Evolutionary Game Theoretic Approach Research Articles

Coordination of interests between local environmental protection departments and enterprises under China’s environmental regulation policies: an evolutionary game theoretical approach

The Central Inspection for Ecological and Environmental Protection (CIEEP) and Environmental Vertical Management Reform (EVMR) policies have reshaped the relationships among stakeholders and the power structure in local environmental governance systems. To reveal the current local environmental collaborative governance mechanism, this study placed CIEEP and EVMR policies within the same research framework and considered multiple governance entities, including the central government, higher-level environmental protection departments, local environmental protection departments, enterprises, and the public, employing evolutionary game theory and numerical simulation techniques to explore the boundary conditions, key influencing factors, influence mechanisms, and realization paths of local environmental collaborative governance from a dynamic process perspective. The results show that: 1) The cost of enterprise environmental behavior, intensity of reward and punishment for enterprise environmental behavior, government subsidies, supervision cost of local environmental protection departments, and intensity of reward and punishment for local environmental protection departments all have important effects on local environmental governance; 2) Compared with the cost of enterprise environmental behavior, enterprise environmental behavior is more sensitive to changes in supervision cost of local environmental protection departments, and the intensity of reward and punishment for enterprise environmental behavior has the same major impact on enterprise environmental behavior as that of local environmental protection departments; 3) The influence of CIEEP policy on enterprise environmental behavior is unstable, showing signs of strategic response. Therefore, local environmental governance systems should be further optimized by encouraging local environmental protection departments to actively supervise and designing differentiated policy tools. This study provides a reference for solving local environmental governance problems under current environmental regulation policies.

An evolutionary game-theoretic approach to analysis the green innovation chain dynamics under government policies

Continuous improvement of the green innovation chain is essential for promoting sustainable entrepreneurship and addressing environmental issues. In this paper, we developed a tripartite evolutionary game model involving tech firms, startups, and accelerators. Our goal is to examine the impact of both static and dynamic government interventions on the evolution of the green innovation chain. After determining the evolutionary stable strategies, we examine government interventions through a system dynamics simulation using three different scenarios: (1) Government support downstream of the innovation chain, (2) government support upstream of the innovation chain, and (3) government support throughout the chain. In the first scenario, it is observed that although it promotes green technology innovation (GTI), the necessary support for startups and accelerators to scale up green innovations is lacking. Consequently, the overall level of GTI is limited. Conversely, in the second scenario, there is a lack of incentives for tech firms to invest in GTI, resulting in a stagnation in the innovation chain. Nevertheless, with government support extended throughout the chain, a synergistic effect emerges between all players, leading to a higher level of collaboration and resulting in a more efficient and effective green innovation chain.

The evolutionary stability of partial migration with Allee effects

An Allee effect is a density-dependent phenomenon in which population growth or individual components of fitness increase as population density increases. Understanding the density-dependent effects is vital to elucidate how populations evolve and to investigate evolutionary stability. Partial migration, where a proportion of a population migrates while other individuals remain resident, is widespread across most migratory lineages. However, the mechanism still needs to be better understood in most taxa, especially those experiencing positive density-dependent effects. Here we investigate the evolutionary stability of a partial migration population with only the migrant population experiencing Allee effects. Using the Evolutionary Game Theoretic (EGT) approach, we prove the existence and uniqueness of an evolutionary stable strategy (ESS). EGT provides a mathematical framework for understanding and modelling Darwinian evolution by natural selection. We also show that the ESS is the only Ideal Free Distribution (IFD) that arises in the context of a partially migrating population in a two-habitat environment.

