Myopic Value of Information in Influence Diagrams
This work addresses a technical challenge in decision analysis for researchers and practitioners, but it appears incremental as it builds on existing frameworks without broad new applications.
The paper tackles the problem of calculating myopic value of information in influence diagrams by proposing a method based on the strong junction tree framework, which allows using the same junction tree with expanded tables for different instantiation orders.
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.