About
We investigate various aspects of societal-scale systems, drawing on concepts from learning theory, dynamical systems, and economics. On the one hand, the involvement of human participants calls for complementing traditional optimization-based approaches with game-theoretic analysis. On the other hand, while classical economic theory models decision-makers as humans, increasingly, machine learning–based algorithms are being deployed in practice. Moreover, as both humans and algorithms adapt over time, a dynamical systems perspective becomes essential. These developments call for a rapprochement among traditionally separate approaches. Our goal is to analyze and design systems through these three lenses. Some current and past projects include: