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Zhang, Hangjing, Yan Chen, and H. Vicky Zhao. "Evolutionary information dynamics over social networks: a review." International Journal of Crowd Science 4, no. 1 (2020): 45–59. http://dx.doi.org/10.1108/ijcs-09-2019-0026.
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Purpose The purpose of this paper is to have a review on the analysis of information diffusion based on evolutionary game theory. People now get used to interact over social networks, and one of the most important functions of social networks is information sharing. Understanding the mechanisms of the information diffusion over social networks is critical to various applications including online advertisement and rumor control. Design/methodology/approach It has been shown that the graphical evolutionary game theory (EGT) is a very efficient method to study this problem. Findings By applying EGT to information diffusion, the authors could predict every small change in the process, get the detailed dynamics and finally foretell the stable states. Originality/value In this paper, the authors provide a general review on the evolutionary game-theoretic framework for information diffusion over social network by summarizing the results and conclusions of works using graphical EGT.
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Bai, Zhu, Mingxia Huang, Shuai Bian, and Huandong Wu. "A Study of Taxi Service Mode Choice Based on Evolutionary Game Theory." Journal of Advanced Transportation 2019 (July 4, 2019): 1–17. http://dx.doi.org/10.1155/2019/8607942.
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The emergence of online car-hailing service provides an innovative approach to vehicle booking but has negatively influenced the taxi industry in China. This paper modeled taxi service mode choice based on evolutionary game theory (EGT). The modes included the dispatching and online car-hailing modes. We constructed an EGT framework, including determining the strategies and the payoff matrix. We introduced different behaviors, including taxi company management, driver operation, and passenger choice. This allowed us to model the impact of these behaviors on the evolving process of service mode choice. The results show that adjustments in taxi company, driver, and passenger behaviors impact the evolutionary path and convergence speed of our evolutionary game model. However, it also reveals that, regardless of adjustments, the stable states in the game model remain unchanged. The conclusion provides a basis for studying taxi system operation and management.
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Villena, Mauricio G., and Marcelo J. Villena. "Evolutionary Game Theory and Thorstein Veblen’s Evolutionary Economics: Is EGT Veblenian?" Journal of Economic Issues 38, no. 3 (2004): 585–610. http://dx.doi.org/10.1080/00213624.2004.11506721.
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Pacheco, Jorge M., Francisco C. Santos, and David Dingli. "The ecology of cancer from an evolutionary game theory perspective." Interface Focus 4, no. 4 (2014): 20140019. http://dx.doi.org/10.1098/rsfs.2014.0019.
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The accumulation of somatic mutations, to which the cellular genome is permanently exposed, often leads to cancer. Analysis of any tumour shows that, besides the malignant cells, one finds other ‘supporting’ cells such as fibroblasts, immune cells of various types and even blood vessels. Together, these cells generate the microenvironment that enables the malignant cell population to grow and ultimately lead to disease. Therefore, understanding the dynamics of tumour growth and response to therapy is incomplete unless the interactions between the malignant cells and normal cells are investigated in the environment in which they take place. The complex interactions between cells in such an ecosystem result from the exchange of information in the form of cytokines- and adhesion-dependent interactions. Such processes impose costs and benefits to the participating cells that may be conveniently recast in the form of a game pay-off matrix. As a result, tumour progression and dynamics can be described in terms of evolutionary game theory (EGT), which provides a convenient framework in which to capture the frequency-dependent nature of ecosystem dynamics. Here, we provide a tutorial review of the central aspects of EGT, establishing a relation with the problem of cancer. Along the way, we also digress on fitness and of ways to compute it. Subsequently, we show how EGT can be applied to the study of the various manifestations and dynamics of multiple myeloma bone disease and its preceding condition known as monoclonal gammopathy of undetermined significance. We translate the complex biochemical signals into costs and benefits of different cell types, thus defining a game pay-off matrix. Then we use the well-known properties of the EGT equations to reduce the number of core parameters that characterize disease evolution. Finally, we provide an interpretation of these core parameters in terms of what their function is in the ecosystem we are describing and generate predictions on the type and timing of interventions that can alter the natural history of these two conditions.
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Qiu, Benliu, and Ningxuan Zhang. "A review on graphical evolutionary game for information diffusion on social networks." International Journal of Crowd Science 2, no. 3 (2018): 259–71. http://dx.doi.org/10.1108/ijcs-06-2018-0011.
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Purpose With the recent development of science and technology, research on information diffusion has become increasingly important. Design/methodology/approach To analyze the process of information diffusion, researchers have proposed a framework with graphical evolutionary game theory (EGT) according to the theory of biological evolution. Findings Through this method, one can study and even predict information diffusion. Originality/value This paper summarizes three existing works using graphical EGT to discuss how to obtain the static state and the dynamics of information diffusion in social network.
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Liu, Yaqing, Lifeng Zhang, Yushang Hu, and Zanxin Wang. "Evolutionary Game Analysis of China–Laos Electric Power Cooperation." Sustainability 16, no. 23 (2024): 10560. https://doi.org/10.3390/su162310560.
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Cross-border power cooperation is considered a pathway for optimal regional use of renewable resources and the reduction of carbon emissions. To enhance such cooperation, it is essential to understand the game behaviors of the involved parties. This study applied evolutionary game theory (EGT) and system dynamics (SD) methods to analyze the factors influencing strategic choices and cooperation benefits in the China–Laos electricity cooperation. An EGT model was first developed to examine the interactive behavior of both parties and the stability of strategies. Subsequently, an SD model of EGT was constructed to simulate the evolutionary game process, explore the intrinsic mechanisms of the evolutionary game, and analyze the factors affecting strategy selection. The results show that: (1) the gaming behaviors cannot be ignored in cross-border power cooperation; (2) compared to the cross-border trade scenario, the strategic cooperation will generate more benefits for the parties involved and thus will be selected as the cooperation game evolves; (3) the initial strategy ratio of both parties is crucial, influencing the direction of strategy evolution and the time to reach equilibrium; (4) the choice of system cooperation strategy is affected by the unit profit of electricity trade, input cost, incremental return, trade volume, transaction cost, excess return, fine for agreement violation, and the ratio of benefit allocation, among which the former three are critical.
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Zhu, Zhanggen, Lefeng Cheng, and Teng Shen. "Spontaneous Formation of Evolutionary Game Strategies for Long-Term Carbon Emission Reduction Based on Low-Carbon Trading Mechanism." Mathematics 12, no. 19 (2024): 3109. http://dx.doi.org/10.3390/math12193109.
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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.
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Zhang, Shanshan, Bing Bai, and Aijia Huang. "Evolution of Financial Ecosystem from the CAS and EGT Perspective." MATEC Web of Conferences 267 (2019): 04007. http://dx.doi.org/10.1051/matecconf/201926704007.
