Relevant bibliographies by topics / Evolutionary Game Theory (EGT)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Evolutionary Game Theory (EGT)'

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Published: 5 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "Evolutionary Game Theory (EGT)"

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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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Dissertations / Theses on the topic "Evolutionary Game Theory (EGT)"

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Jiang, Ge. "Essays in evolutionary game theory." Thesis, University of Essex, 2016. http://repository.essex.ac.uk/16917/.

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This thesis contains three essays in evolutionary game theory. In the first chapter, we study the impact of switching costs on the long run outcome in 2X2 coordination games played in the circular city model of local interactions. We find that for low levels of switching costs, the risk dominant convention is the unique long run equilibrium. For intermediate levels of switching costs the set of long run equilibria contains the risk dominant convention but may also contain conventions that are not risk dominant. For high levels of switching costs also nonmonomorphic states will be included in the set of LRE. We study the impact of location heterogeneity on neighborhood segregation in the one-dimensional Schelling residential model in the second chapter. We model location heterogeneity by introducing an advantageous node, in which a player’s utility is impartial to the composition of her neighborhood. We find that when every player interacts with two neighbors, one advantageous node in the circular city will lead to a result that segregation is no longer the unique LRE. When players interact with more neighbors, more advantageous nodes are necessary to obtain the same result. In the third chapter, we consider a model of social coordination and network formation, where players of two groups play a 2X2 coordination game when connected. Players in one group actively decide on whom they play with and on the action in the game, while players in the other group decide on the action in the game only. We find that if either group’s population size is small in comparison to the linking restriction, all players will choose the risk dominant equilibrium, while when both groups are sufficiently large in population, the players of two groups will coordinate on the payoff dominant action.
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Ghachem, Montasser. "Essays in Evolutionary Game Theory." Doctoral thesis, Stockholms universitet, Nationalekonomiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-132433.

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Evolutionary game theory tries to explain the emergence of stable behaviors observed in human and animal societies. Prominent examples of such behaviors are cooperative and conformist behaviors. In the first part of the thesis, we develop a model of indirect reciprocity with institutional screening to study how institutions may promote cooperative behavior. We show that cooperation can emerge if screening institutions are sufficiently reliable at identifying cooperators. The second part presents a large-population learning model in which individuals update their beliefs through time. In the model, only one individual updates his beliefs each period. We show that a population, playing a game with two strategies, eventually learns to play a Nash equilibrium. We focus on coordination games and prove that a unique behavior arises both when players use myopic and perturbed best replies. The third part studies the payoff calculation in an evolutionary setting. By introducing mutual consent as a requirement for game play, we provide a more realistic alternative way to compute payoffs.<br><p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript.</p>
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Tsakas, Elias. "Essays on epistemology and evolutionary game theory." Göteborg : Dept. of Economics, School of Economics and Commercial Law, Göteborg University, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016654920&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Wallace, Christopher Conor. "Evolutionary game theory in the social sciences." Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322749.

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Beauparlant, Marc A. "Evolutionary Game Theory and the Spread of Influenza." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35635.

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Vaccination has been used to control the spread of infectious diseases for centuries with widespread success. Deterministic models studying the spread of infectious disease often use the assumption of mass vaccination; however, these models do not allow for the inclusion of human behaviour. Since current vaccination campaigns are voluntary in nature, it is important to extend the study of infectious disease models to include the effects of human behaviour. To model the effects of vaccination behaviour on the spread of influenza, we examine a series of models in which individuals vaccinate according to memory or individual decision-making processes based upon self-interest. Allowing individuals to vaccinate proportionally to an exponentially decaying memory function of disease prevalence, we demonstrate the existence of a Hopf bifurcation for short memory spans. Using a game-theoretic influenza model, we determine that lowering the perceived vaccine risk may be insufficient to increase coverage to established target levels. Utilizing evolutionary game theory, we examine models with imitation dynamics both with and without a decaying memory function and show that, under certain conditions, periodic dynamics occur without seasonal forcing. Our results suggest that maintaining diseases at low prevalence with voluntary vaccination campaigns could lead to subsequent epidemics following the free-rider dilemma and that future research in disease control reliant on individual-based decision-making need to include the effects of human behaviour.
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Norman, William. "Evolutionary Game Dynamics and the Moran Model." Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420672.

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Treacy, Brian. "A stochastic differential equation derived from evolutionary game theory." Thesis, Uppsala universitet, Analys och sannolikhetsteori, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-377554.

