Evolutionary Game Approach Research Articles

Unraveling the Impact of Carbon Markets on Corporate Green Technology Investment Behavior: an Evolutionary Game Approach from the Lens of Competitive Manufacturers

Unraveling the Impact of Carbon Markets on Corporate Green Technology Investment Behavior: an Evolutionary Game Approach from the Lens of Competitive Manufacturers

How to mobilize owner-dynamic capabilities in megaprojects? A tripartite evolutionary game approach

PurposeCurrently, many studies have shown an increasing interest in owner-dynamic capabilities (ODCs). Existing studies mainly focus on the dynamic capability basis and capability development within the owner organization, whereas they rarely analyze the capability mobilization within the network of participants in megaprojects. Therefore, this study aims to explain the interaction and evolution of the mobilization strategies of ODCs and the cooperative strategies of other participants.Design/methodology/approachThis study develops a tripartite evolutionary game model to analyze the evolutionarily stable strategy of the owner, the reciprocal participants and the general participants. Results are numerically simulated with a validation case. The asymptotic stability of multiple group strategies is discussed under the replicator dynamic system.FindingsThis study suggests that resource complementarity significantly reduces the difficulty of mobilization. Moreover, these strategies are only effective with sufficient ODCs. The results indicate that reciprocal participants are more sensitive to the change in resource complementarity.Originality/valueThis study provides strategic guidance for mobilizing ODCs in megaprojects to better embrace uncertainty and stress, contributing to the dynamic capability literature with an evolutionary game approach. And new insight for the study of reciprocity preference in megaprojects is also provided.

Cooperative governance mechanisms for personal information security: an evolutionary game approach

PurposeThis paper aims to investigate the cooperative governance mechanisms for personal information security, which can help enrich digital governance research and provide a reference for the formulation of protection policies for personal information security.Design/methodology/approachThis paper constructs an evolutionary game model consisting of regulators, digital enterprises and consumers, which is combined with the simulation method to examine the influence of different factors on personal information protection and governance.FindingsThe results reveal seven stable equilibrium strategies for personal information security within the cooperative governance game system. The non-compliant processing of personal information by digital enterprises can damage the rights and interests of consumers. However, the combination of regulatory measures implemented by supervisory authorities and the rights protection measures enacted by consumers can effectively promote the self-regulation of digital enterprises. The reputation mechanism exerts a restricting effect on the opportunistic behaviour of the participants.Research limitations/implicationsThe authors focus on the regulation of digital enterprises and do not consider the involvement of malicious actors such as hackers, and the authors will continue to focus on the game when assessing the governance of malicious actors in subsequent research.Practical implicationsThis study's results enhance digital governance research and offer a reference for developing policies that protect personal information security.Originality/valueThis paper builds an analytical framework for cooperative governance for personal information security, which helps to understand the decision-making behaviour and motivation of different subjects and to better address issues in the governance for personal information security.

A stochastic evolutionary dynamic game model for analyzing the ride-sourcing market with limited platform reputation

Ride-sourcing services have become increasingly vital in meeting on-demand mobility needs. However, safety issues and public health concerns are obstacles for passengers' trust in ride-sourcing service providers. In this paper, we propose a stochastic evolutionary dynamic game approach to modeling and managing the ride-sourcing market with limited platform reputation, and interpret some intriguing phenomena in terms of passengers' and drivers' travel behavior, e.g. survivor bias, reciprocity, and tragedy of the commons. A stochastic state dynamic game model is formulated to capture complicated interactions among three games/states, i.e. trust game, complaint game, and rating-based game. The stationary distribution of each state and transitional probabilities are deducted from the stochastic game theory. We analyze the ride-sourcing system's evolution and reveal its intrinsic mechanism based on the model. We found that, if the governmental support for complaints is inadequate and passengers bear the high complaint costs, drivers will be more inclined to compensate passengers privately (i.e. reciprocity) to escape punishment for unqualified service. As a result, the service quality will deteriorate in the long run. To prevent the deterioration of service quality, we explore adaptive pricing strategies for the ride-sourcing platform and suggest managerial strategies for the government. This paper bridges the gap between the dynamic evolution and the mechanism of trust in ride-sourcing services, which has been seldom studied in the literature. All of the analytical results are illustrated by numerical experiments.