An evolutionary game-theoretic approach for analysis of green innovation and environmental performance of tech firms under stakeholders’ policies based on system dynamics

An evolutionary game-theoretic approach for analysis of green innovation and environmental performance of tech firms under stakeholders’ policies based on system dynamics

Retracted: Explore the Conflict and Resolution of Pollution NIMBY Facility Construction in the Context of New Media: An Evolutionary Game Theoretical Approach

Retracted: Explore the Conflict and Resolution of Pollution NIMBY Facility Construction in the Context of New Media: An Evolutionary Game Theoretical Approach

Distributed dynamic task allocation for unmanned aerial vehicle swarm systems: A networked evolutionary game-theoretic approach

Task allocation is a key aspect of Unmanned Aerial Vehicle (UAV) swarm collaborative operations. With an continuous increase of UAVs’ scale and the complexity and uncertainty of tasks, existing methods have poor performance in computing efficiency, robustness, and real-time allocation, and there is a lack of theoretical analysis on the convergence and optimality of the solution. This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios. A task allocation model is designed with the local utility of an individual and the global utility of the system. Then, the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game. Additionally, a PayOff-based Time-Variant Log-linear Learning Algorithm (POTVLLA) is proposed, which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter. The former aims to reduce the system’s computational burden and enhance the individual’s effectiveness, while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one. Numerical simulation results show that the approach is optimal, robust, scalable, and fast adaptable to environmental changes, even in some realistic situations where some UAVs or tasks are likely to be lost and increased, further validating the effectiveness and superiority of the proposed framework and algorithm.

An Evolutionary Game-Theoretic Approach to Unmanned Aerial Vehicle Network Target Assignment in Three-Dimensional Scenarios

Target assignment has been a hot topic of research in the academic and industrial communities for swarms of multiple unmanned aerial vehicle (multi-UAVs). Traditional methods mainly focus on cooperative target assignment in planes, and they ignore three-dimensional scenarios for the multi-UAV network target assignment problem. This paper proposes a method for target assignment in three-dimensional scenarios based on evolutionary game theory to achieve cooperative targeting for multi-UAVs, significantly improving operational efficiency and achieving maximum utility. Firstly, we construct an evolutionary game model including game participants, a tactical strategy space, a payoff matrix, and a strategy selection probability space. Then, a multi-level information fusion algorithm is designed to evaluate the overall attack effectiveness of multi-UAVs against multiple targets. The replicator equation is leveraged to obtain the evolutionarily stable strategy (ESS) and dynamically update the optimal strategy. Finally, a typical scenario analysis and an effectiveness experiment are carried out on the RflySim platform to analyze the calculation process and verify the effectiveness of the proposed method. The results show that the proposed method can effectively provide a target assignment solution for multi-UAVs.

An evolutionary game-theoretic approach for investigating the long-term behavior of the industry sector for purchasing renewable and non-renewable energy: A case study of Iran

Industrial sustainability has become increasingly important in manufacturing industries, especially those with high energy consumption. One of the strategies for achieving cleaner production in energy-intensive industries involves the utilization of renewable energy in lieu of conventional energy. Therefore, our study explores the evolutionary behavior of the population of manufacturing industries, particularly energy-intensive producers, to purchase renewable or non-renewable energy under the government's price-discount support. Thus, an evolutionary game for examining producers' behavior under two strategies of purchasing energy from a renewable supplier (R Strategy) and a non-renewable supplier (N Strategy) is considered. The analysis is conducted using data from the cement and steel industries in Iran as case studies. The results indicate that the government's support plays a vital role in changing producers' strategy toward purchasing renewable energy. Also, to change the behavior of industries toward R strategy in a shorter period, the government can support energy-intensive industries like steel manufacturers. These producers can be considered as leading manufacturing industries for transitioning to clean energy and sustainable development.

