An Approximate Nonmyopic Computation for Value of Information

TL;DR

This paper introduces an approximate nonmyopic method for computing the value of information, overcoming the limitations of traditional myopic analyses by leveraging statistical properties of large samples.

Contribution

It presents a novel nonmyopic approximation technique for value of information that improves decision-making over traditional myopic methods.

Findings

Provides a computationally feasible nonmyopic approach

Outperforms myopic analyses in complex decision scenarios

Utilizes statistical properties of large samples

Abstract

Value-of-information analyses provide a straightforward means for selecting the best next observation to make, and for determining whether it is better to gather additional information or to act immediately. Determining the next best test to perform, given a state of uncertainty about the world, requires a consideration of the value of making all possible sequences of observations. In practice, decision analysts and expert-system designers have avoided the intractability of exact computation of the value of information by relying on a myopic approximation. Myopic analyses are based on the assumption that only one additional test will be performed, even when there is an opportunity to make a large number of observations. We present a nonmyopic approximation for value of information that bypasses the traditional myopic analyses by exploiting the statistical properties of large samples.