Incentives for promoting climate change adaptation technologies in agriculture: an evolutionary game approach.

Climate change adaptation technologies (CCATs) have become increasingly important for farmers as they face the challenges of climate change and natural disasters. Despite this, many rural areas still rely on traditional agricultural techniques. To promote the adoption of CCATs in agriculture, it is necessary to explore the incentives and conditions for the effectiveness of the policy. We develop an evolutionary game model to analyze the behavior of local governments and farmers in promoting CCATs. Our findings indicate that, under certain conditions, the promotion of CCATs can achieve equilibrium. The incentive for farmers to adopt CCATs increases within a certain range when local governments provide risk subsidies and cost sharing. When subsidies are too high, however, local governments may choose not to promote CCATs, which reduces the incentives for farmers to adopt them. Publicity is also an important factor in promoting CCATs. Our study provides insight into the development of policies aimed at promoting CCATs in agriculture.

Analysis of Public Technology Collaborative Innovation Based on Private Technology: A Tripartite Evolutionary Game Approach

Public technology collaborative innovation serves as an essential driving force to implement the innovation-driven strategy as well as promote national and regional economic sustainable development. Moreover, strengthening the construction of public technology collaborative innovation system is likewise of great significance for strengthening the quality and technology level of enterprises, enhancing their independent innovation capability, and achieving strategic transformation. Particularly, collaborative innovation of public technology in accordance with mature private technology enables the efficient allocation of technological and economic resources. In practice, there are still issues including high innovation costs, insufficient motivation, and low efficiency of innovation. Existing research typically ignores the technical cooperation among multiple parties. An evolutionary game model of public technology collaborative innovation is established in this paper to examine the interaction mechanism among the government, enterprises, and technology owners. For the purpose of analyzing the dynamic evolution of public technology collaborative innovation behavior and its influencing factors, numerical simulation experiments were conducted with meaningful results: (1) Setting the cost and benefit allocation coefficient in a scientific manner contributes to system stability and promotes cooperation among game players. (2) Reasonable government subsidies and penalties can assist in the formation of an active technological innovation between the firm and the technology owner. (3) The pricing method of private technology transfer is a significant factor influencing the evolution process. Besides, the adoption of a technology pricing method on the basis of government interests is conducive to the formation of a stable tripartite win-win situation. The research on collaborative innovation of public technology is further expanded in this paper, offering a reference for the formulation of relevant policies aimed at promoting cooperation in public technology innovation.

Dynamic Evolutionary Game Approach for Blockchain-Driven Incentive and Restraint Mechanism in Supply Chain Financing

The sustainability of financing is an important measure in the development of supply chains. However, the difficulty and high cost of financing have always been critical factors hindering the sustainable development of small and medium-sized enterprises (SMEs). Blockchain technology (BT) is an effective tool to relieve the current problem. Based on it, this study aims to establish Blockchain-driven incentives and restraint mechanisms for SME financing using a dynamic game model in which financial institutions (FIs) and new agricultural business entities (NABEs) are regarded as game subjects. By analyzing the impact of key parameters on the equilibrium state of the game for all stakeholders, several findings are drawn as follows. (1) The usage of BT affects FI loan strategies by influencing their cost in supply chain financing. (2) The usage of BT affects NABE’s strategy choice by influencing their loan interest rate. (3) The usage of BT affects NABE’s strategy choice by influencing their default losses. (4) The usage of BT affects NABE’s and FI’s strategy choices by influencing their additional benefits. This study provides decision-making support for optimal strategy decisions under different conditions and serves as a theoretical reference for the government in formulating financing incentive and restraint mechanisms.

