Networked Evolutionary Games Research Articles

Two-layer network evolutionary game model applied to complex systems

Two-layer network evolutionary game model applied to complex systems

Negative collaboration risk analysis and control in manufacturing service collaboration based on complex network evolutionary game

In the era of the digital economy, manufacturing service collaboration (MSC) efficiently meets the demands of user personalization and customization through networked collaboration among service providers. However, negative collaboration behaviors triggered by opportunism within the network can lead to risk generation, risk propagation and potential dysfunction of the entire collaboration network. Traditional risk control strategies are often based on experience and lack predictive validation. To improve the efficiency of risk control strategy formulation and validate the effectiveness of risk control, the article proposes a negative collaboration risk control optimal strategy exploration method. This method can realize effective optimal decision-making for risk control and validation of effectiveness by combining the complex network evolutionary game and Q-learning. The experiment results show that this method is able to formulate situation-specific risk control strategies and keep negative collaboration providers below 10%, effectively controlling MSC collaboration risk.

The transformative potential of blockchain technology in developing green supply chain: An evolutionary perspective on complex networks

Distrust is the main obstacle to the green transformation of the supply chain. Blockchain can rebuild trust among multiple participants by employing distributed information technologies. Based on complex network evolutionary game models, this paper tries to reveal the transformative potential of blockchain in promoting the green transformation of the supply chain. Simulation experiments with real electric vehicle data show that if adopting blockchain technology: i) the greenness of the supply chain can increase by 200% even without government policy intervention; by 75% when jointly optimizing the intensity of rewards and penalties; by 40% when exclusively adjusting the reward or penalty intensity; ii) the profits of both suppliers and manufacturers can be improved when they adopt green investment strategies, creating a “win-win” scenario. Besides, optimal government rewards and penalties are provided to accomplish various high levels of greenness in a blockchain supply chain network. This study not only quantifies the significant impact of blockchain technology on enhancing the greenness of supply chains, but also reveals the decision-making mechanisms of enterprises and government under varying degrees of greenness, providing theoretical guidance for enterprises and government to manage green blockchain supply chains.

The impact of energy supply side on the diffusion of low-carbon transformation on energy demand side under low-carbon policies in China

In order to promote the synergistic green and low-carbon development of energy supply-demand sides, this paper uses the complex network evolutionary game to explore the impact of supply side decision-making on the diffusion of low-carbon transformation on demand-side under low-carbon policies. The result shows that: (1) It is feasible for the government to use carbon trading as a policy tool to promote synergistic low-carbon transformation on supply-demand sides, but it is constrained by the carbon price trend. (2) Relying only on the direct supply of clean energy from the energy supply to demand side is not enough to promote synergistic development, and the government needs to guide the both sides to cooperate in the construction of renewable energy power plants. (3) When carbon price is on an upward trend, the government needs to push forward the low-carbon transformation process on supply side, and form the low-carbon transformation of demand side driven by supply side. (4) When carbon price is on a downward trend, the government needs to guide the supply side to bear part of the transformation cost pressure on the demand side, and at the same time give more transformation subsidies.

Research on water resources collaborative utilization based on complex networks: A case study of the Han River Basin

As China's social economy grows and the demand for water resources increases, the Han River Basin faces intensified supply-demand conflicts, reducing water resource utilization efficiency and threatening regional development. To address this, a network evolutionary game model based on complex network and evolutionary game theory was developed for collaborative water resource utilization. The study identified key dynamic factors influencing the game in the Han River Basin and analyzed its evolutionary rules. Simulations revealed that the profit-loss ratio and profit distribution rate significantly affect cooperative behavior. Furthermore, the network's topology exhibits small-world characteristics, and factors such as economic preferences, income distribution, and reward-punishment coefficients critically influence cooperation. Considering the current state of water resources and economic development, it is projected that comprehensive cooperation in the basin may take around 11 years to establish.

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.

