Evolutionary game theory (EGT) offers a powerful modeling framework for analyzing frequency-dependent interactions in biological, social, and artificial systems. Originally adapted from economic models and applied to animal behavior, EGT has become a foundational tool for studying phenomena ranging from microbial competition and tumor progression to cultural dynamics and cooperative AI. The field's mathematical foundations have expanded over the years, incorporating tools from dynamical systems, probability theory, partial differential equations, and network science. This diverse modeling toolkit has enabled researchers to examine how social behaviors evolve in structured populations, how empirical data can inform game parameters, and how environmental feedbacks shape evolutionary dynamics.

This workshop will bring together an interdisciplinary community of EGT researchers to chart new directions for theory and applications. Through presentations and discussions, participants will identify mismatches between current models and observed systems, explore strategies for integrating empirical data into theoretical work, and formulate open problems that resonate across applied mathematics, biology, physics, and computer science. A primary aim of the workshop is to stimulate interdisciplinary collaboration and expand the methodological scope of EGT to frequency-dependent problems across scales.