Self-Prospecting: Optimal Experimentation Under Present-Bias

• Department of Economics

This event features a discussion by Nikhil Vellodi (Paris School of Economics).

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 and ameliorating the DM's lack of self-control. We apply our findings to various settings, including motivational design for talent discovery, short-termism in organizations and policy-making, and optimal self-delusion. 

Co-author: P. Borisova (PSE).