can help us better understand this disease and predict how different interventions and behavioral changes will affect disease progression. This study presents a compartmental model that corresponds to the COVID-19 trend in some countries. The compartments are: susceptible–exposed–unvaccinated infectious-partially vaccinated infectious–hospitalized–recovered–death–vaccinated (with at least three doses of vaccine). In this model, we have compartments such as individuals who have recovered but may re-infect if they come into contact with infection after completing the post-recovery immunity course, and individuals who have received one or two doses of vaccine are assumed to be partially immune and may infect. Individuals who have received at least three doses of vaccine are considered fully immune and will no longer be infected. The results show this model fits the data from the World Health Organization (WHO) for Finland, Denmark, the Netherlands, the United Kingdom, Italy, and Canada. Furthermore, the basic reproduction number of the proposed model is calculated, and we can compare it to the one released by the WHO to analyze the relative risk of infection based on the amount of investment in social distancing. People in Denmark, according to the study’s findings, invested less in social distancing than people in the other five countries. Finally, we use evolutionary game theory to examine the impact of changing strategy from not-vaccinated to partially vaccinated (with one or two doses of vaccine) in a population that is not fully immune.

How Can Variable Charging of Household Waste Be Accepted across Multiple Stakeholders Using Evolutionary Game Theory Coupled with Multiobjective Optimization

How Can Variable Charging of Household Waste Be Accepted across Multiple Stakeholders Using Evolutionary Game Theory Coupled with Multiobjective Optimization

A study on the evolutionary game theory of third-party governance of environmental pollution based on the quasi-co-owned relationship of pollution rights

The third-party governance of environmental pollution and the pollution rights trading system are two typical examples of modern environmental governance systems. In China, the quasi-co-owned relationship of pollution rights between pollution-discharging and pollution-control enterprises is the link through which to achieve synergy between the two factors. To explore how to achieve such synergy, first, the “principle of no-fault liability” is introduced, and the responsibilities of pollution-discharging enterprises and pollution-control enterprises are defined. Second, based on the quasi-co-owned relationship of pollution rights, a revenue-sharing mechanism for pollution rights trading is designed. Finally, a tripartite evolutionary game model consisting of pollution-discharging enterprises, pollution-control enterprises and local governments is constructed, and numerical simulation is performed. The results are as follows. 1) By setting a reasonable market trading price for pollution rights, a reward and punishment distribution coefficient, an improvement in reputation gains, and a saving in pollution rights indicators become conducive to the formation of cooperative relationships between pollution-discharging and pollution-control enterprises, for which the reasonable range of the reward and punishment distribution coefficient is (0.3, 0.4]. 2) Regardless of whether a government subsidy is provided, the optimal range of the revenue-sharing coefficient from pollution rights trading is [0.5, 0.6]. 3) When a government subsidy is withdrawn, increasing the market trading price and surplus indicators of pollution rights can promote the stable operation of the third-party governance model. 4) An increase in policy support and administrative accountability of higher levels of government and a reduction in supervision costs for local governments can help these parties effectively avoid addressing the absence of local government supervision. It is recommended that third-party governance enterprises participate in pollution rights trading to implement the main responsibilities of pollution-discharging and pollution-control enterprises and to set a reasonable revenue-sharing coefficient and reward and punishment distribution coefficient of pollution rights.

Evolutionary analysis of nuclear wastewater collaborative governance based on prospect theory

Dealing nuclear wastewater into the sea significantly threatens the global ecological environment and public health. Existing research primarily focuses on the roles of emission and regulatory bodies, often overlooking the collaborative interactions between these entities. This study extends traditional marine governance strategies by considering upstream and downstream countries as research subjects. We first establish an evolutionary game model for the collaborative governance of nuclear wastewater between upstream and downstream countries, utilizing the Prospect theory and Evolutionary Game Theory. Subsequently, numerical simulations are employed to explore different entities' decision-making behaviors and influencing factors. Our findings indicate that: (1) initial willingness affects the time it takes for the system to reach a stable state; (2) the benefits and costs of proactive governance strategies influence decision-maker's behaviors; (3) subjective factors (risk preference, loss aversion) affect the strategic choices. This research provides a scientific basis for understanding different countries' selection of nuclear wastewater governance strategies.

