Search for: All records

In this paper, we propose a new design variety metric based on the Herfindahl index. We also propose a practical procedure for comparing variety metrics via the construction of ground truth datasets from pairwise comparisons by experts. Using two new datasets, we show that this new variety measure aligns with human ratings more than some existing and commonly used tree-based metrics. This metric also has three main advantages over existing metrics: a) It is a super-modular function, which enables us to optimize design variety using a polynomial time greedy algorithm. b) The parametric nature of this metric allows us to fit the metric to better represent variety for new domains. c) It has higher sensitivity in distinguishing between variety of sets of randomly selected designs than existing methods. Overall, our results shed light on some qualities that good design variety metrics should possess and the non-trivial challenges associated with collecting the data needed to measure those qualities. 

In this paper, we propose a new design variety metric based on the Herfindahl index. We also propose a practical procedure for comparing variety metrics via the construction of ground truth datasets from pairwise comparisons by experts. Using two new datasets, we show that this new variety measure aligns with human ratings more than some existing and commonly used tree-based metrics. This metric also has three main advantages over existing metrics: a) It is a super-modular function, which enables us to optimize design variety using a polynomial time greedy algorithm. b) The parametric nature of this metric allows us to fit the metric to better represent variety for new domains. c) It has higher sensitivity in distinguishing between variety of sets of randomly selected designs than existing methods. Overall, our results shed light on some qualities that good design variety metrics should possess and the non-trivial challenges associated with collecting the data needed to measure those qualities. 

Assessing similarity between design ideas is an inherent part of many design evaluations to measure novelty. In such evaluation tasks, humans excel at making mental connections among diverse knowledge sets and scoring ideas on their uniqueness. However, their decisions on novelty are often subjective and difficult to explain. In this paper, we demonstrate a way to uncover human judgment of design idea similarity using two dimensional idea maps. We derive these maps by asking humans for simple similarity comparisons of the form “Is idea A more similar to idea B or to idea C?” We show that these maps give insight into the relationships between ideas and help understand the domain. We also propose that the novelty of ideas can be estimated by measuring how far items are on these maps. We demonstrate our methodology through the experimental evaluations on two datasets of colored polygons (known answer) and milk frothers (unknown answer) sketches. We show that these maps shed light on factors considered by raters in judging idea similarity. We also show how maps change when less data is available or false/noisy ratings are provided. This method provides a new direction of research into deriving ground truth novelty metrics by combining human judgments and computational methods.