Self-Prospecting: Optimal Experimentation Under Present-Bias

Abstract

We study dynamic information provision to a present-biased decision maker (DM) who faces an experimentation problem. In our model, rewards arrive independently of the hidden state, so that under full information, beliefs remain constant prior to conclusive news arrival. Our main finding is that coarsening the DM's information to induce a continuously increasing path of beliefs during active experimentation is optimal. Doing so links current and future experimentation choices in a complementary fashion, boosting incentives. This ``encouragement'' effect is new to games of strategic experimentation. We apply our findings to various settings, including motivational design for talent discovery, issue-selling in organizations, short-termism in policy-making, and optimal self-delusion. (Joint work with Polina Borisova (PSE, Paris 1))