Policy Learning with Competing Agents

Understand

Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat.

• When agents can respond strategically to such policies, competition arises, complicating estimation of the optimal policy.
• In this paper, we study capacity-constrained treatment assignment in the presence of such interference.
• We consider a dynamic model where the decision maker allocates treatments at each time step and heterogeneous agents myopically best respond to the previous treatment assignment policy.