Evolutionary Game Theory: A General Review

This article surveys 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-the-oretic 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. Moreover, it highlights the importance of interdisciplinary collaboration and innovative methodologies in tackling the complex challenges associated with EGT. The article also reflects on potential directions for future research, stressing the integration of EGT with practical applications and the need for comprehensive models that capture the intricacies of evolutionary dynamics in adaptive systems.