As machine learning becomes increasingly central in society, algorithms gain the ability to shape the outcomes they predict. The statistical limitations of prediction models, together with rising costs of training and inference, create scarcity and information-asymmetry conditions that fuel strategic and competitive behaviors.
These behaviors violate the assumptions of traditional learning methods, and may produce unintended and possibly harmful outcomes. We study learning algorithms as components embedded in social and economic systems, and use tools from dynamical systems, contract design, and evolutionary game theory to analyze the failure modes that may emerge around them.
Building on this analysis, we develop algorithmic and mechanism-design approaches that reduce exploitation, improve economic efficiency, and steer systems toward socially favorable outcomes. Together, our results demonstrate how an integrative, behavior-aware approach can inform both theory and practice.
