with Heterogeneous Values
Thesis for M.S. in Computational Design.
Advisors: Daniel Cardoso Llach, Ph.D and Ray Gastil.
The process of urban design can be viewed as complex negotiations among heterogeneous value agendas representing different stakeholders. As a result, any urban design problem can yield a multitude of framings, each delineates a distinct approach to address its underlying objectives. Planners and designers frequently face the unwieldy challenge of the growing complexity of design problems, characterized by a large set of interwoven and often competing objectives.
Shrouded in complex objectives and uncertainty, how can representations of design strategies provide affordance in the framing of urban design problems? Focused on multiobjective street design as the principle subject of investigation, this thesis proposed a representation of design strategies, integrating Object Process Methodology, Dynamic Bayesian Network, and graph data modeling. Subsequent experiments demonstrated that a design strategy represented as system architecture can encode the framing of its value propositions. The incorporation of Bayesian inference can meaningfully compute uncertainties in both the design and its context while supporting evidence-based beliefupdate. Finally, a graph data model can afford computational analyses that unveil latent interactions between different value framings, synergistic or conflicting, thus informs the reformulation of the design problem.
Type: Academic Research
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