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In recent years, there is a large amount of literature that studies the theory of business ecosystem, but there is rarely literature on the financial system which plays a critical role in the good running of the enterprise. To fill this gap, the purpose of this paper is to address the evolution of financial ecosystem from an ecological and dynamic perspective. In order to provide a better presentation of the evolutionary process, based on complex adaptive system (CAS) theory and evolutionary game theory (EGT), this paper analyzed the adaptability of financial ecosystem and built an evolutionary game model of financial ecosystem to confirm the point of the view. The results show that the evolution of financial ecosystem is a dynamic adaptive process. Under the assumption of limited rationality, the financial ecosystem gradually finds the optimal strategy through adaptive learning, and finally the evolution reaches an equilibrium stage.
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Xiang, Xiaoqian. "Multi-Player Evolutionary Game Theory in Cooperative Governance of Natural, Social, and Network Environments: A Review." Advances in Economics, Management and Political Sciences 184, no. 1 (2025): 51–56. https://doi.org/10.54254/2754-1169/2025.bl23235.
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In the context of rapid economic development, profound transformation of social structure and accelerated technological evolution, environmental governance issues are increasingly presented with cross-domain, multi-subject and dynamic complex characteristics. These challenges often involve strategic conflicts among governments, enterprises, and the public, making cooperation more uncertain and difficult to sustain. Traditional two-player game models are insufficient for capturing the evolving dynamics of such systems. In recent years, multi-player evolutionary game theory (EGT) has provided new perspectives for analyzing various environmental governance. This review explores the application of multi-player EGT in three major governance contexts: natural, social, and cyber environments, highlighting its effectiveness in modeling adaptive behaviors, feedback mechanisms, and policy outcomes under uncertainty, drawing on both theoretical frameworks and simulation-based studies. EGT offers a systematic analytical tool for understanding multi-agent interactions and optimizing governance mechanisms. Moreover, it provides theoretical guidance for public policy design and supports the stable development of collaborative governance. The review also discusses EGTs strong potential to make further contributions in more and more complex interdisciplinary fields in the future.
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Escobar-Cuevas, Héctor, Erik Cuevas, Alberto Luque-Chang, Oscar Barba-Toscano, and Marco Pérez-Cisneros. "Enhancing Metaheuristic Algorithm Performance Through Structured Population and Evolutionary Game Theory." Mathematics 12, no. 23 (2024): 3676. http://dx.doi.org/10.3390/math12233676.
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Diversity is crucial for metaheuristic algorithms. It prevents early convergence, balances exploration and exploitation, and helps to avoid local optima. Traditional metaheuristic algorithms tend to rely on a single strategy for generating new solutions, often resulting in a lack of diversity. In contrast, employing multiple strategies encourages a variety of search behaviors and a diverse pool of potential solutions, thereby improving the exploration of the search space. Evolutionary Game Theory (EGT) modifies agents’ strategies through competition, promoting successful strategies and eliminating weaker ones. Structured populations, as opposed to unstructured ones, preserve diverse strategies through localized competition, meaning that an individual’s strategy is influenced by only a subset or group of the population and not all elements. This paper presents a novel metaheuristic method based on EGT applied to structured populations. Initially, individuals are positioned near optimal regions using the Metropolis–Hastings algorithm. Subsequently, each individual is endowed with a unique search strategy. Considering a certain number of clusters, the complete population is segmented. Within these clusters, the method enhances search efficiency and solution quality by adapting all strategies through an intra-cluster competition. To assess the effectiveness of the proposed method, it has been compared against several well-known metaheuristic algorithms across a suite of 30 test functions. The results indicated that the new methodology outperformed the established techniques, delivering higher-quality solutions and faster convergence rates.
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Gou, Zhuozhuo, and Yansong Deng. "Dynamic Model of Collaboration in Multi-Agent System Based on Evolutionary Game Theory." Games 12, no. 4 (2021): 75. http://dx.doi.org/10.3390/g12040075.
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Multi-agent collaboration is greatly important in order to reduce the frequency of errors in message communication and enhance the consistency of exchanging information. This study explores the process of evolutionary decision and stable strategies among multi-agent systems, including followers, leaders, and loners, involved in collaboration based on evolutionary game theory (EGT). The main elements that affected the strategies are discussed, and a 3D evolution model is established. The evolutionary stability strategy (ESS) and stable conditions were analyzed subsequently. Numerical simulation results were obtained through MATLAB simulation, and they manifested that leaders play an important role in exchanging information with other agents, accepting agents’ state information, and sending messages to agents. Then, with the positivity of receiving and feeding back messages for followers, implementing message communication is profitable for the system, and the high positivity can accelerate the exchange of information. At the behavior level, reducing costs can strengthen the punishment of impeding the exchange of information and improve the positivity of collaboration to facilitate the evolutionary convergence toward the ideal state. Finally, the EGT results revealed that the possibility of collaboration between loners and others is improved, and the rewards are increased, thereby promoting the implementation of message communication that encourages leaders to send all messages, improve the feedback positivity of followers, and reduce the hindering degree of loners.
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Song, Yu, Zhigui Liu, and Xiaoli He. "Hybrid PSO and Evolutionary Game Theory Protocol for Clustering and Routing in Wireless Sensor Network." Journal of Sensors 2020 (October 30, 2020): 1–20. http://dx.doi.org/10.1155/2020/8817815.
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Compared with traditional networks, WSNs have more limited resources such as energy, communication, computing, and storage. The problem of how to achieve energy saving, extend network life cycle, and improve network performance under these limited resources has always been an issue of great interest in WSN research. However, existing protocols do not consider that sensor nodes within the BS threshold may not be clustered. These nodes can directly transmit data to the BS. This simplifies the cluster routing process of the entire WSN and saves more energy. This paper introduces an efficient, and energy-efficient, clustering and equalization routing protocol called the PSOLB-EGT protocol. This protocol introduces a new approach by combining improved particle swarm optimization (PSO) and evolutionary game theory (EGT) algorithms to address the problem of maximizing the network lifetime. The operation of the wireless sensor network is divided into an initialization phase and a data transmission phase. In the initialization phase of the wireless sensor network, the improved PSO algorithm is used to establish clusters and select CHs in areas other than the BS threshold. Entering the data transmission phase, we analyze this problem from the perspective of game theory. We use improved noncooperative evolutionary game theory to build models to solve the problem of the energy waste caused by routing congestion. The proposed PSOLB-EGT protocol is intensively experimented with a number of topologies in various network scenarios, and the results are compared with the well-known cluster-based routing protocols that include the swarm intelligence-based protocols. The obtained results prove that the proposed protocol has increased 9%, 8%, and 5% compared with the ABC-SD protocol in terms of network life, network coverage, and amount of data transmitted, respectively.
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Renton, Jessie, and Karen M. Page. "Evolution of cooperation in an epithelium." Journal of The Royal Society Interface 16, no. 152 (2019): 20180918. http://dx.doi.org/10.1098/rsif.2018.0918.
