Sensitivity Analysis on Interaction Effects of Policy-Augmented Bayesian Networks

TL;DR

This paper introduces a sampling-based framework for efficiently quantifying interaction effects in policy-augmented Bayesian networks used in biomanufacturing, enhancing understanding of complex process mechanisms.

Contribution

It proposes a novel simulation estimation framework and a non-nested sequential sampling algorithm to improve computational efficiency in interaction effect analysis.

Findings

Effective interaction effect quantification using ShapleyOwen indices.

Enhanced computational efficiency with the proposed sequential sampling algorithm.

Accurate estimation of interaction effects under fixed simulation budgets.

Abstract

Biomanufacturing plays an important role in supporting public health and the growth of the bioeconomy. Modeling and studying the interaction effects among various input variables is very critical for obtaining a scientific understanding and process specification in biomanufacturing. In this paper, we use the ShapleyOwen indices to measure the interaction effects for the policy-augmented Bayesian network (PABN) model, which characterizes the risk- and science-based understanding of production bioprocess mechanisms. In order to facilitate efficient interaction effect quantification, we propose a sampling-based simulation estimation framework. In addition, to further improve the computational efficiency, we develop a non-nested simulation algorithm with sequential sampling, which can dynamically allocate the simulation budget to the interactions with high uncertainty and therefore estimate the interaction effects more accurately under a total fixed budget setting.