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This paper constructs a tripartite evolutionary game model between the government, the core enterprises of film copyright export and imports and uses the system dynamics model to simulate and find the optimal selection results of single and mixed government incentives under dynamic changes, aiming to promote the development of foreign trade of film copyright and innovation and development of the film industry so as to improve the overall social benefits of the film industry and provide policy enlightenment for enhancing the import power of foreign core enterprises to introduce domestic film copyrights.

In this paper, a tripartite evolutionary game model of the government, the core enterprises of film copyright export and imports is constructed, the evolution process of cooperation strategy is derived, the impact of innovation income coefficient, mixed incentive policy and single incentive policy on the evolution results is analyzed, and the system dynamic model is used to simulate to find the optimal selection results of single and mixed government incentives under dynamic changes, so as to provide reference for the government’s dynamic incentive decision-making.

The results show that export-oriented core firms are more sensitive to mixed incentives, while import-oriented core firms respond more quickly to single incentives. The large innovation income coefficient has a negative impact on the willingness of import-oriented core enterprises to cooperate. The study proposes measures to increase the willingness of core companies to participate.

Due to the fact that numerical simulation is based on simulation, there may be a certain gap between it and the actual situation. Therefore, it is necessary to further use actual data to conduct empirical analysis on the theoretical model.

This article mainly focuses on analyzing the impact of strategy choices and related parameters of various entities on the incentive mechanism and studying the foreign trade cooperation strategies of film copyright export enterprises under policy support from a theoretical model perspective. Furthermore, research has proven that in order to effectively enhance the willingness of foreign import core enterprises to participate in the foreign trade of domestic film copyrights, the government needs to coordinate the use of single incentive policies and mixed incentive policies. This study provides a major contribution for policymaker to develop film copyright import and export trade.

Based on the research conclusions, this paper puts forward management countermeasures to further improve the development of the film copyright import and export trade. The first is to enrich government incentive methods and stimulate the vitality of film copyright and foreign trade market entities. The second is to guide the core enterprises of film copyright export to increase investment in innovation and stimulate the endogenous driving force of industrial development. Finally, lengthen the foreign trade industry chain of film copyright and increase the income of film derivatives.

Firstly, this paper applies the research methods of evolutionary game and system dynamics simulation to the field of foreign trade research on film copyright and expands the research perspectives and methods of the film industry. Secondly, by analyzing the “cost-benefit incentive” relationship of the evolutionary game of government export-oriented core enterprises and importing core enterprises, an evolutionary game model is constructed, the quantitative point of tripartite interest decision-making is solved and the research object of the evolutionary game method is expanded. Finally, the system dynamics model is used to simulate and find the optimal selection results of single and mixed government incentives under dynamic changes, so as to provide reference for the government’s dynamic incentive decision-making.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

The operation of water environment treatment Public-Private Partnership projects (WETP-PPP) is crucial to the project effectiveness. However, there are often problems in projects that attach importance to construction and neglect operation management, which seriously affect the project operation effect. To ensure the good operation effect of the WETP-PPP, an evolutionary game model of the regulation strategy during the operation period of WETP-PPP is constructed.

An evolutionary game model of regulation is established which considers the government, the project company and the public in water environment treatment Public-Private Partnership projects (WETP-PPP). Five scenarios of equilibriums and the game's evolutionary stable strategies are analyzed, and the corresponding stability conditions are then obtained. Finally, through the simulation, the influence of different factors on the choice of the three-party strategy is analyzed.

First, the key factors that affect the evolution game are the regulation costs and performance rewards of the government, the project company's operation costs and penalties for opportunism and the public supervision costs and rewards. Second, in order to ensure the operation effect, the government needs the performance incentive from the superior government. Third, the public's supervision enthusiasm needs to be mobilized by the government. Last, the penalty strength of speculative operation should be strong enough to play a deterrent role.