Impact of dual-credit policy on diffusion of technology R & D among automakers: Based on an evolutionary game model with technology-spillover in complex network

Dual-credit policy promotes new-energy vehicle (NEV) automakers to invest in technology R & D by granting NEV with better technological performance to higher NEV-credit. Based on the complex network evolutionary game theory, this paper constructs a technology R & D diffusion model of automakers considering the technology spillover effects, and explores the dynamic impact of adjusting different dual-credit policy parameters on technology R & D diffusion among automakers. Our results show that: (1) increasing the annual increment of NEV-credit requirement ratio can encourage more NEV automakers to invest in technology R & D, but also lead to significant increase in NEV-credit price. (2) Reducing the CAFE target coefficient can simultaneously promote the diffusion of NEV technology R & D and GV green-technology R & D, but excessive reduction can hinder GV automakers from entering NEV market. (3) Lowering the upper limit of credit per unit NEV suppresses the diffusion of NEV technology R & D. (4) Reducing technology-spillover is conducive to the incentive of dual-credit policy on the diffusion of technology R & D.

A generic approach for network defense strategies generation based on evolutionary game theory

The generation of optimal defense strategies in dynamic adversarial environments is crucial for cybersecurity. Recently, defense approaches based on evolutionary game theory have gained significant achievements. However, they would fail when facing complex networks and sophisticated attack strategies, due to the fatal drawbacks of defense strategy generation considering atomic attacks only. To relieve this issue, a generic approach for generating defense strategies using evolutionary game theory is proposed in this paper. Initially, a novel payoff quantification method for network attack-defense games based on attack graphs is designed. Innovatively, two factors concerning the decision-maker's degree of irrationality (DI) and the level of environmental security (LES) are introduced into the replicator dynamics equation to model the impacts on equilibrium solutions. Noting that Active Directory (AD) domain service is one of the most used and representative information security management system in Windows domains, from which attack graphs and paths can be plainly extracted and analyzed. Therefore, it is necessary and imperative to anchor AD to unfold the theoretical analyses and experiments validation based on a real environment. Case studies on a real-world AD network demonstrate that the proposed approach is effective and can generate stable and efficient defense strategies.

Stability Analysis of the Credit Market in Supply Chain Finance Based on Stochastic Evolutionary Game Theory

Stability Analysis of the Credit Market in Supply Chain Finance Based on Stochastic Evolutionary Game Theory

The Dynamics of Rewards and Penalties: Governmental Impact on Green Packaging Adoption in Logistics

The widespread use of traditional packaging materials poses significant environmental challenges. Adopting green packaging is essential for reducing pollution and conserving natural resources. This paper aims to examine the effectiveness of government incentives and penalties in promoting the adoption of green packaging by logistics companies. We developed an evolutionary game theory model that involves governments and logistics companies, comparing the impacts of static and dynamic reward and penalty policies. The results indicate that (1) static policies often lead to oscillatory adoption rates of green packaging without achieving a stable equilibrium, while dynamic policies generally promote steadier adoption of sustainable practices. (2) Different combinations of dynamic policies have varying influences on logistics companies’ propensity to adopt green packaging solutions. Specifically, dynamic rewards and static penalties are particularly effective at encouraging logistics companies to adopt green packaging. (3) A combination of dynamic rewards and penalties tends to facilitate more rapid and consistent adoption of green packaging by logistics companies. (4) An increase in government supervision costs is associated with reduced regulatory actions and a lower prevalence of green packaging. These insights are critical for policymakers aiming to craft regulations that successfully encourage sustainability within logistics operations.