Express packaging waste recycling: Stakeholders’ dynamic behavioral changes based on a computational evolutionary game theoretic approach

Identifying stakeholder behaviors is crucial in managing municipal express packaging waste. Prior studies have neglected the impacts of the advertising effect and environmental preference and thus fail to detect stakeholders’ behavioral changes under different policies. To address this limitation, we build a computational evolutionary game model that integrates the advertising effect, environmental preference benefits, and economic and information policies. A numerical simulation is then conducted via a differential equation solver embedded in MATLAB software to identify the behavioral changes of the government, express delivery enterprises, and consumers. The results indicate that subsidies induce enterprises and consumers to reach their stability strategy more quickly, with maximum behavioral change rates of 0.82% and 0.64%, respectively. The marginal impacts of information policy and environmental preference benefits are greatest for consumers, whose behavioral change rates can reach 1.33% and 1.52%, respectively. For enterprises, a 1.71% behavioral change rate can be achieved by increasing the government’s effort level. An increase in enterprises’ effort level causes both enterprises and consumers to reach a stability strategy more quickly and achieves behavioral change rates of 4.21% and 1.51%, respectively. Our model can be used to predict stakeholders’ behavioral changes in the waste disposal industry, and the findings of this study provide novel ideas for waste management practitioners to use in encouraging recycling behaviors.

Electric vehicle charging infrastructure investment strategy analysis: State-owned versus private parking lots

This paper analytically studies the optimal charging infrastructure investment decisions in parking lots with state-owned firms and private firms, which aims to alleviate range anxiety and facilitate the mass adoption of electric vehicles. The investment decisions are made based on the rational choice in the short term and are assumed to be static. However, the investment in charging infrastructure is a long-term strategy and the decisions might evolve over time. As a result, an evolutionary game theoretic approach is proposed to analytically explore the instability of the investment decisions and derive the optimal investment strategies. We first construct a basic model with a payoff matrix of public and private parking lots and formulate the dynamic replicator equations to find the equilibrium points. We further explore the evolutionary stable strategy (ESS) with its conditions, and analyze the impact of key factors on the strategy evolution. In the basic model, we find that the investment ratio of the state-owned firms and private firms is crucial to the ESS and its original parking revenue has no impact on the investment decisions. To discover the impact of the uncertainty of investments, we next reformulate the model and extend the original payoff matrix by incorporating the perceived value function based on prospect theory. The ESS and its relevant conditions are also derived. We observe that two models have the same ESS in most cases when there is a marked difference in their profitability. When the difference in their profitability is small, the perceived return on investment plays a pivotal role in their investment decisions. Finally, managerial implications are discussed.

An Evolutionary Game Theoretical Approach to the Teaching-Learning Techniques in the Post-Pandemic Era

An Evolutionary Game Theoretical Approach to the Teaching-Learning Techniques in the Post-Pandemic Era

Evolutionary Game—Theoretic Approach for Analyzing User Privacy Disclosure Behavior in Online Health Communities

Privacy disclosure is one of the most common user information behaviors in online health communities. Under the premise of implementing privacy protection strategies in online health communities, promoting user privacy disclosure behavior can result in a “win–win” scenario for users and online health communities. Combining the real situation and evolutionary game theory, in this study, we first constructed an evolutionary game model of privacy disclosure behavior with users and online health communities as the main participants. Then, we solved the replication dynamic equations for both parties and analyzed the evolutionary stable strategies (ESSs) in different scenarios. Finally, we adopted MATLAB for numerical simulations to verify the accuracy of the model. Studies show that: (1) factors such as medical service support and community rewards that users receive after disclosing their private personal information affect user game strategy; and (2) the additional costs of the online health communities implementing the “positive protection” strategy and the expected loss related to the privacy leakage risk affect the online health communities’ game strategy. In this regard, this paper puts forward the following suggestions in order to optimize the benefits of both sets of participants: the explicit benefits of users should be improved, the internal environment of the communities should be optimized, the additional costs of the “positive protection” strategy should be reduced, and penalties for privacy leakages should be increased.