The theoretical research in this paper has some limitations. Initially, due to the large number of participants in WETP-PPP, in addition to the government department, the project company and the public studied in this paper, it also involves the consulting industry and financial institutions. In the future, more participants can be added to form four-party interest relationships and conduct four-party evolutionary game research. Second, the operation environment of WETP-PPP is complex and changeable, and various influencing factors are intertwined, the number of parameters involved in this paper is limited, and further detailed research is needed in the future.

Based on the evolutionary game theory, this article discusses the evolution law of the tripartite game behavior of the government department, the project company and the public, which is helpful to clarify the strategy evolution path of the tripartite in the WETP-PPP, and the generation condition and evolution mechanism of the equilibrium strategy of the tripartite game. The key parameters affecting the tripartite strategy selection are analyzed through simulation, which can provide reference for the government department to formulate relevant measures. At the same time, it broadens the application field of evolutionary games and supplements the research on the management mechanism of WETP-PPP during the operation period.

Based on the evolutionary game theory, this paper introduces the supervision behavior of the public, which can provide a new perspective for researchers to conduct relevant research. Secondly, for the regulation during the operation of WETP-PPP, this paper can provide reference for the government department to establish a scientific public supervision system, improve the government supervision mechanism and other relevant measures, which can help promote the public supervision willingness, improve the regulation efficiency of the government and guide the project company to reduce speculation, so as to ensure the effect of water environment management.

This paper focuses on the regulation of WETP-PPP during the operation period to research interactions among the government, the project company and the public. Based on the analysis of the evolutionary game, some suggestions are put forward, such as perfecting the government regulation mechanism, optimizing the reward and punishment system for the project company and broadening the channels of public supervision. The research results of this paper can provide support for the government's regulation of WETP-PPP and ensure the project operation effect.

This study advocates the importance of taking an evolutionary perspective in the strategic configuration of closed-loop supply chains (CLSC) in the transition to a circular economy. Building on the supply chain management and industrial dynamics research domains, an evolutionary analytical framework was developed and applied in the empirical context of the ongoing industrial transition to e-mobility.

This study is designed as an in-depth exploratory case study to capture the multi-layer dynamic complexities and their interplay in CSLC development. The empirical investigation was based on two-year interactions between the authors and various departments in a leading European heavy vehicle manufacturer. The proposed evolutionary analytical framework was used for investigating the dynamics of four CLSC configurations through ten possible trajectories.

The findings demonstrate that the evolution of each CLSC configuration comes with multiple challenges and requirements and point out the necessity for the co-development of technologies, product design and production, and infrastructure through long-term relationships among key supply chain actors. However, this evolutionary journey is associated with multiple dilemmas caused by uncertainties in the market and technology developments. All these factors were properly captured and critically analyzed, along with their interactions, thanks to the constructs included in the proposed evolutionary analytical framework.

The proposed evolutionary framework is applicable for examination of SC transformation in the context of market and technology development, and is particularly relevant for transitioning from linear SC to CLSC. The framework offers a single actor perspective, as it does not directly tackle dynamics and effects of actions taken by SC actors.

The developed framework can support SC managers in identifying, framing, and comparing alternative strategies for CLSC configuration in the transition process.

This study proposes the framework for understanding and guiding the evolutionary process of CLSC development. Its uniqueness lies in the integration of concepts from innovation and evolutionary theories coming from industrial dynamics and SCM literature streams.

This paper aims to propose the mechanism of cross-network effect embedded, which can help cross-border e-commerce (CBEC) platforms strengthen cooperative relationships with sellers more equitably and effectively by using the network structural characteristics of the platforms themselves.

A two-stage evolutionary game model has been used to confirm the influence factors. The mathematical derivation of evolutionary game analysis is combined with the simulation method to examine the role of cross-network effect in cooperation. The evolutionary game model based on the cross-network effect is proposed to achieve better adaptability to the study of cooperation strategy from the two-sided market perspective.

The evolutionary game model captures the interactions of cross-network effect and the influence factors from a dynamic perspective. The cross-network effect has a certain substitution on the revenue-sharing rate of SMEs. CBEC platforms can enhance the connection between consumers and the website by improving the level of construction, which is a good way to attract sellers more cost-effectively and efficiently.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specificCBEC platforms.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specific CBEC platforms.

