Advanced Evolutionary Game Theory

Dear Colleagues,

Cooperation is essential for achieving collective well-being and efficiency in natural and human societies. Building on individual selfishness, evolutionary game theory has made significant progress over the past decades, yielding profound insights into the ways in which we can overcome social dilemmas, particularly in spatially structured populations. In recent years, a variety of new tools have revitalized the field of evolutionary game theory, driven by advancements in computer power and breakthroughs in mathematical theories. Based on intensive computational resources, Monte Carlo simulations have unveiled numerous mechanisms that promote cooperation in spatial evolutionary game models. Utilizing the latest mathematical frameworks, evolutionary graph theory has enabled precise calculations of cooperation conditions on arbitrary real-world network structures. The development of artificial intelligence has inspired novel approaches to evolutionary game theory, such as Q-learning and online experiments using large language models. These developments mark the beginning of a new chapter for advanced evolutionary game theory.

This Special Issue aims to promote these new tools and perspectives, facilitating their communications and integration in evolutionary game dynamics. We encourage contributions from a range of fields, such as physics, mathematics, biology, artificial intelligence, computer science, engineering, economics, and management science.

Dr. Chaoqian Wang
Dr. Qi Su
Guest Editors

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