Does blockchain-based traceability system guarantee information authenticity? An evolutionary game approach

While blockchain-based traceability systems in agricultural product supply chains (APSCs) have the potential to improve operational efficiency and food safety, little literature examined the risk of traceability information being falsified before it is recorded on the blockchain. Therefore, using an evolutionary game model, this paper studies the potential falsification risk in the blockchain-based traceability system for agricultural products. The model focuses on the evolutionary path of two key stakeholders: farmers who may falsify information and blockchain platforms that may verify the authenticity of the information. Factors including government supervision and market demand are also considered. The results demonstrate that: (1) the evolutionary stable strategy of the game model depends on whether both stakeholders can benefit from information falsification after taking into account related costs and penalties; (2) when the purchasers' preference for product freshness and well-known places of origin is sufficiently high, farmers will consistently engage in the information falsification, with the platform allowing such behavior to persist; (3) if the downstream stakeholders attach importance to the authenticity of traceability information recorded on the blockchain, this will reduce farmers' willingness to falsify information and increase blockchain platforms’ willingness to verify authenticity; these two effects can lead to situations where the optimal evolutionary stable strategy exists. Based on the results, we discuss the details of blockchain-enabled audits to curb information falsification. The findings of this paper call attention to preventing falsification and developing guidelines for blockchain technology adoptions in APSCs.

Inter-regional cooperation in the transfers of energy-intensive industry: An evolutionary game approach

Transferring industries to clean energy-producing regions and improving energy efficiency are feasible and important strategies for regional control of energy consumption in energy-intensive industries of energy-rich regions. This paper constructs a three-party evolutionary game model including governments of transferred-in and -out regions, and energy-intensive enterprises. This model explores the cooperation mechanism of multi-players in the inter-regional transfer of energy-intensive industries under different policies and circumstances. The results are as follows. (1) Some certain conditions create a stable equilibrium state in which governments of transferred-in and -out regions choose a cooperative strategy, and the energy-intensive enterprises choose a transfer strategy. (2) Costs are key factors influencing the transfer of energy-intensive enterprises. Also, transferred-in regions should optimize their business environment and formulate their acceptance strategy based on their current energy consumption constraint and clean energy reserve situation. (3) The policy support from governments of transferred-in and -out regions can promote the orderly transfer of energy-intensive enterprises. The findings of this paper can provide a reference to formulate policies for the transfer of energy-intensive enterprises.

Service Mechanism for the Cloud–Edge Collaboration System Considering Quality of Experience in the Digital Economy Era: An Evolutionary Game Approach

In the digital economy era, cloud–edge collaboration technology provides the necessary technical support for the digital transformation of enterprises, which can improve the quality of services (QoS), and it attracts extensive attention from scholars and entrepreneurs from all fields. Under the bounded-rationality hypothesis, this paper investigates the service mechanism for the cloud–edge collaboration system considering the quality of experience (QoE) and presents a dynamic evolutionary game model between cloud service providers and edge operators by applying the evolutionary game theory. Then, this paper analyzes the equilibrium and stability conditions for the decision-making of both parties involved to guarantee the QoE reaches the ideal state. In addition, we investigate the factors that influence the stable cooperation between the two evolutionary stable strategies and validate the theoretical analytical results with numerical simulations. The research results show that the final evolution of the cloud–edge collaboration system depends on the benefits and costs of the game matrix between the two parties and the initial state values of the system. Under a specific condition, the cloud–edge collaboration system can eventually be driven to be an ideal state by reducing the collaboration cost and improving the collaboration benefit. The more both parties focus on the QoE, the more conducive it will be for the formation of a cloud–edge collaboration, thus effectively promoting long-term stability and better serving enterprises’ digital transformation.