A hybrid evolutionary game-theoretic and system dynamics approach for analysis of implementation strategies of green technological innovation under government intervention

Green technological innovation (GTI) is accompanied by a high level of risk and cost. Therefore, choosing the optimal strategy for its implementation has attracted the attention of decision-makers. In this regard, technological firms have two strategies: collaboration with green startups and performing independent green R&D. This paper analyzes the impact of maturity level and innovation level of green startups, as well as the competitive intensity between technological firms on their evolutionary behavior. For this purpose, an evolutionary game model was developed, and the impact of government static and dynamic intervention on the firms’ innovative behavior was investigated by the system dynamics. There are six possible stable equilibrium points. The results suggest that when the government adopts static intervention, at the same time, reducing the maturity level and increasing the innovation level of the green startups decreases technological firms' desire to collaborate with them. When the government adopts dynamic intervention, then it would be more desirable for the technological firms to collaborate with green startups. If a technological firm can increase its product sales compared to the competitor, this change will affect the evolution trend of the system. Increasing sale amounts by a technological firm leads to more own stability rate, decreasing it for its competitor.

Firms’ re-innovation after failure and institutional environment: an evolutionary game theoretical approach

Carrying out follow-up innovation activities is significant to encourage firms with failed innovation to improve their innovation quality and sustainable competitiveness. However, the existing studies lack discussion on how to stimulate firms’ re-innovation after failure from the institutional level. To explore the relationship between institutional environment and the behavior choice of firms’ re-innovation after failure, the behavior characteristics of firms’ re-innovation after failure were discussed. A game model between government departments and firms with failed innovation was constructed by using the evolutionary game approach. The stable equilibrium strategies in the process of institutional environment optimization and firms’ re-innovation decision-making after failure were analyzed. Meanwhile, the case of Zhengzhou in China was used to illustrate the theoretical model. Results show that the subjective perception of risk and benefit of re-innovation affects the behavior choice of firms’ re-innovation after failure. The increase of re-innovation income promotes re-innovation behavior after failure and enhances firms’ competitiveness. The improvement of intellectual property protection improves re-innovation income, but the reduction of re-innovation cost has a limited impact on the behavior choice of firms’ re-innovation after failure. The increase of government social welfare benefits promotes the institutional environment construction and firms’ willingness to re-innovate after failure.

Abstract IA019: Resolving the paradigm of the pre-existing resistance to targeted therapy, and the fitness costs associate with resistance: In vitro and theoretical evidence using an evolutionary game theoretic approach

Abstract Therapeutic strategies for tumor control have traditionally assumed that maximizing reduction in tumor volume correlates with clinical efficacy. Unfortunately, this rapid decrease in tumor burden is almost invariably followed by the emergence of therapeutic resistance. Evolutionary based treatment strategies attempt to delay resistance via judicious treatments that maintain a significant treatable subpopulation. While these strategies have shown promise in recent clinical trials, they often rely on biological conjecture and intuition to derive parameters. To bridge this gap, we recently developed an in vitro assay to directly measure the parameters of the underlying evolutionary game played between cancer cells under therapy. Using this assay we have subsequently directly observed competitive exclusion in non-small cell lung cancer cells – a necessary, but not sufficient condition for adaptive therapy. Intrinsic to this finding lies another advance: one which helps us settle the long standing paradox of pre-existing resistance and fitness costs associated with resistance mutations. In particular, if a cost of resistance exists, how can a less fit mutant persist in a population long enough for a treatment to be given to expose its increased fitness? To answer this, we show experimental and theoretical results that help explain this paradox which we posit submit should be generalizable outside cancer and applicable across the kingdoms of life where eco-evolutionary dynamics are at play. Citation Format: Jacob G. Scott. Resolving the paradigm of the pre-existing resistance to targeted therapy, and the fitness costs associate with resistance: In vitro and theoretical evidence using an evolutionary game theoretic approach [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr IA019.

