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Naturalistic multiattribute choice

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Bhatia, Sudeep and Stewart, Neil (2018) Naturalistic multiattribute choice. Cognition, 179 . pp. 71-88. doi:10.1016/j.cognition.2018.05.025 ISSN 1873-7838.

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Official URL: https://doi.org/10.1016/j.cognition.2018.05.025

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Abstract

We study how people evaluate and aggregate the attributes of naturalistic choice objects, such as movies and food items. Our approach applies theories of object representation in semantic memory research to large-scale crowd-sourced data, to recover multiattribute representations for common choice objects. We then use standard choice experiments to test the predictive power of various decision rules for weighting and aggregating these multiattribute representations. Our experiments yield three novel conclusions: 1. Existing multiattribute decision rules, applied to object representations trained on crowd-sourced data, predict participant choice behavior with a high degree of accuracy; 2. Contrary to prior work on multiattribute choice, weighted additive decision rules outperform heuristic rules in out-of-sample predictions; and 3. The best performing decision rules utilize rich object representations with a large number of underlying attributes. Our results have important implications for the study of multiattribute choice.

Item Type: Journal Article
Subjects: B Philosophy. Psychology. Religion > BF Psychology
H Social Sciences > HF Commerce
Divisions: Faculty of Science, Engineering and Medicine > Science > Psychology
Library of Congress Subject Headings (LCSH): Multiattribute models (Consumer attitudes), Semantic memory
Journal or Publication Title: Cognition
Publisher: Elsevier
ISSN: 1873-7838
Official Date: October 2018
Dates:
DateEvent
October 2018Published
15 June 2018Available
29 May 2018Accepted
Volume: 179
Page Range: pp. 71-88
DOI: 10.1016/j.cognition.2018.05.025
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 30 May 2018
Date of first compliant Open Access: 18 June 2018
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
SES-1626825National Science Foundationhttp://dx.doi.org/10.13039/100000001
ES/K002201/1 [ESRC] Economic and Social Research Councilhttp://dx.doi.org/10.13039/501100000269
ES/N018192/1 [ESRC] Economic and Social Research Councilhttp://dx.doi.org/10.13039/501100000269
RP2012-V-022Leverhulme Trusthttp://dx.doi.org/10.13039/501100000275
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