Assessing Consumer Demand with Noisy Neural Measurements

42 Pages Posted: 24 May 2018 Last revised: 14 Feb 2019

See all articles by Ryan Webb

Ryan Webb

University of Toronto

Nitin Mehta

University of Toronto - Rotman School of Management

Ifat Levy

Yale University - School of Medicine

Date Written: February 10, 2019

Abstract

Recent studies have used the random utility framework to examine whether neural data can assess and predict demand for consumer products, both within and across individuals. However the effectiveness of this methodology has been limited by the large degree of measurement error in neural data. The resulting “error-in-variables” problem severely biases the estimates of the relationship between neural measurements and choice behaviour, thus limiting the role such data can play in assessing marginal contributions to utility. In this article, we propose a method for controlling for this large degree of measurement error in value regions of the brain. We propose that additional neural variables from areas of the brain that are unrelated to valuation can serve as “proxies” for the measurement error in value regions, substantially alleviating the bias in model estimates. We also demonstrate that standard methods for dealing with the error-in-variables problem (instrumental variables) are limited in the context of neural data. We demonstrate the feasibility of our proposed method on an existing dataset of fMRI measurements and consumer choices. After controlling for measurement error, we find a considerable reduction in the variation of estimates across consumers.

Suggested Citation

Webb, Ryan and Mehta, Nitin and Levy, Ifat, Assessing Consumer Demand with Noisy Neural Measurements (February 10, 2019). Rotman School of Management Working Paper No. 3178148. Available at SSRN: https://ssrn.com/abstract=3178148 or http://dx.doi.org/10.2139/ssrn.3178148

Ryan Webb (Contact Author)

University of Toronto ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

Nitin Mehta

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

Ifat Levy

Yale University - School of Medicine ( email )

333 Cedar Street
New Haven, CT 06520-8034
United States

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