# Acquisition of Project-Specific Assets with Bayesian Updating

## TL;DR

This paper analyzes how learning through Bayesian updating influences optimal decision policies and timing in a sequential decision model with irreversible choices, using a firm’s project evaluation scenario.

## Contribution

It introduces a Bayesian sequential decision model with two irreversible options and characterizes the optimal policy via posterior probability thresholds.

## Key findings

- Time-to-decision is not always monotonic with information arrival rate.
- Optimal policy depends on posterior probability thresholds.
- Bayesian updating impacts the timing and nature of decisions.

## 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 so as to learn, via Bayesian updating, about the project\textquoteright s profitability, which is known to be in one of two possible states. The firm continuously observes the project\textquoteright 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.