Investigations that study cooperation strategy from the cross-network effect perspective in CBEC are limited. The research figured out which influence factors are affected by the cross-network effect in cooperation. A two-stage evolutionary game model was proposed to explain the interaction of the factors. The evolutionary game analysis with a simulation method was combined to highlight the role of cross-network effect on cooperation strategy to give a deeper investigation into the sustainable cooperation ofCBEC.

Although green logistics has become a new focus of cooperation between government and enterprises under environmental constraints, how local governments formulate subsidy policies to effectively guide the green transformation of regional logistics and how to facilitate the reasonable cost-sharing are rather critical. This paper will deeply explore the dynamic process of the tripartite participation (government, platform, and logistics enterprises) in the selection of regional green logistics strategy, and reveal the evolutionary game relationship of the three parties.

To explore the dynamics involving the government, platform and logistics enterprises for the green logistic transformation, and reveal the evolutionary gaming among the three parties, based on the bounded rationality premise, this study constructs the tripartite asymmetric evolutionary game models, uses the stability theorem of differential equation to explore the evolution and stability strategy of the system in different cases and explicates the paths of influence on the tripartite behaviors via simulations.

Results of this study indicate that there exist stable equilibrium strategies among the three parties regarding the regional green logistics, and they are affected by different factors. The government's subsidy, subsidy intensity and the platform's cost-sharing proportion can generate positive effects, but the latter two can also impact negatively beyond the effective ranges. The findings provide a theoretical basis for local governments, platforms and logistics enterprises to formulate justifiable subsidy intensity and determine reasonable sharing proportion.

Firstly, considering the significant relevance of local government, it is included in the evolution model, and the tripartite game (among government, platform and enterprises) is explored; Secondly, by comparing the equilibrium results under different game conditions, this paper analyzes the evolution of each party's game strategy to achieve the optimal return under bounded rationality and the important factors determining the strategic selection; Finally, the key factor of platform cost sharing is involved, and to what extent the change of platform cost sharing ratio will influence the systematic stability is explored.

Green innovation in supply chains is crucial for socioeconomic development and stability. Factors that influence collaborative green innovation in the supply chain are complex and diverse. Exploring the main influencing factors and their mechanisms is essential for promoting collaborative green innovation in supply chains. Therefore, this study analyzes how upstream and downstream enterprises in the supply chain collaborate to develop green technological innovations, thereby providing a theoretical basis for improving the overall efficiency of the supply chain and advancing green innovation technology.

Based on evolutionary game theory, this study divides operational scenarios into pure market and government-regulated operations, thereby constructing collaborative green innovation relationships in different scenarios. Through evolutionary analysis of various entities in different operational scenarios, combined with numerical simulation analysis, we compared the evolutionary stability of collaborative green innovation behavior in supply chains with and without government regulation.

Under pure market mechanisms, the higher the green innovation capability, the stronger the willingness of various entities to collaborate in green innovation. However, under government regulation, a decrease in green innovation capability increases the willingness to collaborate with various entities. Environmental tax rates and green subsidy levels promote collaborative innovation in the short term but inhibit collaborative innovation in the long term, indicating that policy orientation has a short-term impact. Additionally, the greater the penalty for collaborative innovation breaches, the stronger the intention to engage in collaborative green innovation in the supply chain.

We introduce the factors influencing green innovation capability and social benefits in the study of the innovation behavior of upstream and downstream enterprises, expanding the research field of collaborative innovation in the supply chain. By comparing the collaborative innovation behavior of various entities in the supply chain under a pure market scenario and government regulations, this study provides a new perspective for analyzing the impact of corresponding government policies on the green innovation capability of upstream and downstream enterprises, enriching theoretical research on green innovation in the supply chain to some extent.

The paper aims to elucidate effective strategies for promoting the adoption of green technology innovation within the private sector, thereby enhancing the value of public–private partnership (PPP) projects during the operational phase.