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Cooperation is prevalent in nature, not only in the context of social interactions within the animal kingdom but also on the cellular level. In cancer, for example, tumour cells can cooperate by producing growth factors. The evolution of cooperation has traditionally been studied for well-mixed populations under the framework of evolutionary game theory, and more recently for structured populations using evolutionary graph theory (EGT). The population structures arising due to cellular arrangement in tissues, however, are dynamic and thus cannot be accurately represented by either of these frameworks. In this work, we compare the conditions for cooperative success in an epithelium modelled using EGT, to those in a mechanical model of an epithelium—the Voronoi tessellation (VT) model. Crucially, in this latter model, cells are able to move, and birth and death are not spatially coupled. We calculate fixation probabilities in the VT model through simulation and an approximate analytic technique and show that this leads to stronger promotion of cooperation in comparison with the EGT model.
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GVK, Sasirekha, and Jyotsna Bapat. "Evolutionary Game Theory-Based Collaborative Sensing Model in Emergency CRAHNs." Journal of Electrical and Computer Engineering 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/696571.
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Game theory has been a tool of choice for modeling dynamic interactions between autonomous systems. Cognitive radio ad hoc networks (CRAHNs) constituted of autonomous wireless nodes are a natural fit for game theory-based modeling. The game theory-based model is particularly suitable for “collaborative spectrum sensing” where each cognitive radio senses the spectrum and shares the results with other nodes such that the targeted sensing accuracy is achieved. Spectrum sensing in CRAHNs, especially when used in emergency scenarios such as disaster management and military applications, needs to be not only accurate and resource efficient, but also adaptive to the changing number of users as well as signal-to-noise ratios. In addition, spectrum sensing mechanism must also be proactive, fair, and tolerant to security attacks. Existing work in collaborative spectrum sensing has mostly been confined to resource efficiency in static systems using request-based reactive sensing resulting in high latencies. In this paper, evolutionary game theory (EGT) is used to model the behavior of the emergency CRAHNS, providing an efficient model for collaborative spectrum sensing. The resulting implementation model is adaptive to the changes in its environment such as signal-to-noise ratio and number of users in the network. The analytical and simulation models presented validate the system design and the desired performance.
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Zhao, Nan, Shuaili Miao, and Yuan Zhang. "A Novel Co-Evolution Model Based on Evolutionary Game about Social Network." Symmetry 14, no. 3 (2022): 581. http://dx.doi.org/10.3390/sym14030581.
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With the development of information networks, information diffusion becomes increasingly complicated in social networks, and the influence from different neighbors presents asymmetry. Evolutionary Game Theory (EGT), which orients the human interaction from the perspective of economics, has been widely concerned. We establish a collaborative evolution model of public opinion information and views based on dynamic evolutionary games of social networks and the underlying asymmetry relationship. In addition, the coupling mechanism of behavior and viewpoints is adopted to study the coupling evolution of the group behavior and viewpoint. Some interesting and valuable results about evolution of the behavior and viewpoints are shown.
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Moch, Enrico. "Game Theory and the Dynamics of Entrepreneurial Decisions in Free Markets." East African Journal of Business and Economics 8, no. 1 (2025): 306–27. https://doi.org/10.37284/eajbe.8.1.2877.
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This paper examines the role of game theory in entrepreneurial decision-making within dynamic markets. While classic models like the Nash Equilibrium explain strategic interactions, they often overlook market changes, innovation cycles, and adaptive entrepreneurship. The Austrian School's market process theory focuses on entrepreneurial discovery and continuous adaptation instead of rigid strategies. Case studies of Tesla and Uber compare game theory with Austrian economics. Simulation-based models, such as agent-based modelling (ABM) and evolutionary game theory (EGT), assess competitive adaptability and strategic decision-making under uncertainty. Findings suggest that static equilibrium models fail to capture strategic flexibility and industry transformation. Entrepreneurs succeed by iterating rather than following fixed plans. Simulation-based models better reflect competitive dynamics, showing that organizations embracing agile learning and adaptation maintain an advantage over those relying on traditional optimization. This paper highlights the need to integrate entrepreneurial theories with dynamic game-theoretic approaches. This paper further proposes a dynamic game-theoretic model that integrates entrepreneurial discovery, uncertainty, and adaptive market shaping
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Leboucher, Cédric, Rachid Chelouah, Patrick Siarry, and Stéphane Le Ménec. "A Swarm Intelligence Method Combined to Evolutionary Game Theory Applied to the Resources Allocation Problem." International Journal of Swarm Intelligence Research 3, no. 2 (2012): 20–38. http://dx.doi.org/10.4018/jsir.2012040102.
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This paper addresses an allocation problem and proposes a solution using a swarm intelligence method. The application of swarm intelligence has to be discrete. This allocation problem can be modelled as a multi-objective optimization problem where the authors minimize the time and the distance of the total travel in a logistic context. This study uses a hybrid Discrete Particle Swarm Optimization (DPSO) method combined to Evolutionary Game Theory (EGT). One of the main implementation issues of DPSO is the choice of inertial, individual, and social coefficients. In order to resolve this problem, those coefficients are optimised by using a dynamical approach based on EGT. The strategies are either to keep going with only inertia, only with individual, or only with social coefficients. Since the optimal strategy is usually a mixture of the three, the fitness of the swarm can be maximized when an optimal rate for each coefficient is obtained. Evolutionary game theory studies the behaviour of large populations of agents who repeatedly engage in strategic interactions. Changes in behaviour in these populations are driven by natural selection via differences in birth and death rates. To test this algorithm, the authors create a problem whose solution is already known. This study checks whether this adapted DPSO method succeeds in providing an optimal solution for general allocation problems.
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Yang, Shuxia, Xianguo Zhu, and Shengjiang Peng. "Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory." Energies 13, no. 8 (2020): 2065. http://dx.doi.org/10.3390/en13082065.
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In recent years, China’s terminal clean power replacement construction has experienced rapid development, and China’s installed photovoltaic and wind energy capacity has soared to become the highest in the world. Precise and effective prediction of the scale of terminal clean power replacement can not only help make reasonable adjustments to the proportion of clean power capacity, but also promote the reduction of carbon emissions and enhance environmental benefits. In order to predict the prospects of China’s terminal clean energy consumption, first of all, the main factors affecting the clean power of the terminal are screened by using the grey revelance theory. Then, an evolutionary game theory (EGT) optimized least squares support vector machine (LSSVM) machine intelligence algorithm and an adaptive differential evolution (ADE) algorithm are applied in the example analysis, and empirical analysis shows that this model has a strong generalization ability, and that the prediction result is better than other models. Finally, we use the EGT–ADE–LSSVM combined model to predict China’s terminal clean energy consumption from 2019 to 2030, which showed that the prospect of China’s terminal clean power consumption is close to forty thousand billion KWh.
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Magpantay, Daryl. "S-invariant Termwise Addition of Reactions Via Reaction Vector Multiples (STAR-RVM) Transformation." European Journal of Pure and Applied Mathematics 16, no. 4 (2023): 2557–80. http://dx.doi.org/10.29020/nybg.ejpam.v16i4.4859.
