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Evolutionary Game Theory: A General Review

Year 2024, Volume: 10 Issue: 2, 85 - 98, 30.12.2024
https://doi.org/10.51803/yssr.1525146

Abstract

This article explores the Evolutionary Game Theory (now EGT), encompassing its historical underpinnings, recent advancements, and future potential. Originating in the 1970s through the pioneering work of John Maynard Smith and George R. Price, EGT leverages game-theoretic concepts to elucidate the evolution of strategies within various populations across biological, economic, and social domains. Notably, recent progress has seen the integration of advanced large language models (LLMs) such as GPT-3.5 and GPT-4 into agent-based simulations, thereby enriching the authenticity and intricacy of strategic interactions. Additionally, the study addresses the complexities associated with modeling diverse behaviors and bridging the insights derived from LLMs to practical applications in fields like biology, healthcare, education, and social sciences. Furthermore, it underscores the significance of interdisciplinary collaboration and innovative methodologies in addressing the multifaceted challenges within EGT. Finally, the article contemplates the potential avenues for future research, emphasizing the fusion of EGT with real-world applications and the necessity for comprehensive models that encompass the complexities of evolutionary dynamics in adaptive systems.

References

  • REFERENCES
  • Alger, I., & Weibull, J.W. (2016). Evolution and Kantian morality. Games and Economic Behaviour, 98, 56–67. [CrossRef]
  • Axelrod, R., & Hamilton, W. (1981). The evolution of cooperation. Science, 211, 1390–1396. [CrossRef]
  • Bauch, T., & Bhattacharyya, S. (2012). Evolutionary game theory and social learning can determine how vaccine scare unfold. Evolutionary Game Theory and Vaccine Scares, 8(4), Article e1002452. [CrossRef]
  • Bayer, P., Brown, J.S., Dubbeldam, J., & Broom, M. (2021) A markov chain model of cancer treatment. bioRxiv doi: 10.1101/2021.06.16.448669 [CrossRef]
  • Bilancini, E., Boncinelli, L., & Wu, J. (2018). The interplay of cultural intolerance and action-assortativity for the emergence of cooperation and homophily. European Economic Review, 102, 1–18. [CrossRef]
  • Bin, W., Zhou, D., Fu, F., Luo, Q., Wang, Q., & Traulsen, A. (2010). Evolution of Cooperation on Stochastic Dynamical Networks. PLoS One, 5(6), Article e11187. [CrossRef]
  • Blume, L. and Easley, D. (2006). If you're so smart, why aren't you rich? belief selection in complete and ıncomplete markets. Econometrica, 74, 929–966. [CrossRef]
  • Brock, W.A., Hommes, C. H. and Wagener, F. O. O. (2005). Evolutionary Dynamics in markets with many trader types. Journal of Mathematical Economics, 41(1–2), 7–42. [CrossRef]
  • Bukkuri, A. (2021). Cancers are in an evolutionary battle with treatments – evolutionary game theory could tip the advantage to medicine. The Conversation https://theconversation.com/cancers-are-in-an-evolutionary-battle-with-treatments-evolutionary-game-theory-could-tip-the-advantage-to-medicine-170175 Accessed on Nov 16, 2021.
  • Bukkuri A., & Brown, J. S. (2021). Evolutionary game theory: Darwinian dynamics and the G function approach. Games, 12(4), Article 72. [CrossRef]
  • Camerer, C. F., & and Fehr, E. (2002). Measuring social norms and preferences using experimental games: A guide for social scientists. IEER Working Paper No. 97. [CrossRef]
  • Canbolat-Özkan, E., Beraha, A., & Baş, A. (2016). Application of evolutionary game theory to strategic innovation. Procedia-Social and Behavioral Sciences, 235(16), 685–693. [CrossRef]
  • Czako, B., Sápi, J., & Kovács, L. (2017). Model-based optimal control method for cancer treatment using model predictive control and robust fixed point method. 2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES). [CrossRef]
  • Dong, Y., Zhang Y., Pan, J., & Chen T. (2020). Evolutionary game model of stock price synchronicity from investor behavior. Discrete Dynamics in Nature and Society, Suppl 4, 1–9. [CrossRef]
  • Evstigneev, I. V., Hens, T., & Schenk-Hoppe, K. R. (2006). Evolutionary stable stock markets. Economic Theory, 27, 449–468. [CrossRef]
  • Fan, K., & Hui, E.C. (2020). Evolutionary game theory analysis is used to understand the decision-making mechanism of governments and developers regarding green building incentives. Building and Environment, 179, Article 106972. [CrossRef]
  • Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., Xu, F., & Li, Y. (2023). Large language models empowered agent-based modeling and simulation: A survey and perspectives. arXiv:2312.11970. doi: /10.48550/arXiv.2312.11970 [CrossRef]
  • Gou, Z., & Deng, Y. (2021). Dynamic model of collaboration in multi-agent system based on evolutionary game theory. Games, 12(4), Article 75. [CrossRef]
  • Han, A., Pereira, L., Santos, F., & Lenaerts, T. (2020). To regulate or not: A social dynamics analysis of an idealised AI race. Journal of Artificial Intelligence Research, 69, 881–921. [CrossRef]
