Sensitivity Analysis for Threshold Decision Making with Dynamic Networks

TL;DR

This paper investigates how inaccuracies in parameters of dynamic Bayesian networks affect decision-making, providing methods to determine bounds within which decisions remain stable, demonstrated through a real-world infectious disease example.

Contribution

It introduces a computational procedure to establish parameter bounds that do not alter recommended decisions in threshold-based dynamic Bayesian networks.

Findings

Parameter variations can be bounded to maintain decision stability.

A practical method for sensitivity analysis in dynamic networks is proposed.

Application demonstrated on a real infectious disease network.

Abstract

The effect of inaccuracies in the parameters of a dynamic Bayesian network can be investigated by subjecting the network to a sensitivity analysis. Having detailed the resulting sensitivity functions in our previous work, we now study the effect of parameter inaccuracies on a recommended decision in view of a threshold decision-making model. We detail the effect of varying a single and multiple parameters from a conditional probability table and present a computational procedure for establishing bounds between which assessments for these parameters can be varied without inducing a change in the recommended decision. We illustrate the various concepts involved by means of a real-life dynamic network in the field of infectious disease.