We investigate the role of performance feedback, in the form of a public leaderboard, in a sequential-sampling contest with costly observations. We show theoretically that for contests with a fixed ending date (i.e., finite horizon), providing public performance feedback may result in fewer expected observations and a lower expected value of the winning observation. We conduct a controlled laboratory experiment to test the theoretical predictions, and find that the experimental results largely support the theory. In addition, we investigate how individual characteristics affect competitive sequential-sampling activity.

We investigate the willingness of individuals to persist at exploration when confronted by prolonged periods of negative feedback. We design a two-dimensional maze game and run a series of randomized experiments with human subjects in the game. Our results suggest individuals explore more when they are reminded of the incremental cost of their actions, a result that extends prior research on loss aversion and prospect theory to environments characterized by model uncertainty. In addition, we run simulations based on a model of reinforcement learning that extend beyond two-period models of decision making to account for repeated behavior in longer-running, dynamic contexts.