0
Research Papers: Design Automation

A Simulation Method to Estimate Nonparametric Distribution of Heterogeneous Consumer Preference From Market-Level Choice Data

[+] Author and Article Information
Changmuk Kang

Assistant Professor
Department of Industrial and Information Systems Engineering,
Soongsil University,
Seoul 06978, South Korea
e-mail: changmuk.kang@ssu.ac.kr

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 9, 2016; final manuscript received August 4, 2016; published online September 13, 2016. Assoc. Editor: Gul E. Okudan Kremer.

J. Mech. Des 138(12), 121402 (Sep 13, 2016) (9 pages) Paper No: MD-16-1113; doi: 10.1115/1.4034470 History: Received February 09, 2016; Revised August 04, 2016

In recent decision-based design trends, product design is optimized for maximizing utility to consumers. A discrete-choice analysis (DCA) model is a widely utilized tool for quantitatively assessing how consumers evaluate utility of a product. Ordinary DCA models specify utility as linear combination of attribute values of a product and coefficients that represent preference of consumers. Assuming that the coefficient value is heterogenous between individual consumers, this study proposes a method to estimate its nonparametric distribution using market-level data, which is the market share of existing products. Where consumers consider k attributes of a product, his/her preference is represented by a k-dimensional vector of coefficient values. This method simulates an empirical distribution of the vectors in k-dimensional space. The whole space is first fragmented by disjoint regions, vectors in which prefer a specific product than others, and then, random points are sampled in each region as much as market share of the corresponding product. In a sense that more points are sampled for a more popular product, the empirical distribution is population of preference vectors. This method is practically useful since it utilizes only market-level data, which are relatively easy to gather than individual-level choice instances. In addition, the simulation procedure is intuitive and easy to implement.

FIGURES IN THIS ARTICLE
<>
Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

A flowchart of the proposed sampling procedure

Grahic Jump Location
Fig. 4

A graphical representation of the importance sampling. Vector ei’s are extreme rays, b¯ is a random convex combination of ei’s, and b is a resulting sample scaled to the sphere surface.

Grahic Jump Location
Fig. 2

An example of redundant inequalities (l4 and l5)

Grahic Jump Location
Fig. 3

A graphical representation of the rejection sampling. Dots are sampled points.

Grahic Jump Location
Fig. 7

Histograms of relative frequency of sampled βn values weighted by their estimated shares. The solid lines are drawn from the true distribution g(b).

Grahic Jump Location
Fig. 5

Histograms of sampled βn values by the proposed method. The solid lines are drawn from the true distribution g(b).

Grahic Jump Location
Fig. 6

Contour graphs representing joint distributions between row and column attributes. Each contour line represents increase of relative frequency by 0.2%. Graph (a) is the true distributions and (b) is the sampled ones by the proposed method.

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In