Invited talk: Learning and Steering Strategic Agents at Scale -- Niao He

AI is moving from isolated decision-makers toward large populations of adaptive agents interacting in markets, platforms, networks, and emerging multi-agent ecosystems. At this scale, classical equilibrium computation can become computationally prohibitive, due to curses of multi-agency and independent learning. This talk develops a mean-field perspective to learning and steering in large-scale multi-agent systems, addressing two central questions: How can strategic agents and population learn at scale, and how can their collective behavior be steered toward desirable outcomes? We discuss when finite, heterogeneous multi-agent systems can be faithfully approximated by population-level models; when equilibrium learning is computationally and statistically tractable; and how decentralized agents can learn from local feedback. We then turn from learning to steering, examining how incentives can shape equilibrium outcomes or directly steer adaptive learning dynamics.