The Dynamics of Education Public Opinion Governance: An Evolutionary Game Theory Approach

In the context of rapid development of social networks, users gather together through social network platforms (SNPs) to form social circles, which become the main carriers for users to disseminate public opinion information and emotions, and it is easy to form the phenomenon of network public opinion polarization in social circles. To prevent the polarization of network public opinions in social circles, the application of blockchain technology to SNPs can provide a credible database for the traceability of public opinions and avoid the spread of false information in social circles. Based on blockchain traceability, this paper establishes a three‐party evolutionary game model consisting of government, SNPs, and social circle managers (SCMs). Through model solving and numerical simulation, the influencing factors of each party’s strategy are analyzed. The findings suggest: (1) Although the rising cost of building blockchain platforms may undermine the government’s incentive to regulate, the government will still prioritise support for their construction if they can significantly enhance social welfare. (2) The government could boost SNPs’ willingness to adopt blockchain technology through subsidy measures, but increased privacy risks could undermine this willingness. (3) Blockchain technology realizes the traceability of public opinion and improves the rational guidance tendency of SCMs. Therefore, in order to promote and strengthen the collaborative governance of network public opinion in social circles, SNPs should enhance the use of blockchain technology, while the government should give full play to its regulatory and supervisory roles. This study introduces blockchain technology for public opinion traceability; explores a collaborative network public opinion governance system based on strict regulation by the government, introduction of blockchain technology by SNPs, and rational guidance by SCMs; expands the related research on network public opinion governance; and provides a new idea for the study of network public opinion governance of social circles based on blockchain traceability.

The evolutionary game theory method has been widely used in the research works about different kinds of energy utilization fields, especially the clean energy utilizations which can facilitate the sustainable energy development. This paper presents a research review of the evolutionary game theory (EGT)-based studies on different energy-related aspects, including the traditional energy utilizations, energy saving and carbon emission reduction, new energy utilizations, new energy vehicles, electric power market, distributed energy systems, micro-grid, smart grid and energy storage. Typical research works based on the evolutionary game theory method and relevant algorithms are introduced and summarized. To promote the sustainable developments of energy utilization technologies as well as the usage of evolutionary game theory method, typical existing problems and several general recommendations about the further EGT-based research works are also proposed. The potential further works based on the evolutionary game theory approach may include the EGT-based studies considering more complex initial conditions and influential factors, verifications of the applicabilities and feasibilities of the evolutionary game theory method based on practical examples, research works with evolutionary game models consisting of more participants, and research and development (R&D) works of new EGT-based or hybrid algorithms.

In this paper, we study how an on-demand service platform could improve its performance through managing user conduct. In such a platform, service providers may reject certain platform-proposed service requests, and their responses, in turn, incentivize users to adjust their conduct. We develop an evolutionary game theory model of user conduct and provider responses that shows that the platform could improve user conduct through either setting a low wage for service providers or implementing priority matching. Building upon these results, we further model providers and users joining and leaving the platform by once again utilizing the evolutionary game theory approach. We find that wage setting alone is a blunt instrument to improve platform performance via managing user conduct, whereas supplementing the wage decision with priority matching could overcome its limitations and serve as an effective strategy to further improve platform performance in terms of growth and profitability. This finding suggests that matching prioritization could be an important strategy for managing platforms with user and provider heterogeneities. In addition, our analysis and results also demonstrate the potential of the evolutionary game theory approach for analyzing the impact of pricing and matching decisions on the performance of large markets. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4391 .

