Evolutionary Game Theory Research Articles

Empirical evidence of resource dependent evolution of payoff matrices in Saccharomyces cerevisiae populations.

In evolutionary game theory, a relative comparison of the cost and benefit associated with obtaining a resource, called payoff, is used as an indicator of fitness of an organism. Payoffs of different strategies, quantitatively represented as payoff matrices, are used to understand complex inter-species and intra-species interactions like cooperation, mutualism, and altruism. Payoff matrices, however, are usually treated as invariant with time - largely due to the absence of any empirical data quantifying their evolution. In this paper, we present empirical evidence of three types of resource-dependent changes in the payoff matrices of evolving Saccharomyces cerevisiae populations. We show that depending on the carbon source and participating genotypes, N-player games could collapse, be born, or be maintained. Our results highlight the need to consider the dynamic nature of payoff matrices while making even short-term predictions about population interactions and dynamics.

Research on the diffusion and control of unsafe behaviors among chemical industry park enterprises based on the SEIR evolutionary game model

In order to control the diffusion of unsafe behaviors among chemical industrial park enterprises, a Susceptible-Exposed-Infectious-Recovered (SEIR) evolutionary game model is constructed based on the theory of epidemiology and evolutionary game theory. By exploring the game relationship between emergency management departments and safety assessment agencies, the paper analyzes the diffusion paths and evolutionary patterns of unsafe behaviors in chemical industrial park enterprises under different behavioral strategies adopted by both parties. Furthermore, it examines the process and patterns of system evolution to a positive stable state, and conducts multi-scenario simulation and analysis. The results indicate that the interactive behaviors of "strict regulation" by emergency management departments and "high-quality safety assessment" by safety assessment agencies are more conducive to controlling the diffusion of unsafe behaviors among chemical industrial park enterprises. Improving the efficiency of emergency management departments, establishing an attractive and constraining reward and punishment system to guide safety assessment agencies in conducting high-quality safety assessments, and stimulating the initiative of chemical industrial park enterprises in safety behaviors will be more conducive to forming an integrated, coordinated, and mutually constraining mechanism for work safety governance.

Compensation Mechanism for Ecological Diversity Governance in Chinese River Basins Application Based on Low-Carbon Water Quality Game Theory

Background: Taking the construction of ecological civilization as an important core of the overall layout of the “Five in One” and the coordinated promotion of the “Four Comprehensives” strategic layout, combined with the global concept of ecological environment governance, provides a new direction for the reform and innovation of China’s watershed ecological compensation system through diversified governance. Subjects and methods: This article focuses on ecological compensation in river basins, exploring collaborative cooperation models between upstream and downstream of the basin under the dual constraints of “low-carbon” and “water quality”, to achieve a “win-win” situation between the ecological environment of the basin and all parties involved in basin protection. Based on the theory of evolutionary game theory, the indicators of “carbon emissions” and “water quality” are introduced to construct a diversified ecological co-governance compensation strategy model for the watershed. By exploring the optimal strategies among various entities in the upstream and downstream of the watershed. Results: (1) In the initial stage, the optimal strategy for ecological compensation in the watershed among stakeholders cannot achieve a stable balance through their evolution; (2) In the mature stage, based on the regulation of the superior government, interest subjects will choose a mutual supervision strategy to avoid bearing high penalties, to achieve Pareto effectiveness in achieving a stable overall economy of the basin; (3) By paying for high-quality resources from downstream and implementing carbon trading among basin entities, we can promote internal resource digestion, reduce intermediate transaction costs, and deeply promote the overall green and sustainable development of the basin. Through analysis, it has been proven that the conclusion is effective, providing decision support for the diversified co-governance of watershed ecosystems. Based on building an ecological community with a shared future in the watershed, we aim to achieve the construction of a community with a shared future for mankind.