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JAVARONE, MARCO ALBERTO. "Statistical Physics of Evolutionary Game Theory and its Applications." Doctoral thesis, Università degli Studi di Cagliari, 2017. http://hdl.handle.net/11584/249581.

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Evolutionary Game Theory represents a vibrant and interdisciplinary research field, that is attracting the interest of scientists belonging to different communities, spanning from physicists to biologists, and from mathematicians to sociologists. In few words, it represents the attempt to study the evolutionary dynamics of a population by the framework of Game Theory, taking into account the Darwinian theory of natural evolution. As result, Evolutionary Game Theory allows to model a number of scenarios, as social and biological systems, with a high level of abstraction. On one hand, the contribution of the classical Game Theory can be identified at a local level, i.e. in the interactions among the agents. For instance, when agents play games like the Prisoner’s Dilemma, according to the Nash Equilibrium, they should defect. On the other hand, in some conditions, it is possible to observe final equilibria far from the expected one. Notably, here we identify the contribution of the Darwinian theory, since the agents can change their behavior according to adaptive mechanisms. Remarkably, often populations reaching non-expected equilibria show emergent behaviors, resulting from their interaction pattern, or from specific local behaviors. For this reason, evolutionary games must be considered as complex systems. Accordingly, we believe that statistical physics constitutes one of the most suitable approaches for studying and understanding their underlying dynamics. In this scenario, one of the aims of this dissertation is to illustrate some models that let emerge a direct link between Evolutionary Game Theory and statistical physics. In addition, we show that the link between the two fields allows to envision new applications beyond the current horizon of Evolutionary Game Theory, as defining optimization strategies. So, at the beginning, we focus on a statistical physics model devised for understanding ’why’ random motion, in continuous spaces (and within a particular speed range), is able to trigger cooperation in the Prisoner’s Dilemma. Then, we study the role of the temperature in the spatial Public Goods Game, defining a link between this game and the classical Voter Model. Eventually, mapping strategies to spins, we study the spatial Public Goods Game in presence of agents susceptible to local fields, i.e. fields generated by their nearest-neighbors. It is worth to note that, from a social point of view, an agent susceptible to a local field can be considered as a conformist, since it imitates the strategy (or behavior) of majority in its neighborhood. Later, we propose three applications of Evolutionary Game Theory. In particular, the first one is a new method for solving combinatorial optimization problems. The second application is focused on the definition of a game for studying the dynamics of Poker challenges. Finally, the third application aims to represent a phenomenon of social evolution, named group formation. To conclude, we deem that the achieved results shed new light on the relation between Evolutionary Game Theory and Statistical Physics, and allow to get insights useful to devise new applications in different domains.
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Rael, Rosalyn Cherie. "Comparing theory and data on multi-species interactions using evolutionary game theory." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/194398.