With the increasing global concern for environmental protection and sustainable development, the low-carbon transformation of the manufacturing industries has become a top priority. The rapid development of green digital technology (GDT) provides new opportunities and a strong impetus for the low-carbon transformation of the manufacturing industries. Meanwhile, green credit, as an important financial tool to promote the development of the green economy, plays a key role in guiding resource allocation. In order to respond to the urgent global demand for environmental protection and sustainable development and to accelerate the pace of the low-carbon transformation of manufacturing industries, based on evolutionary game theory, this paper constructs a three-party evolutionary game model of commercial banks (CBs), digital businesses (DBs) and manufacturing industries (MIs); further subdivides the MIs into two categories of non-polluting MIs and polluting Mis; and performs a numerical simulation using Python to analyze the influence of the main parameters on the evolutionary stabilization strategy. The results of the study are as follows: (1) Changes in the interest rate of the green credit have a greater impact on the strategic evolution process of polluting MIs than non-polluting MIs. The green credit model contributes to the introduction of GDT for the low-carbon transformation by non-polluting MIs, although for polluting MIs, the model hinders, to some extent, their introduction of GDT for the low-carbon transformation. (2) Polluting MIs are more sensitive to the investment cost of introducing GDT than non-polluting MIs. When the support benefits of GDT are too low, polluting MIs are more inclined to choose independent innovation to realize the low-carbon transition. (3) Government subsidies to DBs in terms of GDT innovation are crucial to the DBs’ strategy choices. High subsidies can significantly accelerate the cooperation process between DBs and Mis. The findings reveal the challenges and opportunities faced by both non-polluting and polluting manufacturing industries in the process of the low-carbon transformation. In addition, the study provides theoretical references for the behavioral decisions of commercial banks, digital businesses, and manufacturing industries, and proposes corresponding management suggestions to promote the sustainable development of the manufacturing industries.

In this paper, evolutionary game theory is used to study the boundary conditions of different stable states of knowledge alliances in distributed computing environments and the specific factors that influence the evolution of unstable alliances. The paper demonstrates the symbiotic relationship between members of a knowledge alliance and the applicability of evolutionary game methods, defines the boundary conditions of three different states of knowledge alliances (stable, unstable and dissolved), and reveals the determinants that lead unstable alliances to reach a stable state. This research provides a reference for the development of better distributed computing systems.

In this paper, authors offer one novel mathematical model of credit lending to customers based on evolutionary game theory, and the model presents an efficient and realistic approach. The purpose of the article is to examine the evolutionary game between banks and customers for granting facilities and credit. Authors assumed that customers are divided into two types. The first type of customers includes individuals or small and medium enterprises (SME), applying for microloans from the bank. The second type of customers includes corporate banking or large enterprises, applying for large loans from the bank. The relationship between the bank and the customers is a double-sided problem. Banks and customers may trust each other or want to behave opportunistically. The results show that the game has two equilibriums, and the optimal equilibrium, which is the best-case scenario, occurs when customers and bank players tending to keep “honest” and to “credit,” respectively. Authors used the evolutionary stable strategy to express the parameters that affect these interactions, and by adjusting some of these parameters, authors move the equilibrium towards the optimal solution of the game. Also, by adjusting these parameters, banks can gain more profitability.

Green sensitivity in supply chain management: An evolutionary game theory approach

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.

A wireless network without infrastructure is called MANET. In MANET, a node can move in any direction, at any speed, and at any time. There is no separate router on this network. Instead, the nodes act as a router. To transfer data in the network in an efficient manner, the network should be congestion-free or less congested. If the network has congestion, it would affect the performance of the network. In the routing process, if a node is aware of the neighbour node’s congestion and selects a congestion-free path, it leads to better performance of the network. In this paper, we have designed two approaches for improving the performance of the network. One is the cross-layer protocol and the other is the evolutionary game theory approach to find the least congestion path. Cross-layer involves transport layer and MAC layer of MANET to avoid congestion. The Linear Rank Selection method is one of the evolutionary game theory approaches used to find the Least Congested Node. We have enhanced this strategy in the GPSR protocol. The proposed EVO GPSR protocol performs 4.65% better throughput, 0.54% less network delay, 2.10% less routing overhead, and 8.20% better packet delivery ratio compared with GPSR protocol. Based on simulation, the performance of the proposed protocol is better than the GPSR protocol.