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Interest in connecting Chemical Reactions Network Theory (CRNT) and evolutionary game theory (EGT) arise viewing the tools of network in the analysis of evolutionary games. Here, the evolution of population species is studied as a biological phenomenon and describing the rate of such changes through a replicator system becomes a focus. A set of polynomial kinetics (POK) may then be introduced for the realization of this replicator system and this is based on the polynomial payoff functions defined in the game. These polynomial kinetics result in polynomial dynamical systems of ordinary differential equations, which are used in analyzing strategies that prove beneficial under certain conditions. From the CRNT point of view, it now becomes interesting to study a superset of POK, which we call poly-PL kinetics (PYK). This set is formed by getting nonnegative linear combinations of power law functions. Thus, PYK contains the set PLK of power law kinetics as mono-PL kinetics with coefficient 1. Seeing this connection between CRNT and EGT and what are known about power law kinetics, we take an interest in studying PYK systems. This paper aims to analyze different ways of transforming PYK to PLK in order to explore some approaches for CRNT analysis of PYK systems. Specifically, we study a network structure-oriented transformations using the S-invariant term-wise addition of reactions (STAR) Via Reaction Vector Multiples (RVM) that transform PYK to PLK systems.
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Li, Jiangchao, and Shilei Yang. "Analysis of a Multiparticipant Game under a Subsidy and Punishment Mechanism: An Evolutionary Theory Perspective." Mathematical Problems in Engineering 2021 (July 28, 2021): 1–20. http://dx.doi.org/10.1155/2021/1984676.
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In a market with intense competition, cost pressures tempt enterprises to seek profits in ways that infringe on the interests of consumers. This is especially true when market sentiment is weak. In such situations, governments play a vital role in protecting consumers’ interests and helping struggling enterprises. We construct a tripartite game model that includes the government, enterprises, and consumers under a subsidy and punishment mechanism. We use this model to investigate the strategic choices made by the participants in an evolutionary game theory (EGT) framework. We present four stable equilibrium points as pure strategy solutions with the aid of a replicator dynamic system. Three main findings are presented in this paper. First, not all equilibrium points can be evolutionary stable strategies (ESSs) when considering the potential motivations of the participants to change strategies. Second, there is an equilibrium point that satisfies the stability condition but changes periodically in its strategy space; strategy changes between participants are not synchronized. Third, the government prefers to subsidize enterprises when enterprise speculation is serious or when enterprise investment in improving production technology is high.
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Gao, Ye, Renfu Jia, Yi Yao, and Jiahui Xu. "Evolutionary Game Theory and the Simulation of Green Building Development Based on Dynamic Government Subsidies." Sustainability 14, no. 12 (2022): 7294. http://dx.doi.org/10.3390/su14127294.
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The carbon emissions of the construction industry pose a significant challenge to implementing China’s carbon peaking and carbon neutrality goals. This study considered how to promote stable green building (GB) development. First, evolutionary game theory (EGT) was applied to examine the interaction mechanism of complex behaviors between governments and developers, constructing two scenarios of static and dynamic subsidies. Second, we proposed the ideal state where the government does not give funding subsidies and developers take the initiative to build GBs. On this basis, the simulation method was used to verify the game models and primary conclusions. Finally, a detailed sensitivity analysis of selected parameters was undertaken. The results demonstrated that subsidy policy phase-outs could help in the development of GBs; the probability of an ideal state was positively correlated with government supervision and punishment, and it was negatively correlated with government funding subsidies. The research results can be used as a reference for the government to improve incentive measures and decision support for stakeholders to adjust their strategies.
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Zheng, Yi, and Yaoqun Xu. "Optimizing Green Strategy for Retired Electric Vehicle Battery Recycling: An Evolutionary Game Theory Approach." Sustainability 15, no. 21 (2023): 15464. http://dx.doi.org/10.3390/su152115464.
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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.
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Huang, Yao Xuan, Pin Chao Liao, Chung Han Tsai, and Shu Qiang Gui. "Modeling the Relationships of Factors Affecting Dissemination of Ground Source Heat Pump (GSHP) in China." Advanced Materials Research 723 (August 2013): 976–84. http://dx.doi.org/10.4028/www.scientific.net/amr.723.976.
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Ground source heat pump (GSHP) systems have been employed in the Chinese building sector and many policies and legislations have been issued by the government for its dissemination because of its great potential for energy saving. However, the relationships among the factors affecting dissemination of such technologies have been little studied and therefore, supporting measures may have limited impact on their dissemination. This research employed Evolutionary Game Theory (EGT) to model the associations among the factors affecting dissemination of Ground Source Heat Pump (GSHP) systems in China, laying out a theoretical foundation for policy makers and future research.
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Hafezalkotob, Ashkan, Reza Mahmoudi, Elham Hajisami, and Hui Ming Wee. "Wholesale-retail pricing strategies under market risk and uncertain demand in supply chain using evolutionary game theory." Kybernetes 47, no. 6 (2018): 1178–201. http://dx.doi.org/10.1108/k-02-2017-0053.
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Purpose Nowadays, uncertainty in market demand poses considerable risk to the retailers that supply the market. On the other hand, the risk-averse behaviors of retailers toward risk may have evolved over time. Considering a supply chain including a manufacturer and a population of retailers, the authors intend to investigate how the population of retailers tends to evolve toward risk-averse behavior. Moreover, this study aims to evaluate the effects of wholesale-retail price of manufacturer on evolutionary stable strategy (ESS) of the retailers. Design/methodology/approach Due to market uncertainty, a supply chain with a population of risk-averse and risk-neutral retailers was investigated. The wholesale pricing strategy is determined by a manufacturer acting as a leader, while retailers who make order quantity decisions act as followers. An integrated Cournot duopoly equilibrium and evolutionary game theory (EGT) approach has been used to model this situation. Findings A numerical real-world case study using Iran Khodro Company is analyzed by applying the proposed EGT approach. The study provides managerial insights to the manufacturer as well as retailers in developing their strategies. Results showed that risk behavior of retailers significantly affects optimal wholesale/retail price, profits and ESS. In the long term, the retailers tend to have a risk-neutral behavior to gain more profit. In the short term, if a retailer choses risk-averse strategy, in the long term, it will change its strategy to obtain more profit and remain in the competitive market. Originality/value The contributions in this research are fourfold. First, ESS concept to investigate the risk-averse or risk-neutral attitudes of the retailers was used. Second, the uncertain risk behavior of the competing retailers was considered. Third, the effect of varying wholesale pricing was investigated. Fourth, the equilibrium wholesale and retail prices have been obtained by considering uncertainty demand and risk.
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Pan, Ke, Li Wang, and Lingyu Zhang. "A Study on Enhancing the Information Security of Urban Traffic Control Systems Using Evolutionary Game Theory." Electronics 12, no. 23 (2023): 4856. http://dx.doi.org/10.3390/electronics12234856.
