Searching Choices: Quantifying Decision-Making Processes Using Search Engine Data

H. S. Moat, C. Y. Olivola, N. Chater & T. Preis. Searching Choices: Quantifying Decisionā€Making Processes Using Search Engine Data. Topics in Cognitive Science 8, 685-696 (2016).

12 Pages Posted: 9 Apr 2019

See all articles by Helen Susannah Moat

Helen Susannah Moat

University College London - Department of Civil, Environmental and Geomatic Engineering; Boston University - Center for Polymer Studies

Christopher Y. Olivola

Tepper School of Business, Carnegie Mellon University

Nick Chater

University of Warwick - Warwick Business School

Tobias Preis

Data Science Lab, Behavioural Science, Warwick Business School; The Alan Turing Institute

Date Written: June 1, 2016

Abstract

When making a decision, humans consider two types of information: information they have acquired through their prior experience of the world, and further information they gather to support the decision in question. Here, we present evidence that data from search engines such as Google can help us model both sources of information. We show that statistics from search engines on the frequency of content on the Internet can help us estimate the statistical structure of prior experience; and, specifically, we outline how such statistics can inform psychological theories concerning the valuation of human lives, or choices involving delayed outcomes. Turning to information gathering, we show that search query data might help measure human information gathering, and it may predict subsequent decisions. Such data enable us to compare information gathered across nations, where analyses suggest, for example, a greater focus on the future in countries with a higher per capita GDP. We conclude that search engine data constitute a valuable new resource for cognitive scientists, offering a fascinating new tool for understanding the human decision-making process.

Keywords: Search Engine Data, Google, Wikipedia, Decision Making, Behavioral Science, Computational Social Science

JEL Classification: A10, B40, C10, C20, C22, C53, C90, D70, D79, D83, J10, J11, O40, O47

Suggested Citation

Moat, Helen Susannah and Olivola, Christopher Y. and Chater, Nick and Preis, Tobias, Searching Choices: Quantifying Decision-Making Processes Using Search Engine Data (June 1, 2016). H. S. Moat, C. Y. Olivola, N. Chater & T. Preis. Searching Choices: Quantifying Decisionā€Making Processes Using Search Engine Data. Topics in Cognitive Science 8, 685-696 (2016).. Available at SSRN: https://ssrn.com/abstract=3354184

Helen Susannah Moat

University College London - Department of Civil, Environmental and Geomatic Engineering ( email )

Gower Street
London, WC1E 6BT
United Kingdom

HOME PAGE: http://www.suzymoat.co.uk

Boston University - Center for Polymer Studies ( email )

590 Commonwealth Avenue
Boston, MA 02215
United States

Christopher Y. Olivola

Tepper School of Business, Carnegie Mellon University ( email )

5000 Forbes Ave.
Pittsburgh, PA 15213
United States

Nick Chater

University of Warwick - Warwick Business School ( email )

Coventry CV4 7AL
United Kingdom

HOME PAGE: http://www.wbs.ac.uk/about/person/nick-chater/

Tobias Preis (Contact Author)

Data Science Lab, Behavioural Science, Warwick Business School ( email )

University of Warwick
Coventry, CV4 7AL
United Kingdom

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

The Alan Turing Institute ( email )

British Library, 96 Euston Road
London, NW12DB
United Kingdom

HOME PAGE: http://https://www.turing.ac.uk

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