Abstract
Most real-life decisions are made with less than perfect information and there is often some opportunity to acquire additional information to increase the quality of the decision. In this article, we define and study the sequential information acquisition process of a rational decision maker (DM) when allowed to acquire any finite amount of information from a set of products defined by vectors of characteristics. The information acquisition process of the DM depends both on the values of the characteristics observed previously and the number and potential realizations of the remaining characteristics. Each time an observation is acquired, the DM modifies the probability of improving upon the products already observed with the number of observations available. We construct two real-valued functions whose crossing points determine the decision of how to allocate each available piece of information. We provide several numerical simulations to illustrate the information acquisition incentives defining the behavior of the DM. Applications to knowledge management and decision support systems follow immediately from our results, particularly when considering the introduction and acceptance of new technological products and when formalizing online search environments.