An evolutionary game-theoretic approach to study the technological transformation of the industrial sector toward renewable electricity procurement: A case study of Iran

Electricity shortages severely affect the production chain and cause damage to manufacturing productivity. This study examines how the industrial sector can transform technological innovations toward renewable electricity procurement. We consider two strategies for manufacturers: purchasing electricity from a technology-based supplier (TB strategy) and purchasing it from a non-technology-based supplier (NTB strategy). Manufacturers need technological transformation, such as upgrading their electrical system toward digitalization to manage power outages. We examine manufacturers’ pricing decisions and their long-term behavior to adopt the TB and NTB strategies using a one-population evolutionary game-theoretic approach. Also, we investigate the majority of manufacturers’ population that eventually choose the best strategy. Moreover, we apply the present model to real-world cases of Iran’s cement, steel, and paper industries. The results show that the evolutionary behavior of sectors with high annual electricity consumption, such as steel, converges to the TB strategy faster than cement and paper industries. In contrast, the cement and paper industries are more sensitive to subsidy allocation than the steel industry with respect to electricity procurement strategies.

ACSEE: Antagonistic Crowd Simulation Model With Emotional Contagion and Evolutionary Game Theory

Antagonistic crowd behaviors are often observed in cases of serious conflict. Antagonistic emotions, which is the typical psychological state of agents in different roles (i.e. cops, activists, and civilians) in crowd violent scenes, and the way they spread through contagion in a crowd are important causes of crowd antagonistic behaviors. Moreover, games, which refers to the interaction between opposing groups adopting different strategies to obtain higher benefits and less casualties, determine the level of crowd violence. We present an antagonistic crowd simulation model, ACSEE, which is integrated with antagonistic emotional contagion and evolutionary game theories. Our approach models the antagonistic emotions between agents in different roles using two components: mental emotion and external emotion. We combine enhanced susceptible-infectious-susceptible (SIS) and game approaches to evaluate the role of antagonistic emotional contagion in crowd violence. Our evolutionary game theoretic approach incorporates antagonistic emotional contagion through deterrent force, which is modelled by a mixture of emotional forces and physical forces defeating the opponents. Antagonistic emotional contagion and evolutionary game theories influence each other to determine antagonistic crowd behaviors. We evaluate our approach on real-world scenarios consisting of different kinds of agents. We also compare the simulated crowd behaviors with real-world crowd videos and use our approach to predict the trends of crowd movements in violence incidents. We investigate the impact of various factors (number of agents, emotion, strategy, etc.) on the outcome of crowd violence. We present results from user studies suggesting that our model can simulate antagonistic crowd behaviors similar to those seen in real-world scenarios.

Integrating Human Decisions in the Presence of Byzantines: An Evolutionary Game Theoretical Approach

Integrating Human Decisions in the Presence of Byzantines: An Evolutionary Game Theoretical Approach

Users’ Payment Intention considering Privacy Protection in Cloud Storage: An Evolutionary Game-Theoretic Approach

To solve the current privacy leakage problems of cloud storage services, research on users’ payment intention for cloud storage services with privacy protection is extremely important for improving the sustainable development of cloud storage services. An evolutionary game model between cloud storage users and providers that considers privacy is constructed. Then, the model’s evolutionary stability strategies via solving the replication dynamic equations are analyzed. Finally, simulation experiments are carried out for verifying and demonstrating the influence of model parameters. The results show that the evolutionary stable strategies are mainly affected by the privacy protection profit growth coefficient of both parties, input costs, free-riding gains, and other factors. If the profit growth coefficient is very small, users will not choose to pay and providers will not choose to actively protect user information. As the profit growth coefficient increases, both parties will promote the development of privacy protection with a higher probability. The results are beneficial for cloud storage providers to increase the number of paid users and thus to achieve the sustainable development of cloud storage service.

Corrigendum to “Explore the Conflict and Resolution of Pollution NIMBY Facility Construction in the Context of New Media: An Evolutionary Game Theoretical Approach”

Corrigendum to “Explore the Conflict and Resolution of Pollution NIMBY Facility Construction in the Context of New Media: An Evolutionary Game Theoretical Approach”