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In recent years, there has been significant development in intelligent technologies for urban traffic control, such as smart city and vehicle-to-everything (V2X) communication. These advancements aim to provide more efficient and convenient services to participants in urban transportation. As the urban traffic control (UTC) system integrates with various networks and physical infrastructure, the potential threats of malicious attacks and breaches pose significant risks to the safety of individuals and their properties. To address this issue, this academic paper focuses on studying the network structure of the UTC system. A signal security game model is constructed based on the concepts of evolutionary game theory (EGT), involving three parties: attackers, upper computers (UC), and traffic signal machines (TSM). The model aims to analyze the evolutionary stability of the strategies chosen by each party, and to explore the relationships between various factors and the strategy choices of the three parties. Furthermore, the stability of equilibrium points in the three-party game system is analyzed using the Liapunov method. The conditions in which UC and TSM, dependent on detection rates and defense costs, choose to abandon defense at pure-strategy equilibrium points were obtained. Finally, MATLAB is utilized for simulation analysis to validate the impact of attack costs, defense costs, and detection rates on the information security of UTC systems.
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Deris, Atefeh, and Mahdi Sohrabi-Haghighat. "Abiraterone-Docetaxel scheduling for metastatic castration-resistant prostate cancer based on evolutionary dynamics." PLOS ONE 18, no. 3 (2023): e0282646. http://dx.doi.org/10.1371/journal.pone.0282646.
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Patients with metastatic castration-resistant prostate cancer (mCRPC) are divided into three groups based on their response to Abiraterone treatment: best responder, responder, and non-responder. In the latter two groups, successful outcomes may not be achieved due to the development of drug-resistant cells in the tumor environment during treatment. To overcome this challenge, a secondary drug can be used to control the population of drug-resistant cells, potentially leading to a longer period of disease inhibition. This paper proposes using a combination of Docetaxel and Abiraterone in some polytherapy methods to control both the overall cancer cell population and the drug-resistant subpopulation. To investigate the competition and evolution of mCRPC cancer phenotypes, as in previous studies, the Evolutionary Game Theory (EGT) has been used as a mathematical modeling of evolutionary biology concepts.
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Dong, Changqi, Jida Liu, and Jianing Mi. "How to Enhance Data Sharing in Digital Government Construction: A Tripartite Stochastic Evolutionary Game Approach." Systems 11, no. 4 (2023): 212. http://dx.doi.org/10.3390/systems11040212.
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Digital government construction is a complex system project, and data sharing is its governance niche. Cross-sectoral data sharing is the core issue of improving governance capacity in the construction of digital governments. Aimed at the dilemma of insufficient data sharing across departments, according to evolutionary game theory (EGT), we refined the game relationship between the data management department and the different government functional departments participating in cross-department data sharing. We used white Gaussian noise as a random perturbation, constructed a tripartite stochastic evolutionary game model, analyzed the stability of the stochastic game system and studied the influence of the main parameters on the evolution of the game system with the help of numerical simulation. The results show that there exists a positive stable point in the process of cross-department data sharing. The external effect of data sharing can be improved by enhancing the investment in data sharing by government functional departments. The accumulation of interagency trust relationships can gradually eliminate the differences in data sharing among different departments. The coordination mechanism of government data sharing and the construction of the “good and bad reviews” system can form an internal and external adjustment mechanism for functional departments and the data management department and can promote multiple departments to participate in cross-department data sharing more actively.
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Guo, Yan, Jiajun Lin, and Weiqing Zhuang. "An Evolutionary Game-Based Regulatory Path for Algorithmic Price Discrimination in E-Commerce Platforms." Mathematics 12, no. 17 (2024): 2774. http://dx.doi.org/10.3390/math12172774.
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With the advent of big data, the swift advancement of diverse algorithmic technologies has enhanced the transaction efficiency of the e-commerce business. Nevertheless, it is crucial to acknowledge that e-commerce platforms might employ algorithmic technology to enforce differential pricing for various consumers with the aim of maximizing profits, thus infringing upon the lawful rights and interests of consumers. This paper focuses on the algorithmic price discrimination commonly observed on e-commerce platforms. To effectively regulate this behavior, the paper utilizes evolutionary game theory (EGT) to analyze the strategies employed by e-commerce platforms, consumers, and market regulators to achieve stability. This research employs a real-life situation and utilizes parametric simulation to visualize and analyze the process and outcomes of the three-party evolutionary game. The results demonstrate the credibility and feasibility of the article’s findings. Based on our research, we have reached the following findings: During the process of evolution, the strategic decisions made by the game participants from the three parties will mutually impact each other, and various elements exert varying degrees of influence on the strategic choices made by the game participants from each party. Collaborative governance can enable consumers and market regulators to regulate the discriminatory pricing behavior of e-commerce platforms effectively. This article offers valuable insights into the governance of violations in the e-commerce sector based on robust data and model research.
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Tang, Jianlin, Bin Qian, Yi Luo, et al. "Evolutionary Game Theory-Based Analysis of Power Producers’ Carbon Emission Reduction Strategies and Multi-Group Bidding Dynamics in the Low-Carbon Electricity Market." Processes 13, no. 4 (2025): 952. https://doi.org/10.3390/pr13040952.
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China’s power generation system has undergone reforms, leading to a competitive electricity market where independent producers participate through competitive bidding. With the rise of low-carbon policies, producers must optimize bidding strategies while reducing carbon emissions, creating complex interactions with local governments. Evolutionary game theory (EGT) is well-suited to analyze these dynamics. This study begins by summarizing the fundamental concepts of electricity trading markets, including transaction models, bidding mechanisms, and carbon reduction strategies. Existing research on the application of evolutionary game theory in power markets is reviewed, with a focus on theoretical constructs such as evolutionary stable strategies and replicator dynamics. Based on this foundation, the study conducts a detailed mathematical analysis of symmetric and asymmetric two-group evolutionary game models in general market scenarios. Building upon these models, a three-group evolutionary game framework is developed to analyze interactions within power producer groups and between producers and regulators under low-carbon mechanisms. A core innovation of this study is the incorporation of a case study based on China’s electricity market, which examines the evolutionary dynamics between local governments and power producers regarding carbon reduction strategies. This includes analyzing how regulatory incentives, market-clearing prices, and demand-side factors influence producers’ bidding and emission reduction behaviors. The study also provides a detailed analysis of the bidding strategies for small, medium, and large power producers, revealing the significant impact of carbon pricing and market-clearing prices on strategic decision-making. Specifically, the study finds that small producers tend to adopt more conservative bidding strategies, aligning closely with market-clearing prices, while large producers take advantage of economies of scale, adjusting their strategies at higher capacities. The study explores the conditions under which carbon emission reduction strategies achieve stable equilibrium, as well as the implications of these equilibria for both market efficiency and environmental sustainability. The study reveals that integrating carbon reduction strategies into power market dynamics significantly impacts bidding behaviors and long-term market stability, especially under the influence of governmental penalties and incentives. The findings provide actionable insights for both power producers and policymakers, contributing to the advancement of low-carbon market theories and supporting the global transition to sustainable energy systems.
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Ding, Meng, and Hui Zeng. "Multi-Agent Evolutionary Game in the Recycling Utilization of Sulfate-Rich Wastewater." International Journal of Environmental Research and Public Health 19, no. 14 (2022): 8770. http://dx.doi.org/10.3390/ijerph19148770.
