Myopic Value of Information in Influence Diagrams

TL;DR

This paper introduces a new method for calculating the myopic value of information in influence diagrams using the strong junction tree framework, allowing flexible instantiation order analysis and comparison with traditional approaches.

Contribution

It presents a novel approach leveraging the strong junction tree framework to efficiently compute the value of information in influence diagrams with varying instantiation orders.

Findings

The method effectively computes the myopic value of information.

It allows changing instantiation order via table expansion.

Comparison shows advantages over classic modeling methods.

Abstract

We present a method for calculation of myopic value of information in influence diagrams (Howard & Matheson, 1981) based on the strong junction tree framework (Jensen, Jensen & Dittmer, 1994). The difference in instantiation order in the influence diagrams is reflected in the corresponding junction trees by the order in which the chance nodes are marginalized. This order of marginalization can be changed by table expansion and in effect the same junction tree with expanded tables may be used for calculating the expected utility for scenarios with different instantiation order. We also compare our method to the classic method of modeling different instantiation orders in the same influence diagram.