Secure and efficient trust enabled routing in mobile ad hoc network using game theory and grey wolf optimisation techniques

AbstractMobile Ad hoc Networks (MANETs) are crucial wireless networks for military, corporate, and emergency use, yet they are vulnerable to disruptions from malicious nodes. The presence of malicious nodes can lead to message transmission and routing disorganisation, and network performance is effectively compromised. Game theory‐based fuzzy secure clustering (GTFSC) improves performance metrics in low‐scale and high‐scale networks. This protocol's novel ability to dynamically scale performance measures as nodes expand improves efficiency and adaptability. While improving performance metrics, the proposed algorithm also efficiently identifies malicious nodes and re‐routes the transmission, excluding the found malicious nodes. For any MANET system, secure and successful data transmission is paramount. The proposed protocol integrates various algorithms to fulfil its aim of customised EGT, GWO, and fuzzy clustering. Black hole attacks, grey hole attacks, Sybil attacks, and data tampering attacks are all considered to provide comprehensive attacks on MANET. Every node is assigned trust values, which get updated on data transmission. Fuzzy Clustering is employed to identify malicious nodes. Evolutionary Game Theory (EGT) optimises network organisation by designating cluster heads and clusters as nodes. Additionally, the proposed protocol leverages the Grey Wolf Optimisation Routing Algorithm (GWO), which establishes efficient routes from the source to the sink node. The analysis result shows maximum performance with a packet delivery ratio of around 98%, throughput of 90% end‐to‐end delay reduced by 15%, and energy consumption reduced by 18%, respectively, compared to an existing protocol.

Optimization strategies for green power and certificate trading in China considering seasonality: An evolutionary game-based system dynamics

China has adopted renewable portfolio standards and tradable green certificates (including hydropower) to increase the proportion of green power used and reduce carbon emissions. However, hydropower generation is affected by seasonal factors, making it difficult to increase green power consumption in water-scarce areas. Trading electricity and green certificates in regions with different resource endowments can effectively solve this problem. Therefore, this study uses evolutionary game theory to explore the impact of the change in the policy of issuing green certificates for hydropower on the strategic choices of the trading parties. Based on the evolutionary game model, a system dynamics model is established to explore the optimization strategies of green power and certificate trading under the influence of seasonal factors. The results of the case study show that (1) the price of green certificates varies with seasonal changes. (2) Sellers with abundant hydropower resources should increase the amount of hydropower connected to the grid during the water-abundant period to help deficit areas achieve their consumption targets (3) Expanding the scope of green certificates can gradually eliminate government subsidies and incentives and reduce the government's financial pressure.

Spontaneous Formation of Evolutionary Game Strategies for Long-Term Carbon Emission Reduction Based on Low-Carbon Trading Mechanism

In the context of increasing global efforts to mitigate climate change, effective carbon emission reduction is a pressing issue. Governments and power companies are key stakeholders in implementing low-carbon strategies, but their interactions require careful management to ensure optimal outcomes for both economic development and environmental protection. This paper addresses this real-world challenge by utilizing evolutionary game theory (EGT) to model the strategic interactions between these stakeholders under a low-carbon trading mechanism. Unlike classical game theory, which assumes complete rationality and perfect information, EGT allows for bounded rationality and learning over time, making it particularly suitable for modeling long-term interactions in complex systems like carbon markets. This study builds an evolutionary game model between the government and power companies to explore how different strategies in carbon emission reduction evolve over time. Using payoff matrices and replicator dynamics equations, we determine the evolutionarily stable equilibrium (ESE) points and analyze their stability through dynamic simulations. The findings show that in the absence of a third-party regulator, neither party achieves an ideal ESE. To address this, a third-party regulatory body is introduced into the model, leading to the formulation of a tripartite evolutionary game. The results highlight the importance of regulatory oversight in achieving stable and optimal low-carbon strategies. This paper offers practical policy recommendations based on the simulation outcomes, providing a robust theoretical framework for government intervention in carbon markets and guiding enterprises towards sustainable practices.