  • Heikkinen, D. (2023). Changes in learning behaviors post pandemic: A new era in learning economy. https://mpra.ub.uni-muenchen.de/116921/8/MPRA_paper_116921.pdf Accessed on Dec 23, 2024. Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., … & Tracer, D. (2005). “Economic man” in cross-cultural perspective: Behavioral experiments in 15 small-scale societies. Behavioral and Brain Sciences, 28(6), 795–815. [CrossRef]
  • Hens, T., & Schenk-Hoppé, K. R. (2005). Evolutionary finance: Introduction to the special issue. Journal of Mathematical Economics, 41(1), 1–5. [CrossRef]
  • Kalai, E., & Smorodinsky, M. (1975). Other solutions to Nash's bargaining problem. Econometrica, 43(3), 513–518. [CrossRef]
  • Kaznatcheev, A., Vander Velde, R., Scott, J. G., & Basanta, D. (2017). Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature. British Journal of Cancer, 116, 785–792. [CrossRef]
  • LaCasse, C., & Ross, D. (1994). The microeconomic interpretation of games. PSA : Proceedings of the Biennal Meetings of the Philosophy of Science Assocation, 379–387. [CrossRef]
  • Leach, M., MacGregor, H., Scoones, I., & Wilkinson, A. (2021). Post-pandemic transformations: How and why COVID-19 requires us to rethink development. World Development, 138, Article 105233. [CrossRef]
  • Li, D., & Wang Y. (2022). Online learning management for primary and secondary students during the COVID-19 epidemic: An evolutionary game theory approach. Sustainability, 14(19), Article 12416. [CrossRef]
  • Mielke, J., & Steudle, A. (2018). Green investment and coordination failure: An ınvestors’ perspective. Ecological Economics, 150, 88–95. [CrossRef]
  • Muñiz, H., Accinelli, E., & Hernández, E. (2023) An evolutionary game theoretical approach to the teaching-learning techniques in the post-pandemic era. Open Access Library Journal, 10, 1–22. [CrossRef]
  • Naidu, S., & Hwang, S., & Bowles, S. (2010). Evolutionary bargaining with intentional idiosyncratic play. Economics Letters, 109, 31–33. [CrossRef]
  • Newton, J. (2012). Coalitional stochastic stability. Games and Economic Behavior, 75, 842–854. [CrossRef]
  • Newton, J. (2018). Evolutionary game theory: A renaissance. Games, 9(2), Article 31.
  • Orlando, P. A., Gatenby, R. A., & Brown, J. S. (2012). Cancer treatment as a game: integrating evolutionary game theory into the optimal control of chemotherapy. Physical Biology, 9(6), Article 065007. [CrossRef]
  • Ostrom, E. (1990). Governing the commons: The evolution of institutions for collective action. Cambridge University Press. [CrossRef]
  • Roca, C., Sergi, L., Alex, A., &, Angel, S. (2010). Topological Traps Control Flow on Real Networks: The Case of Coordination Failures. PLoS One, 5(12), 1–9. [CrossRef]
  • Ross, D., & LaCasse, C. (1995). Towards a new philosophy of positive economics. Dialogue, 34(3), 467–494. [CrossRef]
  • Shleifer, A. (2000). Inefficient markets: an introduction to behavioral finance. Oxford University Press. [CrossRef]
  • Smith, J.M., & Price, G. (1973). The logic of animal conflict. Nature, 246, 15–18. [CrossRef]
  • Smith, J. (1979). Hypercycles and the origin of life. Nature, 280, 445–446. [CrossRef]
  • Stanková, K., Brown, J. S., Dalton, W. S., & Gatenby, R. A. (2019). Optimizing cancer treatment using game theory: A review. JAMA Oncology, 5(1), 96–103. [CrossRef]
  • Staudigl, M. (2012). Stochastic stability in asymmetric binary choice coordination games. Games and Economic Behaviour, 75, 372–401. [CrossRef]
  • Straub, P. (1995). Risk dominance and coordination failures in static games. The Quarterly Review of Economics and Finance, 35(4), 339–363. [CrossRef]
  • Suzuki, R., & Arita, T. (2024). An evolutionary model of personality traits related to cooperative behavior using a large language model. Scientific Reports, 14, Article 5989. [CrossRef]
  • Vega-Redondo, F. (2007). Complex social networks. Cambridge University Press. [CrossRef]
  • Wölfl, B., Rietmole, H., Salvioli, M., Kaznatcheev, A., Thuijsman, F., & Brown, J. S. (2022). The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer. Dynamic Games and Application, 12, 313–342. [CrossRef]
  • West, J., Ma, Y., & Newton, P. K. (2018). Capitalizing on competition: An evolutionary model of competitive release in metastatic castration resistant prostate cancer treatment. Journal of Theoretical Biology, 455, 249–260. [CrossRef]
  • You, Y., Chen, Y., You, Y., Zhang, Q., & Cao Q. (2023). Evolutionary game analysis of artificial intelligence such as the generative pre-trained transformer in future education. Sustainability, 15(12), Article 9355. [CrossRef]
  • Young, H. P. (1998). Individual strategy and social structure: An evolutionary theory of institutions. Princeton University Press. [CrossRef]
  • Zarzà, I., Curtò, J., Roig, G., Manzoni, P., & Calafate, C. T. (2023). Emergent cooperation and strategy adaptation in multi-agent systems: An extended coevolutionary theory with LLMs. Electronics, 12(12), Article 2722. [CrossRef]

