Evolutionary Game Theory Research Articles

Effect of Q-learning on the evolution of cooperation behavior in collective motion: An improved Vicsek model

There have been numerous studies on collective behavior, among which communication between agents can have a great impact on both the payoff and the cost of making decisions. Research usually focuses on how to improve the collective synchronization rate or accelerate the process of cooperation under given communication cost constraints. In this context, evolutionary game theory (EGT) and reinforcement learning (RL) arise as essential frameworks for tackling this intricate problem. In this study, an adapted Vicsek model is introduced, wherein agents exhibit varying movement patterns contingent on their chosen strategies. Each agent gains a payoff determined by the advantages of collective motion juxtaposed with the cost of communicating with neighboring agents. Individuals choose the objective agents based on the Q-learning strategy and then adapt their strategies following the Fermi rule. The research reveals that the utmost level of cooperation and synchronization can be attained at an optimal communication radius after applying Q-learning. Similar conclusions have been drawn from research on the influence of random noise and relative cost. Different cost functions were considered in the study to demonstrate the robustness of the proposed model and conclusions under a wide range of conditions. (https://github.com/WangchengjieT/VM-EGT-Q)

Adherence strategy based on evolutionary games in epidemic spreading

In light of the dynamic interaction between epidemic spreading and behavior responses, the development of a comprehensive epidemic model is both crucial and challenging. In this paper, based on the evolutionary game theory, we investigate the co-evolution of adherence strategy decision-making and epidemic spreading, with a particular focus on the role of conformity. Considering individual self-determination and population imitation, we analyze two distinct strategy updating rules: profit-driven decision-making and conformity-driven decision-making. We also account for the influence of neighbors’ strategies on epidemic spread rates. Extensive simulations are conducted to analyze epidemic phase transitions and the distribution of infected individuals at the stationary state of the epidemic. By analyzing the adoption of strategies under different scenarios of conformity fractions, we find that conformity widens the disparity between different strategies over time. In conclusion, our results demonstrate that conformity promotes the adherence strategy, which helps reduce the size of the epidemic.

Trust Based Active Game Data Collection Scheme in Smart Cities

The concept of a smart city is to equip sensors to various objects in urban life to monitor areas and collect sensing data, and make wise decisions based on the collected data. However, some malicious sensor devices may interrupt and interfere with data collection, leading to a reduction in the integrity and availability of information, thereby causing harm to Internet of Things(IoT) applications. Therefore, identifying the credibility of sensor nodes to ensure the credibility of data collection is a challenge. This paper proposes a trust based active game data collection (TAGDC) scheme to collect trust data in the IoT. This TAGDC scheme mainly includes the following parts: 1)An active trust framework plus evolutionary game theory is proposed to encourage high-energy sensors to send detection routes and quickly obtain sensor trust. 2)In order to balance the data security requirements of subnetworks, the number and frequency of detection routes required by subnetworks are estimated through mechanism modeling and fuzzy analytic hierarchy process. 3)The design focuses on the internal trust computing model in the region to evaluate the trust of nodes. The findings of the experiment demonstrate that the TAGDC scheme, as described in this research study, enhances the accuracy of identifying malicious nodes by 20%, reduces the required identification time by 40%, and improves the data collection success rate by 5%.

EFFECTS OF RHYTHMIC REPRODUCTION ON THE SURVIVAL OF COOPERATORS

Organisms on Earth are known to evolve by cooperating with their neighbors in external environments. These cooperating organisms are under the influence of circadian rhythms derived from Earth’s rotation and have various biological rhythms within them. However, little is known about the effects of these rhythms on the cooperation of organisms. In this study, we investigate the effects of the biological rhythms on the survival of cooperators in evolution using evolutionary game theory. We focused on the rhythm in reproduction among the various biological rhythms that organisms have. Our results are as follows. Rhythmic reproductions play a role in limiting the survival of cooperators; they reduce the survival limits of cooperators compared to when there is no rhythm. Their limiting force is sensitive to the number of interacting neighbors, however. When the number of interacting neighbors is relatively small, the effects of rhythm in reproduction on the survival limits of cooperators are not significant. As the number of interacting neighbors increases, the limiting force of rhythmic reproductions becomes more powerful, gradually decreasing the survival limits of cooperators. Variations of rhythm in reproduction do not alter the survival limits; they only cause fluctuations in the population of cooperators within the survival limits. Our findings suggest that the rhythmic reproductions in evolution determine the survival domain of cooperators, where the number of interacting neighbors is a major factor along with rhythmic reproductions. Organisms under the constant pressure imposed by rhythmic reproductions may need to find the optimal number of interacting neighbors to survive and evolve to a new level through cooperation.

Synergizing stakeholder collaboration for value co-creation in China's prefabricated decoration diffusion: A tripartite evolutionary game perspective

The emerging prefabricated decoration (PD) sector in China offers a compelling avenue for revolutionizing the construction industry, aligning with the imperatives of sustainability and industrialization. However, the outlook for the diffusion of PD in China's construction industry is less than promising. The development of PD is closely related to its value creation and the intricate interplay of stakeholder strategies and their ramifications on PD's value co-creation necessitates thorough exploration. Based on evolutionary game theory, this study elucidates the dynamic interactions among local governments, developers, and decoration contractors in China's evolving PD landscape. A tripartite evolutionary game model was established, investigating the evolutionary stable equilibriums and the corresponding strategies. Then, an empirical analysis in Chongqing City validates the game models and primary conclusions. Scrutinizing subsidy thresholds, local subsidy biases, PD proportion in prefabrication, and binary innovation allocation, the research unveils nuanced insights pertinent to boosting PD's value co-creation. Findings advocate an assembly rate threshold of subsidy (65%–70 %) and a shift towards incentivizing decoration contractors to catalyze PD adoption. Additionally, excessive PD proportion and immature product investments may hinder industry development, prompting stakeholders to recalibrate strategies dynamically. These findings characterize the mechanisms of stakeholder value co-creation during the PD diffusion, enriching the implications of value co-creation theory in the emerging industry of the construction sector. They also furnish stakeholders committed to promoting the diffusion of PD in the domestic and international construction sectors with practical strategic guidance, particularly tailored for cities in the early stages of PD development.