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Current industrial development has led to an increase in sulfate-rich industrial sewage, threatening industrial ecology and the environment. Incorrectly treating high-concentration sulfate wastewater can cause serious environmental problems and even harm human health. Water with high sulfate levels can be treated as a resource and treated harmlessly to meet the needs of the circular economy. Today, governments worldwide are working hard to encourage the safe disposal and reuse of industrial salt-rich wastewater by recycling sulfate-rich wastewater (SRW) resources. However, the conflict of interests between the SRW production department, the SRW recycling department, and the governments often make it challenging to effectively manage sulfate-rich wastewater resources. This study aims to use the mechanism of evolutionary game theory (EGT) to conduct theoretical modelling and simulation analysis on the interaction of the behaviour of the above three participants. This paper focuses on the impact of government intervention and the ecological behaviour of wastewater producers on the behavioural decisions of recyclers. The results suggest that the government should play a leading role in developing the SRW resource recovery industry. SRW producers protect the environment in the mature stage, and recyclers actively collect and recover compliant sulfate wastewater resources. Governments should gradually deregulate and eventually withdraw from the market. Qualified recyclers and environmentally friendly wastewater producers can benefit from a mature SRW resources recovery industry.
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Cheng, Lefeng, Pengrong Huang, Mengya Zhang, Ru Yang, and Yafei Wang. "Optimizing Electricity Markets Through Game-Theoretical Methods: Strategic and Policy Implications for Power Purchasing and Generation Enterprises." Mathematics 13, no. 3 (2025): 373. https://doi.org/10.3390/math13030373.
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This review proposes a novel integration of game-theoretical methods—specifically Evolutionary Game Theory (EGT), Stackelberg games, and Bayesian games—with deep reinforcement learning (DRL) to optimize electricity markets. Our approach uniquely addresses the dynamic interactions among power purchasing and generation enterprises, highlighting both theoretical underpinnings and practical applications. We demonstrate how this integrated framework enhances market resilience, informs evidence-based policy-making, and supports renewable energy expansion. By explicitly connecting our findings to regulatory strategies and real-world market scenarios, we underscore the political implications and applicability of our results in diverse global electricity systems. By integrating EGT with advanced methodologies such as DRL, this study develops a comprehensive framework that addresses both the dynamic nature of electricity markets and the strategic adaptability of market participants. This hybrid approach allows for the simulation of complex market scenarios, capturing the nuanced decision-making processes of enterprises under varying conditions of uncertainty and competition. The review systematically evaluates the effectiveness and cost-efficiency of various control policies implemented within electricity markets, including pricing mechanisms, capacity incentives, renewable integration incentives, and regulatory measures aimed at enhancing market competition and transparency. Our analysis underscores the potential of EGT to significantly enhance market resilience, enabling electricity markets to better withstand shocks such as sudden demand fluctuations, supply disruptions, and regulatory changes. Moreover, the integration of EGT with DRL facilitates the promotion of sustainable energy integration by modeling the strategic adoption of renewable energy technologies and optimizing resource allocation. This leads to improved overall market performance, characterized by increased efficiency, reduced costs, and greater sustainability. The findings contribute to the development of robust regulatory frameworks that support competitive and efficient electricity markets in an evolving energy landscape. By leveraging the dynamic and adaptive capabilities of EGT and DRL, policymakers can design regulations that not only address current market challenges but also anticipate and adapt to future developments. This proactive approach is essential for fostering a resilient energy infrastructure capable of accommodating rapid advancements in renewable technologies and shifting consumer demands. Additionally, the review identifies key areas for future research, including the exploration of multi-agent reinforcement learning techniques and the need for empirical studies to validate the theoretical models and simulations discussed. This study provides a comprehensive roadmap for optimizing electricity markets through strategic and policy-driven interventions, bridging the gap between theoretical game-theoretic models and practical market applications.
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Loo, Bee Wah, Pei Ling Tan, Siew Kian Tey, and Wan Yoke Chin. "Authentication Methods Selection in Information Security through Hybrid AHP and EGT." Journal of Advanced Research in Applied Sciences and Engineering Technology 50, no. 2 (2024): 171–85. http://dx.doi.org/10.37934/araset.50.2.171185.
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The information security leader frequently encounters the challenge of choosing the appropriate defence strategy. Effective multi-criteria decision-making (MCDM) is essential in the field of information security for determining the optimal strategies that involve more than one party. To address this challenge, we propose a hybrid model that combines the strengths of the Analytic Hierarchy Process (AHP) with Evolutionary Game Theory (EGT). The hybrid model helps the information security leader assess the criteria for security controls and make the optimal decisions to protect the organization's data. Initially, the AHP is utilized to assess the criteria of information security control. Subsequently, the priority of the alternatives is established through evaluating these criteria. Furthermore, we will construct a defence-attack circumstance using the EGT framework, which involves formulating strategies and determining payoffs for both the information security leaders and attackers involved. We utilize the replicator dynamic to examine the process of evolution in the game, resulting in the determination of the optimal strategy. A case study is conducted to determine the optimal strategy for information security leaders and attackers. The result indicates that the best defence strategy is password protection, followed by token-based and biometric-based protections. On the other hand, the optimal strategy for attackers is no attack, followed by attack and moderate attack. This study contributes to the multi-criteria decision-making (MCDM) problem’s solving by considering the dynamic aspect between both defender and attacker in the context of information security.
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Zhang, Chen, Yaoqun Xu, and Yi Zheng. "Blockchain Traceability Adoption in Low-Carbon Supply Chains: An Evolutionary Game Analysis." Sustainability 16, no. 5 (2024): 1817. http://dx.doi.org/10.3390/su16051817.
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Blockchain technology has brought innovation to supply chain management, particularly in managing carbon emissions in the manufacturing sector. However, there is a research gap regarding the policy tools and the role of local governments in implementing blockchain technology to achieve carbon emissions traceability. Additionally, the strategic relationships and policy implications resulting from the implementation of blockchain technology are not examined systematically. An effective method for examining the strategies used in interactions between supply chain stakeholders and governments is evolutionary game theory, or EGT. This paper employs mathematical modelling and MATLAB 2016 software simulation to examine the decision-making process of manufacturing companies when considering implementing blockchain technology traceability. Specifically, the subjects in the model include product manufacturers (PM), product suppliers (PS), and local governments (LGs). The aim is to examine the decision-making behavior of carbon traceability participants in blockchain technology. This paper analyses the most effective blockchain-based traceability strategies for low-carbon supply chain members under a variety of scenarios by modifying the parameters. The findings suggest the following: (1) Manufacturers and suppliers need to manage the cost of blockchain traceability, collaborate to create an environmentally friendly product certification system, and improve brand image. (2) Local governments should set up efficient reward and punishment systems to incentivize supply chain stakeholders to engage in the blockchain traceability system. The aforementioned discoveries furnish policymakers with guidance to encourage the implementation of blockchain-based carbon footprint traceability technology, thereby establishing a transparent carbon footprint traceability framework across the entire supply chain.