Evrimsel Oyun Teorisi: Genel Bir İnceleme

Year 2024, Volume: 10 Issue: 2, 85 - 98, 30.12.2024
https://doi.org/10.51803/yssr.1525146

Abstract

Bu makale, Evrimsel Oyun Teorisinin (EOT) tarihsel temellerini, teoride yaşanan son gelişmeleri ve evrimsel oyun teorisinin gelecekteki potansiyel evrimini incelemektedir. 1970'lerde John Maynard Smith ve George R. Price'ın öncü çalışmalarıyla ortaya çıkan EOT, biyolojik, ekonomik ve sosyal alanlardaki çeşitli stratejilerin evrimini araştırmak için oyun teorisi kavramlarından yararlanmakta ve dönemler arası etkileşimi sağlamaktadır. EOT özellikle son zamanlarda GPT-3.5 ve GPT-4 gibi geniş dil modellerinin ajan bazlı simülasyonlara entegre edilmesiyle daha da zengin bir literatüre kavuşmuştur. Bu çalışma ayrıca, farklı davranışların modellenmesi ve geniş dil modellerinden elde edilen içgörülerin biyoloji, sağlık, eğitim ve sosyal bilimler gibi alanlardaki pratik uygulamalarla birleştirilmesiyle ilgili karmaşıklıkları ele almaktadır. Ayrıca, evrimsel oyun teorisindeki çok yönlü zorlukların ele alınmasında disiplinler arası işbirliğinin ve yenilikçi metodolojilerin öneminin altını çizmektedir. Son olarak çalışmamız, EOT'nin gelecekteki potansiyel evrimini inceleyerek, EOT'nin adaptif sistemlerdeki evrimsel dinamiklerin karmaşıklığını kapsayan kapsamlı modeller ile zenginleştirilmesi gerektiğini vurgulamaktadır.