Public participation in Governance of E-waste recycling: A tripartite evolutionary game analysis

The surge in informal E-waste recycling has led to significant resource wastage and environmental hazards. The current government-centered governance system faces substantial obstacles to effective implementation due to information gaps and operational challenges. This study adopts the concept of social governance by incorporating public participation into the traditional government-led e-waste recycling system. Utilizing the bounded rationality of evolutionary game theory, this research constructs a tripartite game model involving the government, recyclers, and the public. It analyzes the behavioral strategy choices of each participant and the mechanisms of mutual influence under different scenarios. The main findings are: (1) Solely relying on public power is insufficient for effectively governing informal recyclers. (2) Reasonable and moderate incentives can promote public participation, serving as an effective supplementary tool for government regulation. (3) Increasing public exposure to the informal dismantling behaviors of recyclers is crucial for enhancing governance effectiveness. Specific measures can include establishing complaint departments for related incidents and creating media channels. These findings underscore the importance of integrating public participation into e-waste governance to address the limitations of a purely government-centered approach.

Evolutionary game theoretical approach for reducing carbon emissions in a complex supply chain organization

PurposeOur research explores the intricate behavior of low-carbon supply chain organizations in an ever-evolving landscape, emphasizing the profound implications of government-mandated low-carbon policies and the growing low-carbon market. Central to our exploration is applying a combined game theory model, merging Evolutionary Game Theory (EGT) with the Shapley Value Cooperative Game Theory Approach (SVCGTA).Design/methodology/approachWe establish a two-tier supply chain featuring retailers and manufacturers within this novel framework. We leverage an integrated approach, combining strategic Evolutionary Game Theory and Cooperative Game Theory, to conduct an in-depth analysis of four distinct low-carbon strategy combinations for retailers and manufacturers.FindingsThe implications of our findings transcend theoretical boundaries and resonate with a trinity of economic, environmental and societal interests. Our research goes beyond theoretical constructs to consider real-world impacts, including the influence of changes in government low-carbon policies, the dynamics of consumer sensitivities and the strategic calibration of retailer carbon financing incentives and subsidies on the identified ESS. Notably, our work highlights that governments can effectively incentivize organizations to reduce carbon emissions by adopting a more flexible approach, such as regulating carbon prices, rather than imposing rigid carbon caps.Originality/valueOur comprehensive analysis reveals the emergence of an Evolutionary Stability Strategy (ESS) that evolves in sync with the phases of low-carbon technology development. During the initial stages, our research suggests that manufacturers or retailers adopt low-carbon behavior as the optimal approach.

Can subsidy policies achieve fuel cell logistics vehicle (FCLV) promotion targets? Evidence from the beijing-tianjin-hebei fuel cell vehicle demonstration city cluster in China

Fuel cell logistics vehicle (FCLV) plays a pillar role in achieving carbon neutrality in the freight sector. Due to high costs and imperfect infrastructure, millions of subsidies are granted to develop FCLV and fulfill promotion targets set by the government. To examine the effectiveness of this action, the study proposes a tripartite evolutionary game theory (EGT) model to simulate the interactions between local governments, private investors, and consumers, and explores the fulfillment of promotion targets in the Beijing-Tianjin-Hebei fuel cell vehicle demonstration city cluster. The obtained results show that: (i) the FCLV promotion targets of demonstration cities except Tangshan could be substantially achieved under the current subsidy; (ii) purchase subsidies for consumers can help achieve promotion targets at a faster pace, while construction subsidies for investors have a limited incentive effect, so more non-financial incentives need to be provided to investors, such as innovative operating models and priority approval of land for hydrogen refueling station construction; (iii) a comparison of subsidies in different cities reveals that Beijing's subsidies are the most efficient and that there is an optimal allocation between consumers and private investors that allows for the faster accomplishment of promotion targets without increasing the fiscal burden.