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Jeong, Seong-Sik, Hee-Sung Cha, and Jong-Han Yoon. "An Evolutionary Game Theory-Based Framework for Analyzing Behavioral Strategies in Contractor–Owner Conflicts over Additional Construction Costs." Buildings 15, no. 4 (2025): 545. https://doi.org/10.3390/buildings15040545.
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The construction industry faces increasing conflicts over additional construction costs due to economic uncertainties, such as global pandemics and wars. These disputes often lead to project delays, legal actions, and even construction halts, causing significant financial and operational losses for stakeholders. To address these challenges, this study develops a simulation model based on evolutionary game theory (EGT) to identify the key influencing factors and applies the Analytic Hierarchy Process (AHP) to analyze and manage the conflicts between contractors and owners in private construction projects. The model quantifies decision-making dynamics by calculating the relative importance of various factors under different scenarios. A proof-of-concept simulation of the model reveals that cooperative evolution dynamics significantly decrease when the cost-sharing ratio reaches 0.5 for contractors and 0.9 for owners. Furthermore, the sensitivity analysis indicates that exceeding cost-sharing thresholds undermines cooperation, increasing the risk of disputes. Through this simulation, this study concludes that fostering mutual trust and informed decision-making on cost-sharing ratio significantly reduces project disputes and enhances the stakeholders’ profitability. The developed model and its framework serve as valuable tools for providing project stakeholders with actionable insights aimed at fostering strategic behaviors that minimize dispute-driven financial risks in construction projects.
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Chen, Yu, Yantai Chen, Yanlin Guo, and Yanfei Xu. "Research on the Coordination Mechanism of Value Cocreation of Innovation Ecosystems: Evidence from a Chinese Artificial Intelligence Enterprise." Complexity 2021 (February 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/7629168.
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This paper models the game process of the value cocreation of enterprises based on evolutionary game theory (EGT). The factors influencing value cocreation are found through mathematical analysis. Taking iFLYTEK as an example, a representative enterprise of artificial intelligence (AI) in China, six factors affecting value cocreation are verified, which are the excess return rate, the distribution coefficient of the excess return rate, coordination costs in the system, the cost-sharing coefficient, imitation costs, and penalties. These six factors have a profound impact on value cocreation in the ecosystem. Through the case study of iFLYTEK, it is concluded that innovation ecosystems can enable small- and medium-sized AI enterprises to grow. In order to build a sound ecosystem, we need to establish a mechanism to select partners, reduce the costs of cooperation, and strengthen the protection of intellectual property. At the beginning of the cooperation, it is necessary to establish a mechanism with clear responsibilities, rights, and interests. The conclusion is of great significance to the development of AI enterprises.
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Pan, Yiguang, Xiaomei Deng, Rashid Maqbool, and Weirui Niu. "Insurance Crisis, Legal Environment, and the Sustainability of Professional Liability Insurance Market in the Construction Industry: Based on the US Market." Advances in Civil Engineering 2019 (July 21, 2019): 1–13. http://dx.doi.org/10.1155/2019/1614868.
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PLI (professional liability insurance) is currently the main method used to control construction practice risk and is an important economic measure of construction industry governance. Few literatures have analyzed the sustainability of the liability insurance market. In particular, the research on the sustainability of the PLI market in the construction industry is still blank. The sustainability of the market can be identified with the equilibrium of the system over a certain period of time. From the perspective of cooperation benefits, this paper adopts evolutionary game theory (EGT) to analyze the evolutionary trends of stakeholders’ behaviors and their evolutionarily stable strategy (ESS) in the PLI market of the construction industry. A case study from the history of the US PLI market evolution over nearly 100 years is taken to illustrate the stakeholder game and interpret the market evolution path, and several typical stages of the development of the US PLI market are explored. Some factors that can cause a shift in equilibrium are found. The results show that the change in the legal environment will directly affect the payoffs of the stakeholders, cause market imbalance, and trigger crisis. These findings will help out the government to regulate the market in a timely manner by improving external factors, such as by building a sound credit system and ensuring the stability of the legal system. In an equilibrium state, competitive markets can eliminate individuals with high accident rates and companies with high operating costs. Moreover, these findings will also set a base for future researches to investigate the role of insurance market and legal environment in depth while providing the intensive critical factors towards sustainable construction industry.
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Akberdina, Victoria, Grigoriy Korovin, and Aleksandra Ponomareva. "A game-theoretical model of multisubject industrial policy." SHS Web of Conferences 55 (2018): 01019. http://dx.doi.org/10.1051/shsconf/20185501019.
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The vector of industrial policy developmen aimed at the transition from the domination of the state to the involvement in its development of all stakeholders is relevant in developed countries. Such an approach requires an additional scientific justification, confirming its feasibility. The purpose and objectives of the paper is the development within the framework of game theory a model of relationships of subjects interested in the industrial policy based on their interests, strategies, areas of conflict and areas of consensus. The study used a multi-subject approach, which implies the existence of a number of independent stakeholders with their own goals and strategies. The methodology of evolutionary game theory (EGT) was used to analyze the interests of the stakeholders of their coincidences and conflicts. The process of formation of industrial policy identified three possible points of equilibrium. The interaction between the state and enterprises is formalized as a game in a normal form, the functions of utility of the players and the equation of replication dynamics are presented. To formalize the problem and finding the equations of the replicative dynamics, we have considered the problem in a general form for the continuous asymmetric games. In terms of content, the results and decisions can be used as a characteristic of the space for the creation of multiple mutually acceptable agreements between real and potential participants in the process of industrial policy formation. It is possible to further analyze the model to obtain a quantitative assessment of the factors that have the greatest impact on the motivation of the interaction participants.
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Cheng, Hanlei, Jian Li, Jing Lu, Sio-Long Lo, and Zhiyu Xiang. "Incentive-Driven Information Sharing in Leasing Based on a Consortium Blockchain and Evolutionary Game." Journal of Theoretical and Applied Electronic Commerce Research 18, no. 1 (2023): 206–36. http://dx.doi.org/10.3390/jtaer18010012.
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Blockchain technology (BCT) provides a new way to mitigate the default risks of lease contracts resulting from the information asymmetry in leasing. The conceptual architecture of a consortium blockchain-based leasing platform (CBLP) is first proposed to facilitate information sharing between small and medium-sized enterprises (SMEs, the “lessees”) and leasing firms (LFs, the “lessors”). Then, based on evolutionary game theory (EGT), this study builds a two-party game model and analyzes the influences of four types of factors (i.e., information sharing, credit, incentive–penalty, and risk) on SMEs’ contract compliance or default behaviors with/without blockchain empowerment. The primary findings of this study are as follows: (1) SMEs and LFs eventually evolve to implement the ideal “win–win” strategies of complying with the contract and adopting BCT. (2) The large residual value of the leased asset can tempt SMEs to conduct a default action of unauthorized asset disposal, while leading LFs to access the CBLP to utilize information shared on-chain. (3) When the maintenance service is outsourced instead of being provided by lessors, the maintenance fee is not a core determinant affecting the equilibrium state. (4) There is a critical value concerning the default penalty on-chain to incentivize the involved parties to keep their commitments. (5) The capability of utilizing information, storage overhead, and security risk should all be taken into consideration when deciding on the optimal strategies for SMEs and LFs. This study provides comprehensive insights for designing an incentive mechanism to encourage lessees and lessors to cooperatively construct a sustainable and trustworthy leasing environment.