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Mathematical models with fixed parameters have a long history of use in describing the dynamics of populations in ecological interactions. However, in many instances, evolutionary changes in species characteristics can have a significant influence on these dynamics. Using evolutionary game theory, we incorporate evolution into population dynamic models and apply the resulting “Darwinian dynamic” models to study the effects that evolutionary changes can have on populations in several ecological scenarios. We start with a single species (Chapter 2), then add a competitor (Chapter 3), and a predator (Chapter 4). In Chapter 2, a rigorous mathematical analysis of the Darwinian logistic model for a single species shows that stable equilibria occur at strategies that maximize population size rather than growth rate. We apply this model to the data obtained from an experimental study on genetically perturbed populations of the flour beetle Tribolium castaneum. In Chapter 3, we apply a Darwinian dynamic modification of the Lotka-Volterra model to investigate circumstances under which evolution will change expected competitive outcomes. We compare the results of our Darwinian Lotka-Volterra model to studies in which unusual observations were made in studies of the flour beetles T. castaneum and T. confusum, including a reversal in the “winner” of competitive exclusion, and evolution from exclusion to coexistence. Chapters 2 and 3 provide one of the few examples in which evolutionary game theory has been successfully applied to empirical data. From a foundation provided by the Darwinian logistic equation, we build Darwinian dynamic models with two and three trophic levels to study effects of evolution on some basic ecological interactions in Chapter 4. We show how a consumer can cause a resource (producer) species to evolve to a mean strategy that increases its growth rate rather than its population size. We also briefly study how predation on the consumer species can affect equilibrium strategies of species lower in the food chain. Our results show how evolutionary game theoretic methods can be useful for studying both theoretical and applied problems that arise due to evolutionary processes, even when they occur on a ecological time scale. They provide a foundation for the future study of evolutionary effects in larger complex networks of interacting species.
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SALOMAO, GABRIEL MEYER. "ENVIRONMENTAL POLICY REGULATION FROM THE PERSPECTIVE OF EVOLUTIONARY GAME THEORY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=31461@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO<br>Um dos principais desafios do mundo moderno é equilibrar o desenvolvimento econômico com a responsabilidade ambiental, de forma a alcançar um crescimento sustentável que minimize os impactos gerados no meio ambiente. Isto tem feito com que muitos governos e empresas estabeleçam políticas ambientais como instrumentos para a garantia de um futuro com sustentabilidade e que favoreçam o bem estar social. A proposta desta dissertação é utilizar a teoria dos jogos evolucionários para analisar as interações entre agentes reguladores de políticas ambientais de comando e controle e os agentes (firmas) que impulsionam o desenvolvimento econômico, porém que impactam de alguma forma o meio ambiente. Enquanto os primeiros precisam determinar as melhores ações estratégicas para implementar a política e aumentar o bem estar social, os segundos só irão se adequar à regulamentação ambiental estabelecida se esta escolha lhe render um maior retorno (payoff). Os resultados mostram que o custo de fiscalização é determinante para estabelecer a política ambiental mais adequada e, dependendo da magnitude deste custo, existem diferentes formas de iniciar sua implementação a fim de favorecer o melhor resultado possível sob o ponto de vista do bem estar social.<br>The modern world faces the great challenge of balancing economic development and environmental responsibility in order to achieve sustainable growth that minimizes the impacts generated on the environment. This has forced some governments and firms establish environmental policies as instruments to guarantee a future with sustainability and favors social welfare. Using evolutionary games theory, this work aims to analyze the interactions between environmental policies regulators and firms that provide economic development impacting the environment. While the firsts need to choose the best strategic actions to established and inspect environmental standards sets to increase social welfare, the company will only complies if this choice yields a better payoff. The results show how the inspection cost is decisive for establishing the better environmental policy and depending on the magnitude of this cost, there are different ways to introduce it in order to provide a better social welfare result.
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Books on the topic "Evolutionary Game Theory (EGT)"

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Tsakas, Elias. Essays on epistemology and evolutionary game theory. University of Gothenburg, 2008.

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Bomze, Immanuel M. Game theoretical foundations of evolutionary stability. Springer-Verlag, 1989.

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Cressman, Ross, ed. The Stability Concept of Evolutionary Game Theory. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-49981-4.

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Tanimoto, Jun. Fundamentals of Evolutionary Game Theory and its Applications. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-54962-8.

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Javarone, Marco Alberto. Statistical Physics and Computational Methods for Evolutionary Game Theory. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70205-6.

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Bøe, Turid. Evolutionary game theory and the battle of the sexes. Chr. Michelsen Institute, Development Studies and Human Rights, 1997.

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1945-, Sigmund Karl, ed. Evolutionary game dynamics: American Mathematical Society Short Course, January 4-5, 2011, New Orleans, Louisiana. American Mathematical Society, 2011.

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American Mathematical Society. Short Course. Evolutionary game dynamics: American Mathematical Society Short Course, January 4-5, 2011, New Orleans, Louisiana. Edited by Sigmund Karl 1945-. American Mathematical Society, 2011.

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Vega-Redondo, Fernando. Evolution, games, and economic behaviour. Oxford University Press, 1996.

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Shakya, Siddhartha. Markov Networks in Evolutionary Computation. Springer Berlin Heidelberg, 2012.

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Book chapters on the topic "Evolutionary Game Theory (EGT)"

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Zehentner, Eva. "The emergence of the English dative alternation as a response to system-wide changes." In Ditransitives in Germanic Languages. John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/sigl.7.01zeh.

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This chapter revisits the much-discussed question whether a causal relationship holds between several changes observed in the history of English; these are (a) the increasing use of prepositional patterns, (b) the loss of nominal case marking, and (c) the fixation of constituent order. Located within the same time-period, namely Middle English, there is relatively broad consensus that the processes are correlated. However, the extent and directionality of causation is highly debated. This chapter addresses this issue by taking another look at a specific case study which reflects all the changes: the history of the dative alternation. To add to results from earlier corpus-based investigations on this development, the emergence of the alternation is modelled by means of Evolutionary Game Theory (EGT). Specifically, the study tests the hypothesis that the increase of prepositional ditransitives and ultimately the dative alternation is a consequence of case marking being lost and constituent order becoming fixed, and discusses the potential benefits of taking an EGT approach to such questions.
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Pereira, Luís Moniz, Francisco C. Santos, and António Barata Lopes. "AI Modelling of Counterfactual Thinking for Judicial Reasoning and Governance of Law." In Multidisciplinary Perspectives on Artificial Intelligence and the Law. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41264-6_14.