Diffusion model of multi-agent collaborative behavior in public crisis governance network based on complex network evolutionary game

In order to explore the phenomenon of diffusion of group decision making formed by the emergence of decision-making behaviors of governance agents in public crisis governance systems, this research uses a complex network evolutionary game approach, considers BA scale-free networks as network vectors of public crisis governance systems, and develops a diffusion model of collaborative governance decision making behaviors. Simulation experiments are also conducted to show the macro-level impact of micro-subjects’ decision-making behavior on group “Emergence-Diffusion”. The results of this study show that the cost of collaborative governance has the most significant effect on the depth and breadth of the spread of collaborative behavior in governance networks. The size of the network determines the speed of network diffusion. The smaller the network size, the more sensitive it is to the spillover benefits of collaborative governance, and the larger the network size, the more sensitive it is to the penalties of non-compliance. The findings of the study have implications for the collaborative behavior of multiple agents in public crisis governance. The main findings are that (1) in order to ensure the stability of the collaborative governance system, decision making options should be selected according to the size of the network. (2) A reasonable penalty mechanism for breach of contract should be set up to avoid the phenomenon of “free-riding” in collaborative governance. (3) Reasonable allocation of collaboration benefits and maintenance of cooperative relationships between nodes in the neighborhood. (4) External regulators should ensure that information in the network is disseminated without barriers and reduce the phenomenon of information asymmetry.

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.

Aggregation Method to Strategy Consensus of Large-Size Networked Evolutionary Matrix Games

Aggregation Method to Strategy Consensus of Large-Size Networked Evolutionary Matrix Games

An evolutionary analysis of the diffusion of low-carbon technology innovation in supply networks

In an era increasingly defined by the urgent need to address energy sustainability and environmental degradation, the advancement of low-carbon technologies stands at the forefront of global governmental agendas. Despite China's pioneering efforts in the adoption and development of low-carbon initiatives, its status as the preeminent global carbon emitter highlights the significant challenges impeding the widespread adoption of these innovations. This study delves into these complexities by employing a sophisticated complex network evolutionary game model that intricately weaves together consumer preferences and carbon pricing as pivotal determinants of utility. By examining the influence of corporate strategic decisions on the diffusion of low-carbon technologies, our research provides an insightful micro-level analysis that diverges from the macro-level orientation typically dominating the field. This nuanced approach not only sheds light on the mechanics of low-carbon technology dissemination but also contributes to a more comprehensive understanding of how to effectively navigate and surmount the barriers to low-carbon technological progress. Through this exploration, our study seeks to augment the discourse on the propagation of low-carbon technologies, offering vital insights for policymakers, industry stakeholders, and the broader academic community on fostering a more sustainable and low-carbon future.

The Dynamics of Community Engagement in Distance Education: A Sociological Analysis Based on Online Learning Platforms

The rapid advancement of information technology has redefined distance education as a fundamental component of modern education systems. The extensive deployment of online learning platforms has further propelled this transformation, catalyzing innovation in educational methodologies while simultaneously presenting novel challenges and demands in the realm of community engagement. In the sphere of online learning, considerable research has been conducted on the efficacy of these platforms. However, studies specifically dedicated to the effective cultivation and maintenance of community engagement through these platforms are notably scarce. Recognized as pivotal for educational outcomes, student satisfaction, and enduring academic success, community engagement within the context of distance education warrants comprehensive exploration. This study delves into this exploration by developing a dynamic model of interaction and a coupled network evolutionary game model that incorporates the nuances of social group dynamics. Initiating with a critical review of existing literature on community engagement in distance learning, the study identifies prevalent limitations. These limitations include an over-reliance on qualitative data, the absence of dynamic analyses, and an oversight of the intricacies of group interactions. To bridge these gaps, we propose a data-driven interaction dynamics model tailored for online learning platforms. Additionally, we suggest a network evolutionary game model that considers the interplay among social groups. These models collectively deepen our understanding of the evolution of community engagement over time and elucidate how both individual and collective behaviors influence the communal health of online learning environments.

Minimum cost control of weighted networked evolutionary games with switched topologies and threshold

Networked evolutionary games (NEGs) are a class of models that capture the interactions and evolution of strategies among rational agents in a network. In this paper, we study the problem of minimum cost control of NEGs with switched topologies and threshold, where the network structure can change over time and the agents have a minimum payoff requirement to survive. Using the semi-tensor product (STP) of matrices, we express the weighted networked evolutionary game in an algebraic form. Then, we propose an efficient algorithm to design the minimum cost control that ensures the survival of the agents. Finally, we demonstrate the validity of our theoretical results with an example and simulations.