Strategic dynamics of local governments in regional collaborative governance: An evolutionary game theory analysis of haze pollution response in the Fen-Wei Plain, China

Regional collaborative governance has become a key strategy for environmental protection, especially in reducing transboundary pollution transfer. This study, set against the backdrop of environmental governance in China's Fen-Wei Plain, employs evolutionary game theory to deeply analyze the strategic choices of local governments in managing haze pollution. We developed a model incorporating 14 key variables to systematically explore the emission reduction strategies of local governments under various policy environments. Through numerical simulations, we not only validate the effectiveness of the model but also focuses on how incentives and punishments from the central government influence the stability of local governments adopting a “strict enforcement” strategy. We find that appropriate incentives from the central government can significantly enhance the tendency of local governments to choose a “strict enforcement” strategy for emission reduction. Under certain conditions, whether adopting “strict enforcement” or “superficial enforcement,” both can lead to an Evolutionarily Stable Strategy (ESS). Moreover, the intensity of rewards and penalties from the central government and the benefits of collaborative governance by local governments are key factors determining the stability of strategies. Our findings underscore the importance of establishing performance-oriented incentive mechanisms, refining reward and punishment measures, and focusing on sustainable and adaptable governance strategies. The strategic recommendations provided by this study offer important guidance for balancing incentives and punishments, thereby stimulating local government enthusiasm for governance, which supports high-quality environmental protection and sustainable development goals.

Evolutionary game and simulation study of public transport under government incentive and punishment mechanism.

Public transport plays an indispensable role in the whole public transport system. This paper makes an in-depth study on how public transport can provide passengers with higher service quality while meeting the needs of passengers. In order to achieve this research goal, this paper organically incorporates the three key subjects of government supervision, public transport and passengers into the research framework. Evolutionary game theory is used to construct the corresponding research model. It has been found that the decision-making behaviours of government regulators, public transport and passengers are intricately intertwined to influence each other in the evolutionary process. It is particularly noteworthy that the incentive or punishment measures adopted by the government have a great impact on the quality of public transport services. In addition, timely supervision and inspection of government regulatory authorities by higher authorities proved to be crucial for buses to provide stable and high-quality services. This study reveals the mechanisms of interaction between different subjects in the public transport system, particularly the government-guided incentive measures and supervision mechanism to promote the overall service level. To further support the research conclusions, this paper carries on the simulation analysis, and puts forward the countermeasures and suggestions for the bus to provide high-quality service according to the simulation results. These recommendations will help guide the government, public transport and passengers to make better decisions in the synergistic development process, thereby improving the overall level of service.

The impact of time delays on mutant fixation in pairwise social dilemma games

Evolutionary game theory examines how strategies spread and persist in populations through reproduction and imitation based on their fitness. Traditionally, models assume instantaneous dynamics where fitness depends on the current population state. However, some real-world processes unfold over time, with outcomes emerging from history. This motivates incorporating time delays into evolutionary game models, where fitness relies on the past. We study the impact of time delays on mutant fixation in a Moran Birth–death process with two strategies in a well-mixed population. At each time step of the process, an individual reproduces proportionally to fitness coming from the past. We model this as an absorbing Markov chain, allowing computational calculation of the fixation probability and time. We focus on three important games: the Stag-Hunt, Snowdrift and Prisoner’s Dilemma. We will show time delays reduce the fixation probability in the Stag-Hunt and the Prisoner’s Dilemma but increase it in the Snowdrift. For the Stag-Hunt and the Prisoner’s Dilemma, time delays lengthen the fixation time until a critical point, then reduce it. The Snowdrift exhibits the opposite trend.

Recent achievements in nonlinear dynamics, synchronization, and networks.

