A No-Regret Framework for Adaptive Incentive Design
How to nudge strategic people toward collective good while learning their preferences
A new method lets a central authority (like a regulator or planner) design taxes, subsidies, or payments that steer self-interested agents toward socially beneficial choices—while simultaneously figuring out what those agents actually want through their repeated responses. The framework guarantees that estimation errors shrink predictably and the total social cost loss stays close to optimal, even when agents' preferences start out completely unknown.
Policy makers constantly struggle to design incentives—carbon taxes, congestion pricing, welfare programs—without knowing exactly how people will respond or what constraints they face. This framework provides a principled way to adjust incentives over time as you learn, ensuring you don't waste resources on poorly-tuned policies while fumbling in the dark. It trades short-term exploration (slightly suboptimal incentives that reveal preferences) for long-term efficiency, with proven mathematical guarantees on how much welfare you'll recover.