Research on the Regulation of Algorithmic Price Discrimination Behaviour of E-Commerce Platform Based on Tripartite Evolutionary Game

With the development of the digital economy, the algorithms and big data technologies of e-commerce platforms have gradually turned into double-edged swords. While realising personalised recommendations, they also provide information technology support for the use of algorithmic prices to discriminate and extract residual value from consumers. Consumers frequently use Black Cat and third-party media to complain, resulting in a significant negative impact. Therefore, in order to regulate algorithmic price discrimination, using e-commerce platforms, local governments and consumers act as game subjects, taking an evolutionary game approach. We analyse the impact of different situations and factors on the system’s evolutionary stability strategy and conduct its verification via simulation experiments. This study shows that several measures, such as increasing cooperation with the media; establishing clear regulatory rules to reduce the extent of algorithmic price discrimination and the grey revenue of e-commerce platforms; establishing a long-term mechanism for consumer feedback; improving rewards and punishments to increase the probability of successful regulation and penalties by local governments; sharing information to reduce the cost of consumer regulation; and setting reasonable bonus thresholds based on government revenue and consumer regulation costs, can effectively regulate algorithmic price discrimination and promote the sustainable development of e-commerce platforms.

Evolutionary Game Study of Waste Separation Policy in the Context of the “Double Carbon” Target

Research on waste separation promotion policies is of great theoretical and practical significance for the universal implementation of the domestic waste separation system. This paper constructs a non-cooperative tripartite evolutionary game model that includes central command, local deployment, and enterprise performance. An evolutionary game approach was used to analyze the strategic choices of the central government, local government, and separation enterprises in waste separation promotion, and this study investigated the factors influencing the evolution of these choices using numerical simulations. The findings indicated the following: central government, local governments, and separation enterprises are affected differently by their respective willingness to participate; the behavior of separation enterprises is less influenced by the central government’s and local governments’ willingness to participate and is primarily influenced by market factors, whereas local government is more influenced by the central government’s willingness to participate; and local government and classification enterprises are affected differently by their respective willingness to participate. While separation firms are more susceptible to the cost-sharing, income, and revenue distribution coefficient, local governments are more responsive to policy support. The promotion of waste separation in China requires strengthened centralized waste management to avoid the failure of local waste separation, broadening of the scope of central funding incentives and establishing local separation compensation mechanisms, clarifying local waste separation performance responsibilities and new waste tax collection standards, supporting separation enterprises’ technological innovation and guiding public participation in waste separation, creating an environment for waste separation, and deepening the study of waste separation accordingly.

Emission reduction with hybrid mechanisms in civil aviation: An evolutionary game approach

With the rapid growth of the aviation industry, the issue of carbon emissions has become a substantial challenge for governments and airlines. This paper proposes a hybrid carbon emission reduction mechanism, including major airlines in the emission trading systems and implementing carbon tax for small and medium-sized airlines. First, a tripartite evolutionary game model is constructed to study strategic behaviors. Second, four scenarios of evolutionarily stable strategies (ESSs) are analyzed. Finally, the influencing parameters of players’ strategy choices are analyzed through simulations. The results show that: 1) the steady development scenarios (1, 1, 1) can be reached under the appropriate conditions; 2) the parameters such as carbon allowances and carbon tax prices significantly influence the evolutionary trend of stakeholders’ dynamic choices; 3) the implementation of a hybrid mechanism by the government could facilitate the choice of low carbon operation strategies for both types of airlines. Accordingly, a series of policy recommendations are proposed to promote carbon emission reduction in civil aviation. This study combines evolutionary game and scenario analysis methods in an attempt to provide a new perspective on carbon emission reduction governance, thereby promoting the effective development of carbon emission reduction in civil aviation in the future.

