Constructing Belief: Using Bayesian Belief Networks to Measure and Manage Uncertainty in Digital Design

Abstract

A Bayesian Belief Network is a diagrammatic way to reason probabilistically and understand causal inference in complex systems. We propose using Bayesian Belief Networks (BBN) in the early stages of design projects to highlight components with high risk of failure. Identifying these components of high risk can inform how resources should be best used on costly modelling tasks. In addition, high risk components may impose functional modelling requirements, which in turn will inform the design of flexible systems for critical areas. This approach has the potential to significantly reduce risk by focusing and informing modelling efforts, which in turn increases the chance of success of the project and lowers costs for all stakeholders involved.

Using a prototype software application developed to quickly create BBNs and calculate a final probability value of a specific outcome (the “work product”), we test different project scenarios collected through three interviews with industry professionals. In each case, we identify an aspect of the project that changed during the course of the project with far reaching implications. By adjusting the values and structure of these networks we formulate specific functional requirements for digital models and in some cases, the associated construction systems. We find that these requirements would have increased the overall value of their respective projects by directly addressing the areas of strong influence and uncertainty identified in the BBN.