Utilizing prospect theory, the paper considers the government and the public as external driving forces. It establishes a tripartite evolutionary game model composed of government regulators, the private sector and the public. The paper uses numerical simulations to explore the evolutionary stable equilibrium strategies and the determinants influencing each stakeholder.

The paper demonstrates that government intervention and public participation substantially promote green technology innovation within the private sector. Major influencing factors encompass the intensity of pollution taxation, governmental information disclosure and public attention. However, an optimal threshold exists for environmental publicity and innovation subsidies, as excessive levels might inhibit technological innovation. Furthermore, within government intervention strategies, compensating the public for their participation costs is essential to circumvent the public's “free-rider” tendencies and encourage active public collaboration in PPP project innovation.

By constructing a tripartite evolutionary game model, the paper comprehensively examines the roles of government intervention and public participation in promoting green technology innovation within the private sector, offering fresh perspectives and strategies for the operational phase of PPP projects.

Nucleic acid testing is an effective method of accurate prevention and control and a key measure to block the spread of the epidemic. However, the fraud in nucleic acid testing occurred frequently during epidemics. This paper aims to provide a viable scheme for the government to strengthen the supervision of nucleic acid testing and to provide a new condition for the punishment for the negative act of the government and the upper limit of the reward for nucleic acid testing institution of no data fraud.

This paper formulates an evolutionary game model between the government and nucleic acid testing institution under four different mechanisms of reward and punishment to solve the issue of nucleic acid testing supervision. The authors discuss the stability of equilibrium points under the four distinct strategies and conduct simulation experiments.

The authors find that the strategy of dynamic reward and static penalty outperforms the strategies of static reward and static penalty, dynamic reward and static penalty, static reward and dynamic penalty, dynamic reward and dynamic penalty. The results reveal the appropriate punishment for the negative act of the government can enhance the positivity of the government's supervision in the strategy of dynamic reward and static penalty, while the upper limit of the reward for nucleic acid testing institution of no data fraud cannot be too high. Otherwise, it will backfire. Another interesting and counterintuitive result is that in the strategy of dynamic reward and dynamic penalty, the upper limit of the penalty for data fraud of nucleic acid testing institution cannot be augmented recklessly. Otherwise, it will diminish the government's positivity for supervision.

Most of the existing evolutionary game researches related to the reward and punishment mechanism and data fraud merely highlight that increasing the intensity of reward and punishment can help improve the government's supervision initiative and can minimize data fraud of nucleic acid institution, but they fall short of the boundary conditions for the punishment and reward mechanism. Previous literature only study the supervision of nucleic acid testing qualitatively and lacks quantitative research. Moreover, they do not depict the problem scenario of testing data fraud of nucleic acid institution regulated by the government via the evolutionary game model. Thus, this study effectively bridges these gaps. This research is universal and can be extended to other industries.

Team social capital (TSC) has been attracting increasing research attention aiming to explore team effectiveness through within- and cross-team resource conduits. This study bridges two disconnected theories – TSC and evolutionary theory – to examine gaming clans and analyzes mechanisms of the clans' TSC building from an evolutionary perspective.

This research draws longitudinal data from a sample of gaming teams (N = 1,267) from anonymized player data from the game World of Tanks spanning 32 months. The authors explored teams' evolutionary patterns using hidden Markov models and applied longitudinal multilevel modeling to test hypotheses.

The results showed that teams of different sizes and levels of evolutionary fitness vary in team closure and bridging social capital. The authors also found that larger teams are more effective than smaller ones. The positive association between team-bridging social capital and effectiveness is more substantial for smaller teams.

This research advances the theoretical development of TSC by including the constructs of teams' evolutionary status when analyzing strategic social capital building. Adding to existing literature studying the outcome of TSC, this research also found a moderating effect of team size between TSC and effectiveness. Finally, this study also contributes to a longitudinal view of TSC and found significant evolutionary patterns of teams' membership, TSC, and effectiveness.