Policy Learning with Competing Agents

arXiv:2204.01884v4 Announce Type: replace
Abstract: 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 …

abstract agents aim arxiv capacity competition cs.lg decision econ.em learn makers paper policies policy stat.ml study treatment type