This Focus Issue covers recent developments in the broad areas of nonlinear dynamics, synchronization, and emergent behavior in dynamical networks. It targets current progress on issues such as time series analysis and data-driven modeling from real data such as climate, brain, and social dynamics. Predicting and detecting early warning signals of extreme climate conditions, epileptic seizures, or other catastrophic conditions are the primary tasks from real or experimental data. Exploring machine-based learning from real data for the purpose of modeling and prediction is an emerging area. Application of the evolutionary game theory in biological systems (eco-evolutionary game theory) is a developing direction for future research for the purpose of understanding the interactions between species. Recent progress of research on bifurcations, time series analysis, control, and time-delay systems is also discussed.

Hypernetwork modeling and topology of high-order interactions for complex systems

Interactions among the underlying agents of a complex system are not only limited to dyads but can also occur in larger groups. Currently, no generic model has been developed to capture high-order interactions (HOI), which, along with pairwise interactions, portray a detailed landscape of complex systems. Here, we integrate evolutionary game theory and behavioral ecology into a unified statistical mechanics framework, allowing all agents (modeled as nodes) and their bidirectional, signed, and weighted interactions at various orders (modeled as links or hyperlinks) to be coded into hypernetworks. Such hypernetworks can distinguish between how pairwise interactions modulate a third agent (active HOI) and how the altered state of each agent in turn governs interactions between other agents (passive HOI). The simultaneous occurrence of active and passive HOI can drive complex systems to evolve at multiple time and space scales. We apply the model to reconstruct a hypernetwork of hexa-species microbial communities, and by dissecting the topological architecture of the hypernetwork using GLMY homology theory, we find distinct roles of pairwise interactions and HOI in shaping community behavior and dynamics. The statistical relevance of the hypernetwork model is validated using a series of in vitro mono-, co-, and tricultural experiments based on three bacterial species.

A comprehensive design pipeline for FCEV powertrain configuration considering the evolutionary development process and extreme economic performance

Due to stringent emission regulations, the zero-emission hydrogen fuel cell electric vehicles (FCEVs) are gaining significant attention, but their slow power response requires careful selection of auxiliary energy sources and power systems to optimize energy efficiency. This paper presents a comprehensive design pipeline for the FCEV powertrain, encompassing methods for powertrain configuration design, feasible domain design for component parameters, and parameter matching. Initially, a powertrain configuration design method based on evolutionary game theory is introduced, forecasting trends in energy-efficient powertrain configurations using a dynamic cost function that adjusts based on the proportions of different configurations. Next, a feasible domain design method is developed to establish fixed constraints on component parameters, ensuring a fair balance between performance needs and overall costs, including development and lifecycle expenses associated with the powertrain configuration. Subsequently, dynamic programming (DP) is used for parameter matching, assessing the utmost energy-saving potential. The cost function in this method incorporates the degradation cost of powertrain components, underscoring the economic performance of the target FCEV over its lifespan. Finally, simulation assessments indicate that the proposed design pipeline effectively identifies the optimal powertrain configuration for FCEVs, endowing each possible configuration with exceptional economic efficiency across different component parameters.

Coordination of Renewable Energy Integration and Peak Shaving through Evolutionary Game Theory

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid.

Research on low-carbon supply chain emission reduction strategies based on blockchain technology

The difficulty of regulating carbon trading due to information asymmetry and low consumer trust in low-carbon products are key factors hindering companies from reducing emissions. This paper examines a manufacturer-led secondary low-carbon supply chain consisting of a single supplier and a retailer, focusing on the impact of blockchain technology on carbon transaction costs and consumers' low-carbon preferences. Utilizing Stackelberg game theory, the paper constructs a supply chain decision model for emission reduction, determining the payment matrix and analyzing the stable strategy for blockchain adoption through evolutionary game theory. The findings indicate that retailers' adoption of blockchain technology significantly promotes emission reduction within the supply chain, whereas manufacturers' adoption has minimal impact. Additionally, the study reveals that variations in blockchain adoption costs and carbon quotas result in multiple evolutionary stable strategies. Specifically, when blockchain adoption costs and carbon quotas are below certain thresholds, the system reaches a unique equilibrium where both parties adopt blockchain technology.

