Choice Modelling with Gaussian Processes in the Social Sciences: A Case Study of Neighbourhood Choice in Stockholm

25 Pages Posted: 15 Nov 2016 Last revised: 21 Apr 2020

See all articles by Richard Mann

Richard Mann

University of Leeds - School of Mathematics

Viktoria Spaiser

University of Leeds

Lina Hedman

Uppsala University

David Sumpter

Uppsala University - Department of Mathematics

Date Written: November 14, 2016

Abstract

We present a non-parametric extension of the conditional logit model, using Gaussian pro- cess priors. The conditional logit model is used in quantitative social science for inferring interaction effects between personal features and choice characteristics from observations of individual multinomial decisions, such as where to live, which car to buy or which school to choose. The classic, parametric model presupposes a latent utility function that is a linear combination of choice characteristics and their interactions with personal features. This imposes strong and unrealistic constraints on the form of individuals’ preferences. Extensions using non-linear basis functions derived from the original features can ameliorate this problem but at the cost of high model complexity and increased reliance on the user in model specification. In this paper we develop a non-parametric conditional logit model based on Gaussian process logit models. We demonstrate its application on housing choice data from over 50,000 moving households from the Stockholm area over a two year period to reveal complex homophilic patterns in income, ethnicity and parental status.

Keywords: conditional logit model, discrete choice model, Gaussian processes, neigh- bourhood choice, segregation

Suggested Citation

Mann, Richard and Spaiser, Viktoria and Hedman, Lina and Sumpter, David, Choice Modelling with Gaussian Processes in the Social Sciences: A Case Study of Neighbourhood Choice in Stockholm (November 14, 2016). Available at SSRN: https://ssrn.com/abstract=2869215 or http://dx.doi.org/10.2139/ssrn.2869215

Richard Mann (Contact Author)

University of Leeds - School of Mathematics ( email )

Leeds LS2 9JT
United Kingdom

HOME PAGE: http://www.richardpmann.com

Viktoria Spaiser

University of Leeds ( email )

Social Science Building
Woodhouse Lane
Leeds, LS2 9JT
United Kingdom
0044 7470 122518 (Phone)

HOME PAGE: http://www.viktoriaspaiser.com

Lina Hedman

Uppsala University ( email )

Box 513
Uppsala, 751 20
Sweden

David Sumpter

Uppsala University - Department of Mathematics ( email )

Ångströmlaboratoriet
Lägerhyddsvägen 1
Uppsala, 75106
Sweden

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