How to Enhance Data Sharing in Digital Government Construction: A Tripartite Stochastic Evolutionary Game Approach

Digital government construction is a complex system project, and data sharing is its governance niche. Cross-sectoral data sharing is the core issue of improving governance capacity in the construction of digital governments. Aimed at the dilemma of insufficient data sharing across departments, according to evolutionary game theory (EGT), we refined the game relationship between the data management department and the different government functional departments participating in cross-department data sharing. We used white Gaussian noise as a random perturbation, constructed a tripartite stochastic evolutionary game model, analyzed the stability of the stochastic game system and studied the influence of the main parameters on the evolution of the game system with the help of numerical simulation. The results show that there exists a positive stable point in the process of cross-department data sharing. The external effect of data sharing can be improved by enhancing the investment in data sharing by government functional departments. The accumulation of interagency trust relationships can gradually eliminate the differences in data sharing among different departments. The coordination mechanism of government data sharing and the construction of the “good and bad reviews” system can form an internal and external adjustment mechanism for functional departments and the data management department and can promote multiple departments to participate in cross-department data sharing more actively.

Optimal Allocation Scheme of Renewable Energy Consumption Responsibility Weight under Renewable Portfolio Standards: An Integrated Evolutionary Game and Stochastic Optimization Approach

Developing renewable energy has become a major strategy for China to accelerate the energy transition and combat climate change. Accordingly, a guarantee mechanism for renewable energy consumption with renewable portfolio standards (RPS) has been set in China. However, currently, the top-down allocation of regional renewable energy consumption targets often has issues of unfairness and inefficiency. It is necessary to investigate the issue of how to stimulate the renewable energy consumption potential on the demand side and reasonably formulate the consumption responsibility weights of various market entities. This paper aimed to develop a new methodology for the weight allocation of renewable energy consumption responsibilities. In doing so, an integrated model of an evolutionary game and stochastic optimization was constructed between market entities and governments. Then, the equilibrium strategies of market entities and governments were obtained through the evolutionary game. Furthermore, based on the equilibrium strategies, this paper optimized the renewable energy consumption weight of each market entity, which constitutes the optimal allocation scheme of renewable energy consumption responsibility weights. Finally, using the data of 7069 market entities in Hubei Province in 2021, this study simulated the model to verify its effectiveness and practicability. The results indicate that the willingness of market entities to assume more consumption responsibility is positively correlated with the government’s incentives and the maturity of the green electricity trading market. This study provides important implications for optimizing government regulations and promoting renewable energy consumption.

Evolutionary game analysis of energy saving behavior of tourism enterprises under carbon emission constraints

Abstract As one of the sectors affected by global climate change, tourism enterprises are actively seeking feasible energy-saving and emission reduction schemes. The article adopts the evolutionary game approach and establishes the game matrix between tourism enterprises and the government. The research results show that the strategy choice of the two-game subjects depends on the initial state and its evolutionary path. The local government needs to adjust penalty measures for environmental pollution of tourism enterprises and reduce the cost of controlling low carbon reasonably tourism. Enterprises need to reduce the cost of saving energy through dynamic adjustment of carbon emission rewards, which are the basis of the long-term perspective and the big picture. On the premise of considering both interests, tourism enterprises and government should make joint efforts to achieve the goal of energy conservation, environmental protection and sustainable development.

Analysis of collaborative urban public crisis governance in complex system: A multi-agent stochastic evolutionary game approach

As the world economy is leaping forward, countries have been exposed to the constant threat of public crises. Accelerated urbanization has currently led to a proliferation of urban public crisis events. Conventional governance methods become ineffective under the effect of the highly uncertain and dynamic nature of urban public crisis events. Accordingly, this study primarily aims to facilitate the construction of a collaborative multi-agent alliance in urban public crisis governance. Thus, evolutionary game theory is combined with system dynamics, Gaussian white noise stochastic disturbance terms are introduced to model the complex characteristics of urban public crisis events, and evolutionary game models and stochastic evolutionary game models are built for three types of groups (i.e., local governments, enterprises, and the public) to study the evolutionary process of group strategy choice behavior with or without central government supervision. The result of this study suggests that under the effect of the complexity factor, the central government's dynamic reward and punishment mechanism plays a role of in reaching a facilitative and stable alliance for collaborative urban public crisis governance. Furthermore, contributions, unresolved issues and vital challenges are identified. In this study, a solid basis is laid for future research and widespread use of collaborative urban public crisis governance, which provides a reference for stakeholders in the field of urban public crisis governance.