References

  • REFERENCES
  • Alger, I., & Weibull, J.W. (2016). Evolution and Kantian morality. Games and Economic Behaviour, 98, 56–67. [CrossRef]
  • Axelrod, R., & Hamilton, W. (1981). The evolution of cooperation. Science, 211, 1390–1396. [CrossRef]
  • Bauch, T., & Bhattacharyya, S. (2012). Evolutionary game theory and social learning can determine how vaccine scare unfold. Evolutionary Game Theory and Vaccine Scares, 8(4), Article e1002452. [CrossRef]
  • Bayer, P., Brown, J.S., Dubbeldam, J., & Broom, M. (2021) A markov chain model of cancer treatment. bioRxiv doi: 10.1101/2021.06.16.448669 [CrossRef]
  • Bilancini, E., Boncinelli, L., & Wu, J. (2018). The interplay of cultural intolerance and action-assortativity for the emergence of cooperation and homophily. European Economic Review, 102, 1–18. [CrossRef]
  • Bin, W., Zhou, D., Fu, F., Luo, Q., Wang, Q., & Traulsen, A. (2010). Evolution of Cooperation on Stochastic Dynamical Networks. PLoS One, 5(6), Article e11187. [CrossRef]
  • Blume, L. and Easley, D. (2006). If you're so smart, why aren't you rich? belief selection in complete and ıncomplete markets. Econometrica, 74, 929–966. [CrossRef]
  • Brock, W.A., Hommes, C. H. and Wagener, F. O. O. (2005). Evolutionary Dynamics in markets with many trader types. Journal of Mathematical Economics, 41(1–2), 7–42. [CrossRef]
  • Bukkuri, A. (2021). Cancers are in an evolutionary battle with treatments – evolutionary game theory could tip the advantage to medicine. The Conversation https://theconversation.com/cancers-are-in-an-evolutionary-battle-with-treatments-evolutionary-game-theory-could-tip-the-advantage-to-medicine-170175 Accessed on Nov 16, 2021.
  • Bukkuri A., & Brown, J. S. (2021). Evolutionary game theory: Darwinian dynamics and the G function approach. Games, 12(4), Article 72. [CrossRef]
  • Camerer, C. F., & and Fehr, E. (2002). Measuring social norms and preferences using experimental games: A guide for social scientists. IEER Working Paper No. 97. [CrossRef]
  • Canbolat-Özkan, E., Beraha, A., & Baş, A. (2016). Application of evolutionary game theory to strategic innovation. Procedia-Social and Behavioral Sciences, 235(16), 685–693. [CrossRef]
  • Czako, B., Sápi, J., & Kovács, L. (2017). Model-based optimal control method for cancer treatment using model predictive control and robust fixed point method. 2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES). [CrossRef]
  • Dong, Y., Zhang Y., Pan, J., & Chen T. (2020). Evolutionary game model of stock price synchronicity from investor behavior. Discrete Dynamics in Nature and Society, Suppl 4, 1–9. [CrossRef]
  • Evstigneev, I. V., Hens, T., & Schenk-Hoppe, K. R. (2006). Evolutionary stable stock markets. Economic Theory, 27, 449–468. [CrossRef]
  • Fan, K., & Hui, E.C. (2020). Evolutionary game theory analysis is used to understand the decision-making mechanism of governments and developers regarding green building incentives. Building and Environment, 179, Article 106972. [CrossRef]
  • Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., Xu, F., & Li, Y. (2023). Large language models empowered agent-based modeling and simulation: A survey and perspectives. arXiv:2312.11970. doi: /10.48550/arXiv.2312.11970 [CrossRef]
  • Gou, Z., & Deng, Y. (2021). Dynamic model of collaboration in multi-agent system based on evolutionary game theory. Games, 12(4), Article 75. [CrossRef]
  • Han, A., Pereira, L., Santos, F., & Lenaerts, T. (2020). To regulate or not: A social dynamics analysis of an idealised AI race. Journal of Artificial Intelligence Research, 69, 881–921. [CrossRef]
  • Heikkinen, D. (2023). Changes in learning behaviors post pandemic: A new era in learning economy. https://mpra.ub.uni-muenchen.de/116921/8/MPRA_paper_116921.pdf Accessed on Dec 23, 2024. Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., … & Tracer, D. (2005). “Economic man” in cross-cultural perspective: Behavioral experiments in 15 small-scale societies. Behavioral and Brain Sciences, 28(6), 795–815. [CrossRef]
  • Hens, T., & Schenk-Hoppé, K. R. (2005). Evolutionary finance: Introduction to the special issue. Journal of Mathematical Economics, 41(1), 1–5. [CrossRef]
  • Kalai, E., & Smorodinsky, M. (1975). Other solutions to Nash's bargaining problem. Econometrica, 43(3), 513–518. [CrossRef]
  • Kaznatcheev, A., Vander Velde, R., Scott, J. G., & Basanta, D. (2017). Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature. British Journal of Cancer, 116, 785–792. [CrossRef]