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Kabir, K. M. Ariful, and Jun Tanimoto. "Evolutionary game theory modelling to represent the behavioural dynamics of economic shutdowns and shield immunity in the COVID-19 pandemic." Royal Society Open Science 7, no. 9 (2020): 201095. http://dx.doi.org/10.1098/rsos.201095.
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The unprecedented global spread of COVID-19 has prompted dramatic public-health measures like strict stay-at-home orders and economic shutdowns. Some governments have resisted such measures in the hope that naturally acquired shield immunity could slow the spread of the virus. In the absence of empirical data about the effectiveness of these measures, policymakers must turn to epidemiological modelling to evaluate options for responding to the pandemic. This paper combines compartmental epidemiological models with the concept of behavioural dynamics from evolutionary game theory (EGT). This innovation allows us to model how compliance with an economic lockdown might wane over time, as individuals weigh the risk of infection against the certainty of the economic cost of staying at home. Governments can, however, increase spending on social programmes to mitigate the cost of a shutdown. Numerical analysis of our model suggests that emergency-relief funds spent at the individual level are effective in reducing the duration and overall economic cost of a pandemic. We also find that shield immunity takes hold in a population most easily when a lockdown is enacted with relatively low costs to the individual. Our qualitative analysis of a complex model provides evidence that the effects of shield immunity and economic shutdowns are complementary, such that governments should pursue them in tandem.
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NIE, PU-YAN, and PEI-AI ZHANG. "FIXATION TIME FOR EVOLUTIONARY GRAPHS." International Journal of Modern Physics B 24, no. 27 (2010): 5285–93. http://dx.doi.org/10.1142/s0217979210056852.
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Evolutionary graph theory (EGT) is recently proposed by Lieberman et al. in 2005. EGT is successful for explaining biological evolution and some social phenomena. It is extremely important to consider the time of fixation for EGT in many practical problems, including evolutionary theory and the evolution of cooperation. This study characterizes the time to asymptotically reach fixation.
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Beunen, Raoul, and Kristof Van Assche. "Steering in Governance: Evolutionary Perspectives." Politics and Governance 9, no. 2 (2021): 365–68. http://dx.doi.org/10.17645/pag.v9i2.4489.
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Steering has negative connotations nowadays in many discussions on governance, policy, politics and planning. The associations with the modernist state project linger on. At the same time, a rethinking of what is possible by means of policy and planning, what is possible through governance, which forms of change and which pursuits of common goods still make sense, in an era of cynicism about steering yet also high steering expectations, seems eminently useful. Between laissez faire and blue-print planning are many paths which can be walked. In this thematic issue, we highlight the value of evolutionary understandings of governance and of governance in society, in order to grasp which self-transformations of governance systems are more likely than others and which governance tools and ideas stand a better chance than others in a particular context. We pay particular attention to Evolutionary Governance Theory (EGT) as a perspective on governance which delineates steering options as stemming from a set of co-evolutions in governance. Understanding steering options requires, for EGT, path mapping of unique governance paths, as well as context mapping, the external contexts relevant for the mode of reproduction of the governance system in case. A rethinking of steering in governance, through the lens of EGT, can shed a light on governance for innovation, sustainability transitions, new forms of participation and self-organization. For EGT, co-evolutions and dependencies, not only limit but also shape possibilities of steering, per path and per domain of governance and policy.
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Michel, Arij. "Evolutionary Game Theory." International Journal of Circular Economy and Waste Management 1, no. 2 (2021): 20–28. http://dx.doi.org/10.4018/ijcewm.2021070103.
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The article uses evolutionary game theory analysis as the research object, which is the most commonly used research method of institutional change, and summarizes some methods in the research of institutional change, and points out the advantages and disadvantages of evolutionary game analysis in the research of institutional change and through the comparison of cutting-edge methods and evolutionary games to see the development direction of future research institutional changes.
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Ortmann, Andreas, and Jorgen W. Weibull. "Evolutionary Game Theory." Southern Economic Journal 63, no. 3 (1997): 834. http://dx.doi.org/10.2307/1061129.
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Fudenberg, Drew, Jörgen W. Weibull, and Jorgen W. Weibull. "Evolutionary Game Theory." Scandinavian Journal of Economics 98, no. 3 (1996): 461. http://dx.doi.org/10.2307/3440739.
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Munro, Alistair. "Evolutionary Game Theory." Economic Journal 107, no. 440 (1997): 218–19. http://dx.doi.org/10.1093/ej/107.440.218.
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Sigmund, Karl, and Martin A. Nowak. "Evolutionary game theory." Current Biology 9, no. 14 (1999): R503—R505. http://dx.doi.org/10.1016/s0960-9822(99)80321-2.
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Smith, John Maynard. "Evolutionary game theory." Physica D: Nonlinear Phenomena 22, no. 1-3 (1986): 43–49. http://dx.doi.org/10.1016/0167-2789(86)90232-0.
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Van Damme, Eric. "Evolutionary game theory." European Economic Review 38, no. 3-4 (1994): 847–58. http://dx.doi.org/10.1016/0014-2921(94)90121-x.
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Hartley, Kris, and Michael Howlett. "Policy Assemblages and Policy Resilience: Lessons for Non-Design from Evolutionary Governance Theory." Politics and Governance 9, no. 2 (2021): 451–59. http://dx.doi.org/10.17645/pag.v9i2.4170.
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Evolutionary governance theory (EGT) provides a basis for holistically analyzing the shifting contexts and dynamics of policymaking in settings with functional differentiation and complex subsystems. Policy assemblages, as mixes of policy tools and goals, are an appropriate unit of analysis for EGT because they embody the theory’s emphasis on co-evolving elements within policy systems. In rational practice, policymakers design policies within assemblages by establishing objectives, collecting information, comparing options, strategizing implementation, and selecting instruments. However, as EGT implies, this logical progression does not always materialize so tidily—some policies emerge from carefully considered blueprints while others evolve from muddled processes, laissez faire happenstance, or happy accident. Products of the latter often include loosely steered, unmoored, and ‘non-designed’ path dependencies that confound linear logic and are understudied in the policy literature. There exists the need for a more intricate analytical vocabulary to describe this underexplored ‘chaotic’ end of the policy design spectrum, as conjuring images of ‘muddles’ or ‘messes’ has exhausted its usefulness. This article introduces a novel metaphor for non-design—the bird nest—to bring studies of policy design and non-design into lexical harmony.
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Harper, Marc. "Escort evolutionary game theory." Physica D: Nonlinear Phenomena 240, no. 18 (2011): 1411–15. http://dx.doi.org/10.1016/j.physd.2011.04.008.
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