Challenges to Bayesian decision support using morphological matrices for design: empirical evidence

Abstract

A novel Bayesian design support tool is empirically investigated for its potential to support the early design stages. The design support tool provides dynamic guidance with the use of morphological design matrices during the conceptual or preliminary design stages. This paper tests the appropriateness of adopting a stochastic approach for supporting the early design phase. The rationale for the stochastic approach is based on the uncertain nature of the design during this part of the design process. The support tool is based on Bayesian belief networks (BBNs) and uses a simple but effective information content–based metric to learn or induce the model structure. The dynamically interactive tool is assessed with two empirical trials. First, the laboratory-based trial with novice designers illustrates a novel emergent design search methodology. Second, the industrial-based trial with expert designers illustrates the hurdles that are faced when deploying a design support tool in a highly pressurised industrial environment. The conclusion from these trials is that there is a need for designers to better understand the stochastic methodology for them to both be able to interpret and trust the BBN model of the design domain. Further, there is a need for a lightweight domain-specific front end interface is needed to enable a better fit between the generic support tool and the domain-specific design process and associated tools.