China’s rapid population ageing collides with a legacy of mid-rise walk-ups built without elevators, creating a persistent mobility barrier and a classic coordination problem within buildings. Using evolutionary game theory (EGT) this thematic review synthesizes evidence on how incentives, rules and learning dynamics shape cooperation over time and advances a proposed cooperative evolutionarily stable strategy (ESS) for elevator retrofitting. This thematic review examines how incentives, rules and learning dynamics shape cooperation over time through reviewing international and Chinese databases (2015–2025), screened studies using pre-specified inclusion/exclusion criteria and extracted variables on players, strategies, payoff construction and policy instruments as findings were synthesized via thematic analysis aligned to EGT constructs (players, strategies, payoff matrices, replicator dynamics and evolutionarily stable strategies). Four themes emerged including (i) asymmetric payoffs by floor position and negative externalities facing lower floors bias outcomes toward stalemate, (ii) calibrated government intervention including subsidy thresholds, majoritarian voting rules and transparent compensation reconfigures payoffs and enables coalition building, (iii) governance and implementation matter as empowered neighborhood committees, staged procedures and pay-per-use financing reduce transaction costs and household risk, (iv) diffusion effects are clear as visible completions raise perceived benefits and lower perceived risks in adjacent estates which accelerates adoption. The reviewed evidence identifies a gap in the monetary valuation of externalities, city-comparable compensation formulas and post-installation governance indicating the need for multi-stage models that extend beyond the vote. Based on identified finding’s, recommendations for future policies include bundling instruments rather than rely on a single lever, legislate lifecycle obligations (sinking funds tied to title), publish standard, floor-indexed cost-sharing and compensation schedules grounded in observable impacts, resource community intermediaries to mediate and verify and seed high-visibility demonstration projects to harness social learning making cooperation both attractive and durable.

Activation of oncogenes is one of the most critical factors which results in cancer. In this paper, activation of oncogenes due to the mutation is studied and then the effect of mutant cells on the neighborhood cells is investigated. In order to study the changes in amount of the mutant cells in tissue, at first, we consider the influence of mutation, barely; and the effect of mutant cells on the other cells and compartments around them after a specific time is modeled based on the evolutionary game theory approach. We have used replicator dynamics with onesided mutation to analyze the model. Furthermore, the impact of mutant cells on normal cells is studied by changing the mutation rate of them. Stability of the model are analyzed for different values of the game parameters. Simulation results effectively indicate the impact of mutant cells on enhancing the mutation rate of healthy ones.

As the global new energy vehicle (NEV) industry rapidly expands, the disposal and recycling of end-of-life (EOL) power batteries have become imperative. Efficient closed-loop supply chain (CLSC) management, supported by well-designed regulations and strategic investments, plays a crucial role in sustainable waste power battery recycling. In this study, an evolutionary game theory (EGT) methodology is used to construct a tripartite game model to investigate the interactions among manufacturers, recyclers, and the government to study the decision-making dynamics of green investments. In addition, numerical simulations are performed to evaluate the sensitivity of the relevant parameters on the stability of the evolution of the system. The results reveal that government green subsidies can stimulate early period investments in advanced recycling technologies. However, as the battery recycling industry matures, a ‘free-rider’ behavior emerges among enterprises, which can be mitigated through the imposition of a carbon tax. Eventually, as the industry reaches maturity, manufacturers and recyclers autonomously invest for enhanced profitability. This research provides valuable insights for government policy formulation, facilitating the formal recycling of retired batteries and fostering sustainability in the NEV sector.

Inactivation of alleles in tumor suppressor genes (TSG) is one of the most important issues that results in evolution of cancerous cells. In this paper, the evolution of healthy, one and two missed allele cells is modeled using the concept of evolutionary game theory and replicator dynamics. The proposed model also takes into account the interaction rates of the cells as designing parameters. Different combinations of the equilibrium points of the parameterized nonlinear system is studied and categorized to some cases. In each case, the interaction rates values are suggested in which, the equilibrium points are located in the appropriate location in the state space, such that, at least one of the desired equilibrium points are stable. Based on the suggested interaction rates, it is proved that the system doesn’t have any interior equilibrium point and will converge to one of the boundary equilibrium points. In addition, the proposed conditions for interaction rates guarantees that, when a trajectory of the system reaches to the boundaries, then it will converge to a desired equilibrium point.

Evolutionary behaviors regarding pricing and payment-convenience strategies with uncertain risk

Optimizing environmental regulation policies for digital transformation in energy enterprises in China: An evolutionary game theory approach