A network perspective on tourism waste management: An evolutionary game analysis of stakeholders' participation in the Third Pole region

There is a striking absence of complex network models and extensive analysis of behavioral factors in the field of waste management research. Moreover, research on effective collaboration among different stakeholders in tourism waste management in the Third Pole region is currently lacking, despite the increasing involvement of various groups. Therefore, this paper aims to explore the cooperative relationship between multiple stakeholders in tourism waste management using the Lulang Scenic Area as a case study. A field investigation and complex network evolutionary game simulation were used to identify the cooperation dynamics between multiple stakeholders. The results indicate the following. (1) The stakeholder network characterized by small-world dynamics has efficient information diffusion and cooperation. (2) A threshold value for positive externalities and returns on investment exists, beyond which stakeholders are more inclined to participate in sustainable waste management. (3) Incentives below the threshold attract direct stakeholders in waste generation and management, whereas incentives above it draws in a broader range of stakeholders, primarily indirect stakeholders. (4) A critical mass of cooperative stakeholders tends to create a demonstration effect that encourages additional participation, supporting the growth of cooperative networks in tourism waste management. This paper innovatively shows the potential of a cooperation strategy between multiple stakeholders strengthened by appropriate incentives.

A dynamic incentive mechanism for data sharing in manufacturing industry

Data sharing is a critical component in a blockchain traceability platform. Therefore, creating a reasonable incentive mechanism to ensure that all enterprises participate in data sharing is vital for blockchain platforms. Currently, many researchers employ evolutionary game theory to analyze problems related to data sharing. However, evolutionary game theory typically assumes that the population composed of enterprises is mixed uniformly. Enterprises in the manufacturing industry are not uniformly mixed, as they tend to have specific connections with each other due to the size of enterprises and volume of business. Therefore, a networked evolutionary game is introduced to solve this problem. Firstly, an incentive model for enterprises sharing data is established. Then, a scale-free network is employed to simulate the connections between enterprises. To comprehensively consider the individual and group benefits of enterprises in the game, this study designs a strategy update rule for networked evolutionary game based on Discrete Particle Swarm Optimization and Variable Neighborhood Descent algorithm. To tackle the challenge of determining reasonable incentive values in networked evolutionary games, this study proposes a dynamic incentive mechanism based on the Q-Learning algorithm. Finally, the experiments indicate that this method can successfully facilitate the stable involvement of enterprises in data sharing.

Network evolutionary game analysis of green credit: A perspective of carbon emissions trading

AbstractAs an effective measure for the local government to promote reduction of corporate carbon emissions and sustainable development of social economy, the implementation of green finance policies in enterprises and financial institutions has encountered severe strikes from the low returns of low carbon production and low interest rates of green credit business. Under the premise of liberalizing carbon emissions trading, a triple‐level network evolutionary game consisting of a local government, banks, and enterprises is constructed to analyze response behavior and strategy selection mechanism to green credit. The impacts of management tools are investigated in terms of setting carbon market price, limiting the financing upper bound of misbehaving enterprises, limiting the emissions quota of enterprises for fraudulent green credit, and rewarding clean project development on the decisions of individuals in the network. Results show the following: First, increasing the trading price of carbon emissions has a catalytic effect on enhancing the activeness of banks and enterprises to follow green finance policies, but the autonomous regulation ability of which is limited. Second, reducing the funding ceiling for emitting emissions illegally ambiguates enterprises' preferences for production strategies and discourages them from undertaking low‐carbon projects. Third, excessive restrictions on the share of emissions may evoke regulatory inaction by the local government, consequently leading to instability in the evolution network. Fourth, granting reward for clean production is instrumental for the network to promote green finance policies, but exorbitant subsidies increase the fiscal burden of the local government with no significant effect on the node selection of environment‐friendly strategies.

An evolutionary mechanism of social preference for knowledge sharing in crowdsourcing communities

Crowdsourcing community, as an important way for enterprises to obtain external public innovative knowledge in the era of the Internet and the rise of users, has a very broad application prospect and research value. However, the influence of social preference is seldom considered in the promotion of knowledge sharing in crowdsourcing communities. Therefore, on the basis of complex network evolutionary game theory and social preference theory, an evolutionary game model of knowledge sharing among crowdsourcing community users based on the characteristics of small world network structure is constructed. Through Matlab programming, the evolution and dynamic equilibrium of knowledge sharing among crowdsourcing community users on this network structure are simulated, and the experimental results without considering social preference and social preference are compared and analysed, and it is found that social preference can significantly promote the evolution of knowledge sharing in crowdsourcing communities. This research expands the research scope of the combination and application of complex network games and other disciplines, enriches the theoretical perspective of knowledge sharing research in crowdsourcing communities, and has a strong guiding significance for promoting knowledge sharing in crowdsourcing communities.

