Rate My Professor Garcia Gallego

Garcia Gallego's research investigates how self-interested agents, such as machines and humans, learn to cooperate and coordinate in natural and artificial settings without central authority, utilizing game theory, evolutionary dynamics, agent-based models, and simulations. His work applies to multi-agent systems, AI, dynamics of social behavior, computational ecology, social dilemmas, and mechanism design. He has received grants including ARC Discovery Project "Governing the knowledge commons" (2019–2025) with Toby Handfield and Neil Levy, "Modelling collective behaviour to protect social insect ecosystem services" (2018–2022), and others from Facebook and Group of Eight. Key publications encompass "Picking strategies in games of cooperation" (Proceedings of the National Academy of Sciences, 2025, with Arne Traulsen), "Age polyethism can emerge from social learning: A game-theoretic investigation" (PLoS Computational Biology, 2025, with Maryam Khajehnejad and Bertrand Meyer), "Learning to cooperate against ensembles of diverse opponents" (Neural Computing and Applications, 2025, with Ishan Perera and Fien de Nijs), "Repeated games with partner choice" (PLoS Computational Biology, 2025), and "Academic Journals, Incentives, and the Quality of Peer Review: A Model" (Philosophy of Science, 2024, with Kevin J.S. Zollman and Toby Handfield). With over 1,700 citations documented on Google Scholar, his scholarship impacts evolutionary game theory and multi-agent reinforcement learning. He lectures and serves as chief examiner for units including Introduction to Computer Science (FIT1008), Algorithmic Problem Solving (FIT1029), and Advanced Topics in Computational Science (FIT4012).