HMAE: a high-fidelity multi-agent simulator for economic phenomenon emergence

Economic models based on multi-agents are increasingly attracting attention and can provide a new perspective for exploring the causes behind social phenomena at the individual level. Existing research usually adopts society-level learning methods, and more research on micro-level heterogeneity among individuals is needed. For this, we propose a high-fidelity multi-agent economy (HMAE) model based on evolutionary game theory, including three types of agents: workers, firms, and the government. In particular, we characterize worker heterogeneity regarding laziness factors, work endowments, and commuting distances. These agents continuously and iteratively update their strategies by randomly exploring and imitating their neighbors to maximize their utility value. We simulated the evolution process of agent behavioral decisions through experiments and found that individual heterogeneity can significantly affect the decisions of workers and firms. These phenomena are consistent with some economic evolution trends in real life, and our research can provide an analytical tool for analyzing the causes of emerging economic phenomena.

Research on the Green Transition Path of Airport Development under the Mechanism of Tripartite Evolutionary Game Model

Since existing studies primarily explore green development measures from the static perspective of a single airport stakeholder, this paper constructs an evolutionary game model to analyze the strategic choices of three key stakeholders: airport authorities, third-party organizations, and government departments, based on evolutionary game theory. By solving the stable strategy of the tripartite evolution using the Jacobian matrix, the green transition of airport development can be divided into three stages: “initiation”, “development”, and “maturity”, allowing for the exploration of key factors influencing the green transition of airport development. A simulation analysis is conducted based on real Guangzhou Baiyun International Airport data. The results indicate that the tripartite evolutionary game strategy is stable at E4(0,0,1) and the green transition of Baiyun Airport remains in the development stage. By improving the reward and punishment mechanisms of government departments, the evolutionary game strategy can be stabilized at E8(1,1,1), promoting the green transition of airport development toward the mature stage. By adjusting the game parameters, the dynamic process of green transition in airports at different levels of development and under varying regulatory environments can be effectively captured, supporting the precise formulation of corresponding policies.

Sustainable development through the balancing of photovoltaic charging facilities and agriculture for energy harvesting

Photovoltaics (PV) and electric vehicles (EVs) provide viable alternatives for powering rural areas and promoting sustainable development. However, solar energy and agricultural land compete with each other, necessitating a balance between energy needs and land preservation. Despite the potential of agricultural PV charging stations, there is a lack of research on their operational models, policies, stakeholder interactions, and feasibility of development. This study combines renewable energy and charging infrastructure subsidy policies, utilizes a public-private partnership model, and employs evolutionary game theory to establish a theoretical framework exploring strategic interactions among energy operators, EV users, and farmers. The model considers the impacts of generation, charging, and agricultural elements, conducting techno-economic research. A case study conducted in Sioux Falls, South Dakota, USA, revealed subsequent findings: (1) The players' evolutionary stable approach ultimately converges to (1,1,1). When renewable energy subsidies are decreased by 50 %, the likelihood of active engagement for energy operators drops to 0, and for farmers, it falls to 0.8. (2) As the usage duration of charging facilities grows by 50 %, energy operators and EVUs make steady progress before reaching 0.2 cycles, but farmers encounter a delay after 0.4 cycles. (3) It is feasible to fulfil the yearly charging requirements for EVs in Sioux Falls and South Dakota using only 7.4 ha and less than 22 ha of agricultural land area, respectively. Investors in Sioux Falls have the potential to earn an annual income of $1.1085 million, generate 7906.07 MWh of electricity, and reduce GHG emissions by 6525.91 tons of CO2-equivalent. Although farmers generate higher annual revenue, it typically takes them over a decade to reach the point of breaking even, and their return on investment is comparatively lower than that of energy operators.