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Research Papers: Design Theory and Methodology

Interpreting Idea Maps: Pairwise Comparisons Reveal What Makes Ideas Novel

[+] Author and Article Information
Faez Ahmed

Department of Mechanical Engineering,
University of Maryland,
College Park, MD 20742
e-mail: faez00@umd.edu

Sharath Kumar Ramachandran

School of Engineering Design,
Technology and Professional Programs,
The Pennsylvania State University,
University Park, PA 16801
e-mail: sharath@psu.edu

Mark Fuge

Department of Mechanical Engineering,
University of Maryland,
College Park, MD 20742
e-mail: fuge@umd.edu

Samuel Hunter

Industrial and Organizational Psychology,
The Pennsylvania State University,
University Park, PA 16801
e-mail: sth11@psu.edu

Scarlett Miller

School of Engineering Design,
Technology and Professional Programs,
The Pennsylvania State University,
University Park, PA 16801
e-mail: shm13@psu.edu

1Corresponding author.

Open-source code implementing our approach is available at: https://github.com/IDEALLAB/idea_mapContributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received June 30, 2018; final manuscript received October 19, 2018; published online December 20, 2018. Assoc. Editor: Katja Holtta-Otto.

J. Mech. Des 141(2), 021102 (Dec 20, 2018) (13 pages) Paper No: MD-18-1514; doi: 10.1115/1.4041856 History: Received June 30, 2018; Revised October 19, 2018

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 to score ideas on their uniqueness. However, their decisions about 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 (2D) idea maps. We derive these maps by asking participants 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 design domain. We also propose that novel ideas can be identified by finding outliers on these idea maps. To demonstrate our method, we conduct experimental evaluations on two datasets—colored polygons (known answer) and milk frother sketches (unknown answer). We show that idea maps shed light on factors considered by participants in judging idea similarity and the maps are robust to noisy ratings. We also compare physical maps made by participants on a white-board to their computationally generated idea maps to compare how people think about spatial arrangement of design items. This method provides a new direction of research into deriving ground truth novelty metrics by combining human judgments and computational methods.

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Topics: Design , Errors
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Figures

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Fig. 1

Example of triplet query asked from participants in our experiment. Participant answers the question: “Which design is more similar to design A?”.

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Fig. 2

(a) Dataset of ten polygons used in first experiment and (b) two idea maps with four items each. Although these maps look different, they satisfy the same set of triplet queries.

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Fig. 3

Two-dimensional embedding obtained for polygons using triplets given by the automated rater, who uses preset rules

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Fig. 4

Two-dimensional embedding obtained from polygon dataset by participant 5, who uses number of sides as primary criteria the decisions

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Fig. 5

Two-dimensional embedding obtained from polygon dataset by participant 9, who uses “color, shape, number of side” as criteria

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Fig. 6

Ten milk frother sketches used in experimental 2

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Fig. 7

Left: (a) Idea map of design sketches for participant 7. Center of the sketch represents the 2D position of embedding. Two main clusters can be seen. Right: (b) Idea map of design sketches for participant 10. Center of the sketch represents the 2D position of embedding.

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Fig. 8

Idea map obtained by combining triplets from all participants. Id of each sketch is at bottom right corner.

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Fig. 9

Left: (a) Triplet error between idea maps of embedding shown in Fig. 8 and embedding obtained using a subset of triplet ratings. 100 runs with different subsets used to obtain embeddings. Using only 30% of triplets, median error is less than 10%. Right: (b) Triplet error between embedding generated using noisy triplets compared to embedding shown in Fig. 8. We perform 100 runs and flip a subset of triplets randomly to obtain the embeddings. Small increase in the median error shows that idea maps are robust to small percentage of false ratings by participants.

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Fig. 10

Left: (a) Participant creating a map by positioning idea sketches on provided canvas. Right: (b) correspondence between human generated map and triplet map for participant 10 after Procrustes transformation. Apart from sketch 4, most sketches have minor relative displacements.

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