  • LaCasse, C., & Ross, D. (1994). The microeconomic interpretation of games. PSA : Proceedings of the Biennal Meetings of the Philosophy of Science Assocation, 379–387. [CrossRef]
  • Leach, M., MacGregor, H., Scoones, I., & Wilkinson, A. (2021). Post-pandemic transformations: How and why COVID-19 requires us to rethink development. World Development, 138, Article 105233. [CrossRef]
  • Li, D., & Wang Y. (2022). Online learning management for primary and secondary students during the COVID-19 epidemic: An evolutionary game theory approach. Sustainability, 14(19), Article 12416. [CrossRef]
  • Mielke, J., & Steudle, A. (2018). Green investment and coordination failure: An ınvestors’ perspective. Ecological Economics, 150, 88–95. [CrossRef]
  • Muñiz, H., Accinelli, E., & Hernández, E. (2023) An evolutionary game theoretical approach to the teaching-learning techniques in the post-pandemic era. Open Access Library Journal, 10, 1–22. [CrossRef]
  • Naidu, S., & Hwang, S., & Bowles, S. (2010). Evolutionary bargaining with intentional idiosyncratic play. Economics Letters, 109, 31–33. [CrossRef]
  • Newton, J. (2012). Coalitional stochastic stability. Games and Economic Behavior, 75, 842–854. [CrossRef]
  • Newton, J. (2018). Evolutionary game theory: A renaissance. Games, 9(2), Article 31.
  • Orlando, P. A., Gatenby, R. A., & Brown, J. S. (2012). Cancer treatment as a game: integrating evolutionary game theory into the optimal control of chemotherapy. Physical Biology, 9(6), Article 065007. [CrossRef]
  • Ostrom, E. (1990). Governing the commons: The evolution of institutions for collective action. Cambridge University Press. [CrossRef]
  • Roca, C., Sergi, L., Alex, A., &, Angel, S. (2010). Topological Traps Control Flow on Real Networks: The Case of Coordination Failures. PLoS One, 5(12), 1–9. [CrossRef]
  • Ross, D., & LaCasse, C. (1995). Towards a new philosophy of positive economics. Dialogue, 34(3), 467–494. [CrossRef]
  • Shleifer, A. (2000). Inefficient markets: an introduction to behavioral finance. Oxford University Press. [CrossRef]
  • Smith, J.M., & Price, G. (1973). The logic of animal conflict. Nature, 246, 15–18. [CrossRef]
  • Smith, J. (1979). Hypercycles and the origin of life. Nature, 280, 445–446. [CrossRef]
  • Stanková, K., Brown, J. S., Dalton, W. S., & Gatenby, R. A. (2019). Optimizing cancer treatment using game theory: A review. JAMA Oncology, 5(1), 96–103. [CrossRef]
  • Staudigl, M. (2012). Stochastic stability in asymmetric binary choice coordination games. Games and Economic Behaviour, 75, 372–401. [CrossRef]
  • Straub, P. (1995). Risk dominance and coordination failures in static games. The Quarterly Review of Economics and Finance, 35(4), 339–363. [CrossRef]
  • Suzuki, R., & Arita, T. (2024). An evolutionary model of personality traits related to cooperative behavior using a large language model. Scientific Reports, 14, Article 5989. [CrossRef]
  • Vega-Redondo, F. (2007). Complex social networks. Cambridge University Press. [CrossRef]
  • Wölfl, B., Rietmole, H., Salvioli, M., Kaznatcheev, A., Thuijsman, F., & Brown, J. S. (2022). The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer. Dynamic Games and Application, 12, 313–342. [CrossRef]
  • West, J., Ma, Y., & Newton, P. K. (2018). Capitalizing on competition: An evolutionary model of competitive release in metastatic castration resistant prostate cancer treatment. Journal of Theoretical Biology, 455, 249–260. [CrossRef]
  • You, Y., Chen, Y., You, Y., Zhang, Q., & Cao Q. (2023). Evolutionary game analysis of artificial intelligence such as the generative pre-trained transformer in future education. Sustainability, 15(12), Article 9355. [CrossRef]
  • Young, H. P. (1998). Individual strategy and social structure: An evolutionary theory of institutions. Princeton University Press. [CrossRef]
  • Zarzà, I., Curtò, J., Roig, G., Manzoni, P., & Calafate, C. T. (2023). Emergent cooperation and strategy adaptation in multi-agent systems: An extended coevolutionary theory with LLMs. Electronics, 12(12), Article 2722. [CrossRef]
There are 49 citations in total.

Details

Primary Language English
Subjects Econometrics (Other)
Journal Section Makaleler
Authors

Aras Yolusever 0000-0001-9810-2571

Publication Date December 30, 2024
Submission Date July 30, 2024
Acceptance Date November 18, 2024
Published in Issue Year 2024 Volume: 10 Issue: 2

Cite

APA Yolusever, A. (2024). Evolutionary Game Theory: A General Review. Yildiz Social Science Review, 10(2), 85-98. https://doi.org/10.51803/yssr.1525146