Acquisition of Project-Specific Assets with Bayesian Updating

Abstract

We study the impact of learning on the optimal policy and the time-to-decision in an infinite-horizon Bayesian sequential decision model with two irreversible alternatives: exit and expansion. In our model, a firm undertakes a small-scale pilot project to learn, via Bayesian updating, about the project's profitability, which is known to be in one of two possible states. The firm continuously observes the project's cumulative profit, but the true state of the profitability is not immediately revealed because of the inherent noise in the profit stream. The firm bases its exit or expansion decision on the posterior probability distribution of the profitability. The optimal policy is characterized by a pair of thresholds for the posterior probability. We find that the time-to-decision does not necessarily have a monotonic relation with the arrival rate of new information.