Evrimsel Oyun Teorisi: Genel Bir İnceleme

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.

Keywords

• Evrimsel Oyun Teorisi
• Ajan Bazlı Kompütasyonel İktisat
• Geniş Dil Modelleri
• Replikatör Dinamikleri

Evolutionary Game Theory: A General Review

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.

Keywords

• Evolutionary Game Theory
• Agent-Based Models
• Large Language Models
• ; Replicator Dynamics

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