Sensitivity Analysis for Threshold Decision Making with Dynamic Networks
This work addresses parameter sensitivity for decision-making in dynamic networks, specifically in infectious disease modeling, but is incremental as it builds on prior sensitivity analysis methods.
The study investigates how inaccuracies in dynamic Bayesian network parameters affect threshold-based decision recommendations, presenting a computational procedure to determine parameter bounds that maintain decision stability, illustrated with an infectious disease network.
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.