Multi-attribute decision-making and data visualization for multi-disciplinary group building project decisions

摘要: With improvements in computer modeling that allow AEC professionals to rapidly simulate trillions of design and construction alternatives, decision-makers need a way to efficiently and effectively interpret and explore the vast design space while building team and organizational consensus around a solution. Multi-attribute decision-making (MADM) and data visualization methods play an important role in successfully achieving these goals. However, while the prescriptive benefits of these methods are widely acknowledged, the precise impact that they have on design team performance and project quality has not been fully described. This paper presents the descriptive findings from four early-stage building design charrettes that implemented normative data visualization and MADM techniques. Key metrics considered are normalized solution quality, level of design exploration, and consensus among multiple decisionmakers. Empirically, the results show that MADM is associated with higher rates of group consensus, and that a combination of MADM and visual aids generates the greatest improvement in solution quality over time.