A decision framework for improving the service quality of charging stations based on online reviews and evolutionary game theory

With “charging anxiety” gradually replacing “range anxiety” as the major obstacle for consumers to purchase electric vehicles (EVs), the quantity-driven rather than quality-driven charging station development model barely meets the development of the EV market. It is of great practical significance that a comprehensive and in-depth insight into charging stations’ customer requirements (CRs) to uncover their pain points and improvement measures. Therefore, this study develops a decision framework for improving EV charging service quality based on quality function deployment (QFD). First, CRs are extracted based on charging station online reviews and classified by the revised importance performance analysis (IPA) method to identify the charging service’s main highlights and pain points. Then, the process of achieving consensus among experts on evaluating the correlation between CRs and service design requirements (DRs) is considered as an evolutionary game of coalition, with a meritocratic Fermi rule considering affinities (MFA) being introduced. Finally, the importance of DRs is determined by combining the Shapley value and risk aversion theory. The results indicate that regulation and convenience are the main highlights of charging stations to satisfy EV users, while reliability and assurance are primary pain points. The charging station operators should prioritize their limited resources on providing efficient customer and maintenance services, installing charging stations with international standards, and regularly inspecting and updating charging infrastructures. This study is beneficial for charging station operators to understand what customers want and how to satisfy them.

How can rural China achieve sustainable development through inclusive innovation? A tripartite evolutionary game analysis

Promoting inclusive innovation is crucial for achieving inclusive growth and sustainable development. This study investigated the decision-making behaviors and interaction mechanisms among the government, enterprises, and base of the pyramid (BoP) customers in the context of inclusive innovation in rural China by applying evolutionary game theory. A tripartite evolutionary game model was developed, and evolutionarily stable strategies along with their corresponding conditions were derived. Numerical simulations were employed to analyze the dynamic evolution process of stakeholders’ strategy choices under various scenarios and their sensitivity to key parameters. The findings suggest that the government plays different roles throughout the lifecycle of inclusive innovation in rural China, acting as an advocate in the initial stage, a promoter in the growth stage, and gradually diminishing its involvement as the market matures. Furthermore, the study reveals that low subsidies are not conducive to the evolution of optimal solutions for the three stakeholders, while high supervision costs reduce the government’s regulatory willingness, thereby hindering enterprises and the BoP customers from implementing and engaging in inclusive innovation in rural areas. Based on these insights, this research proposed some promotional mechanisms tailored to the different developmental stages of inclusive innovation, drawing from the industry life cycle theory. The insights and model developed in this study extend beyond rural China and offer a valuable framework for enhancing sustainable development in rural areas across other developing countries and regions.

Evolutionary game theory in patent alliance dynamics: strategy and policy implications

ABSTRACT This study investigates the dynamics of patent alliances, underscoring their strategic relevance for firms pursuing technological innovation and market leadership. We address the issue of opportunistic behaviour stemming from the contractual gaps in these alliances, proposing strategic measures to curtail such conduct and bolster alliance robustness. Utilising Evolutionary Game Theory (EGT), we conduct numerical simulations to analyze the motives for alliance formation, the efficacy of anti-opportunism strategies, and their influence on alliance durability. The study highlights how regulatory policies increase repercussions, fostering cooperative alliances. Our simulations, predicated on defined parameters such as the likelihood of detecting and sanctioning opportunistic actions, showcase how these factors shape the strategic decisions of alliance members. This study enriches the theoretical discourse on patent alliance management by integrating the EGT approach, shedding light on effective deterrents against opportunistic behaviour and mechanisms fostering alliance solidarity. We advocate for a synchronised approach that aligns stringent regulatory practices with corporate strategies to ensure the resilience and success of patent alliances, crucial for navigating the complexities of competitive technology markets and maintaining long-term alliance efficacy. This comprehensive strategy is essential for managing the intricacies of patent alliances and securing their long-term viability and productivity.

Impact of policy interventions on low‐carbon technology innovation diffusion in supply networks

AbstractThe promotion of sustainability and innovation in the current dynamic landscape of global industry in the context of climate change has become imperative. This research paper explores the critical role of policy interventions in facilitating the diffusion of low‐carbon technologies through intricate supply networks. As countries endeavor to meet environmental goals and transition to a more sustainable economy, it becomes critical to understand how policies can effectively shape the diffusion of these technologies through the supply chain. This study employs game theory and evolutionary game theory to investigate the intricate interactions within supply networks, with a particular focus on the nuanced effects of policy on the diffusion of low‐carbon technology innovations. The findings suggest that dynamically adjusted policies have the potential to significantly increase the uptake of low‐carbon technologies. In particular, an effective subsidy scheme not only promotes the diffusion of innovations, but also demonstrates the ability of government subsidies to be distributed efficiently. In addition, the implementation of dynamic carbon trading schemes is considered to be an important mechanism for promoting firms' decarbonization and stabilizing strategic decision‐making processes within firms. This study highlights the significance of tailored dynamic policy frameworks in promoting the adoption of sustainable technologies in supply chains, thus making a significant contribution to the broader goal of achieving a sustainable future.

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.