Research on the diffusion of green innovation behavior based on complex network evolutionary game

AbstractThe strategic choice of green innovation subjects and the evolution of green innovation networks have a two‐way feedback effect. This study aims to construct a comprehensive framework for analyzing the evolution of green innovation networks from micro to macro levels. The gravity model demonstrates the structural complexity of green innovation networks. Additionally, an evolutionary game model based on green innovation networks is constructed to discuss the network's diffusion mechanism of green innovation behavior and its influence on network characteristic indicators. The research findings reveal that (1) the green innovation networks in China exhibit a continuous growth trend but the connectivity of urban nodes varies significantly; (2) the green innovation network's marginal nodes are the “vulnerable groups” in the evolutionary game and the active green innovation strategy cannot be completely spread; and (3) the cost of passive green innovation and the demand for green consumption are the internal and external driving forces that promote the spread of the active green innovation strategy in the network. Finally, this study concludes with several suggestions, including the establishment of a mechanism to promote green innovation reform with the city, industry, and enterprises as the node, fulcrum, and focus, respectively. Additionally, it suggests increasing domestic demand for green consumption. These findings and suggestions can provide a reference for the development of green innovation in other countries or regions.

Exploring the evolutionary mechanism of the cross-regional cooperation of construction waste recycling enterprises: A perspective of complex network evolutionary game

Cooperation and information sharing among various regional departments play a crucial role in facilitating the equitable disposal of construction waste across regions, fostering regional coordination in waste management. This paper establishes the complex network evolutionary game model of cross-regional cooperation of construction waste recycling enterprises, and conducts numerical simulation to analyze the strategic adjustment process of recycling enterprises under the coordination and complementarity of market mechanism and government mechanism, and explores the Pareto optimal conditions that are conducive to cross-regional cooperation of recycling enterprises. The result indicates that with the increase of the market scale of cross-regional treatment of construction waste, it can effectively improve the willingness of cross-regional cooperation of recycling enterprises. In the market mechanism, both the profit distribution and synergistic benefit have a significant impact on the cross-regional cooperation behavior of construction waste recycling enterprises, while the impact of spillover benefit is relatively minor in comparison. In various network scales, the profit distribution coefficients exhibit threshold values within the ranges of 0.3–0.35, 0.25 to 0.3, and 0.25 to 0.3, respectively. Within the government mechanism, the government subsidy coefficient falls within the threshold of 0.3–0.4. Notably, greater government support proves more conducive to advancing cross-regional cooperation among recycling enterprises. For small-scale and medium-scale markets, there exists a threshold for default penalties ranging between 4 and 5. In contrast, large-scale markets have a default penalty threshold between 3 and 4. Imposing reasonable penalties for contract breaches can effectively stimulate the sustainable and consistent development of cross-regional cooperation among recycling enterprises. These findings establish a foundation for shaping cross-regional cooperation strategies for recycling enterprises, enhancing the efficiency of construction waste treatment, and fostering the coordinated development of urban areas.

Weighted Nash equilibrium of incomplete‐profile networked evolutionary games with multiple payoffs

AbstractThis paper investigates the existence and convergence of weighted Nash equilibrium for incomplete‐profile networked evolutionary games with multiple payoffs. First, the incomplete‐profile networked evolutionary game under probabilistic myopic best response adjustment rule is transformed into an algebraic form based on the semi‐tensor product of matrices. Second, a method for calculating weighted Nash equilibrium is presented, and the relationship between weighted Nash equilibrium and positive‐probability fixed point is derived. Furthermore, a criterion is provided to verify whether the profiles in the feasible profile set can converge to the set of weighted Nash equilibriums with probability one. Finally, an illustrative example is given to support the new results obtained in this paper.

Synergistic effects of carbon cap-and-trade and renewable portfolio standards on renewable energy diffusion

Carbon cap-and-trade mechanisms (CCT) and renewable portfolio standards (RPS) have been identified as effective administrative instruments for steering the conduct of electricity generation and promoting the adoption of renewable energy sources. However, the synergistic effects of these policies are often overlooked, and static implementation can limit their effectiveness. To address these gaps, this study employs a dynamic CCT and RPS framework, utilizing complex network evolutionary game theory to examine their influence on renewable energy diffusion. The findings reveal a distinct synergy between CCT and RPS in enhancing renewable energy diffusion, although these policies may occasionally result in redundancy or conflict. A static CCT combined with a dynamic RPS demonstrates greater efficacy in promoting renewable energy diffusion, while a dynamic CCT does not consistently increase renewable energy adoption. Additionally, renewable energy quota standards facilitate higher levels of diffusion. In perfectly competitive markets, renewable energy diffusion is more effective than in monopolistic environments. These insights offer valuable policy suggestions for stimulating renewable energy diffusion and expediting high-quality, low-carbon transitions.