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AbstractWhen speaking of moral judgment, we refer to a function of recognizing appropriate or condemnable actions and the possibility of choice between them by agents. Their ability to construct possible causal sequences enables them to devise alternatives in which choosing one implies setting aside others. This internal deliberation requires a cognitive ability, namely that of constructing counterfactual arguments. These serve not just to analyse possible futures, being prospective, but also to analyse past situations, by imagining the gains or losses resulting from alternatives to the actions actually carried out, given evaluative information subsequently known.Counterfactual thinking is in thus a prerequisite for AI agents concerned with Law cases, in order to pass judgement and, additionally, for evaluation of the ongoing governance of such AI agents. Moreover, given the wide cognitive empowerment of counterfactual reasoning in the human individual, namely in making judgments, the question arises of how the presence of individuals with this ability can improve cooperation and consensus in populations of otherwise self-regarding individuals.Our results, using Evolutionary Game Theory (EGT), suggest that counterfactual thinking fosters coordination in collective action problems occurring in large populations and has limited impact on cooperation dilemmas in which such coordination is not required.
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Helbing, Dirk. "Evolutionary Game Theory Game theory!evolutionary|bb." In Quantitative Sociodynamics. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11546-2_12.

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Durlauf, Steven N., and Lawrence E. Blume. "Deterministic Evolutionary Dynamics." In Game Theory. Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280847_7.

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Cressman, Ross, and Joe Apaloo. "Evolutionary Game Theory." In Handbook of Dynamic Game Theory. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-44374-4_6.

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Marsh, Tim. "Evolutionary Game Theory." In Encyclopedia of Personality and Individual Differences. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1631.

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Mailath, George J. "Evolutionary Game Theory." In The New Palgrave Dictionary of Economics and the Law. Palgrave Macmillan UK, 2002. http://dx.doi.org/10.1007/978-1-349-74173-1_141.

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Cressman, Ross, and Joe Apaloo. "Evolutionary Game Theory." In Handbook of Dynamic Game Theory. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27335-8_6-1.

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Helbing, Dirk. "Evolutionary Game Theory." In Quantitative Sociodynamics. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8516-3_11.

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Sandholm, William H. "Evolutionary Game Theory." In Encyclopedia of Complexity and Systems Science. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-642-27737-5_188-3.

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Conference papers on the topic "Evolutionary Game Theory (EGT)"

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Escobar-Cuevas, Hector, Erik Cuevas, Alberto L. Chang, et al. "Differential Evolution Search Strategy Enhancement Through Evolutionary Game Theory." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10612179.

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Jarosz, Paweł, and Adam Marszałek. "Multi-Objective Optimization in Asynchronous Environments: An Evolutionary Game Theory Approach." In 2024 32nd International Conference on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS). IEEE, 2024. https://doi.org/10.1109/mascots64422.2024.10786577.

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Wu, Hongqian, Hongzhong Deng, Jichao Li, and Hankang Luo. "Research on Task Collaboration Over Heterogeneous Networks Based on Evolutionary Game Theory." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831853.

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Chen, Tianxiang, Feng Wang, Wenjie Hou, Shaoting Tang, and Zhiming Zheng. "Dynamic Incentive Model for Federated Learning Model Trading via Evolutionary Game Theory." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888321.

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Nie, Jingyi, Bingxin Lin, and Lei Zhou. "The Effect of Structural Properties of Complex Networks on Evolutionary Game Dynamics." In 2024 4th International Conference on Control Theory and Applications (ICoCTA). IEEE, 2024. https://doi.org/10.1109/icocta64736.2024.00062.

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Brunetti, Ilaria, and Eitan Altman. "Revisiting evolutionary game theory." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760150.

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Tomassini, Marco. "Introduction to evolutionary game theory." In Proceeding of the fifteenth annual conference companion. ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2480808.

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Tomassini, Marco. "Introduction to evolutionary game theory." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2598394.2605363.

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Feng, Huibin, Shunyi Zhang, Chao Liu, Junrong Yan, and Ming Zhang. "P2P Incentive Model On Evolutionary Game Theory." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.799.

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Hall, Lawrence O., and Alireza Chakeri. "Relational data partitioning using evolutionary game theory." In 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2014. http://dx.doi.org/10.1109/cidm.2014.7008656.

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