Session: DTM-03-01: Perception, Prototyping, and Inspiration in Design

Paper Number: 111714

111714 - Adaptive Optimization of Subjective Design Attributes: Characterizing Individual and Aggregate Perceptions 

Subjective attributes play a significant part in the assessment of user-facing products. Unlike performance requirements, these quantities are best evaluated through human feedback. While people share commonalities in their evaluations, allowing personalization during quantification of these subjective attributes can improve the alignment between computation and human perceptions in design. We investigate this topic through a study in which participants (N = 56) make a series of pairwise decisions between parameterized mugs, and indicate their perceptions of how comfortable each is to hold. Interactive Bayesian optimization is used to adaptively arrive at a design that optimizes this subjective quantity. Participants guide the model through only their own decisions, make decisions using a model that has already been trained with simulated data (N = 25), or data from the real decisions of other participants (N = 31). The resulting designs are evaluated across the different cases, showing the impact of individual and aggregate perceptions and the ability of Bayesian optimization to capture them. The findings imply that balancing aggregate and individual-level decisions within models simultaneously results in the best alignment with human perceptions of subjective attributes. Implications for design include the potential for personalized control over subjective attributes for designers, users, or users-as-designers.

Presenting Author: Ananya Nandy University of California, Berkeley

Presenting Author Biography: Ananya is a PhD student in the mechanical engineering department at UC Berkeley.

Authors:

Ananya Nandy University of California, Berkeley
Kosa Goucher-Lambert University of California, Berkeley