Resources Sharing Behavior of Science and Technology Service Platform: An Evolutionary Game Approach

As the increasingly complex demand for scientific and technological innovation and explosive growth of scientific and technological resources, all participants of the scientific and technological service platform need to actively share resources to provide support for scientific and technological innovation activities. Therefore, it is of great practical significance to use evolutionary game theory to study the sharing behavior of science and technology resources of users of science and technology service platforms. This paper provides an evolutionary game model based on the analysis of user resource sharing behavior of science and technology service platform, discusses the dynamic evolution process and influence factors of game equilibrium strategy of science and technology resource sharing behavior. The results show that the less the extra cost of positive sharing, the more the extra benefits and the less the defection benefits are, the more users in the platform will choose the positive resource sharing strategy. The research results have an important guiding role in establishing and managing the science and technology service platform, and in making decision about science and technology resource sharing behavior.

A Dynamic Hierarchical Framework for IoT-Assisted Digital Twin Synchronization in the Metaverse

Metaverse, also known as the Internet of 3-D worlds, has recently attracted much attention from both academia and industry. Each virtual subworld, operated by a virtual service provider (VSP), provides a type of virtual service. Digital twins (DTs), namely, digital replicas of physical objects, are key enablers. Generally, a DT belongs to the party that develops it and establishes the communication link between the two worlds. However, in an interoperable metaverse, data-like DTs can be “shared” within the platform. Therefore, one set of DTs can be leveraged by multiple VSPs. As the quality of the shared DTs may not always be satisfying, in this article, we propose an agile solution, i.e., a dynamic hierarchical framework, in which a group of Internet of Things devices in the lower level are incentivized to collectively sense physical objects’ status information and VSPs in the upper level determine synchronization intensities to maximize their payoffs. We adopt an evolutionary game approach to model the devices VSP selections and a simultaneous differential game to model the optimal synchronization intensity control problem. We further extend it as a Stackelberg differential game by considering some VSPs to be first movers. We provide open-loop solutions based on the control theory for both formulations. We theoretically and experimentally show the existence, uniqueness, and stability of the equilibrium to the lower level game and further provide a sensitivity analysis for various system parameters. Experiments show that the proposed dynamic hierarchical game outperforms the baseline.

Evolutionary dynamics of homeowners’ energy-efficiency retrofit decision-making in complex network

Understanding homeowners' energy-efficiency retrofit (EER) decision-making is a critical priority for reducing the adverse environmental impacts of the building sector and promoting a sustainable consumption transition. Existing research lacks attention to the dynamics and social interactions in the decision-making process of homeowner EER adoption. This paper applies the complex network-based evolutionary game approach with agent-based modeling to construct an evolutionary dynamics model for homeowners’ EER adoption decision-making. Through simulation experiments, this paper examines the effects of various key factors, including government incentives, retrofit costs, retrofit uncertainty, and network size, on the evolution of EER adoption. The results suggest that government incentives facilitate EER adoption, but their effects require a sufficiently long period of policy implementation and extensive social interaction to be realized. Reducing retrofit costs is a robust and effective way to encourage EER adoption, especially when uncertainty is high. Retrofit uncertainty has a significant impact on the adoption evolution. Increased uncertainty can hinder adoption decisions. In particular, the combination of high uncertainty and incentives is prone to lead to incentive failure. The increase in network size contributes to EER adoption, but attention needs to be paid to the impact of potential incentive redundancy in large-scale networks.