Skip to main content Accessibility help
×
Home
Hostname: page-component-55597f9d44-ms7nj Total loading time: 1.048 Render date: 2022-08-16T02:37:37.530Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true

Using big data to map the relationship between time perspectives and economic outputs

Published online by Cambridge University Press:  20 November 2019

Christopher Y. Olivola
Affiliation:
Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213olivola@cmu.eduhttps://sites.google.com/site/chrisolivola/ Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213
Helen Susannah Moat
Affiliation:
Data Science Lab, Behavioural Science Group, Warwick Business School, University of Warwick, Coventry CV4 7AL, United KingdomSuzy.Moat@wbs.ac.ukTobias.Preis@wbs.ac.ukhttp://www.wbs.ac.uk/about/person/suzy-moat/http://www.wbs.ac.uk/about/person/tobias-preis/ The Alan Turing Institute, British Library, London NW1 2DB, United Kingdom.
Tobias Preis
Affiliation:
Data Science Lab, Behavioural Science Group, Warwick Business School, University of Warwick, Coventry CV4 7AL, United KingdomSuzy.Moat@wbs.ac.ukTobias.Preis@wbs.ac.ukhttp://www.wbs.ac.uk/about/person/suzy-moat/http://www.wbs.ac.uk/about/person/tobias-preis/ The Alan Turing Institute, British Library, London NW1 2DB, United Kingdom.

Abstract

Recent studies have shown that population-level time perspectives can be approximated using “big data” on search engine queries, and that these indices, in turn, predict the per-capita Gross Domestic Product of countries. Although these findings seem to support Baumard's suggestion that affluence makes people more future-oriented, they also reveal a more complex relationship between time perspectives and economic outputs.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Moat, H. S., Olivola, C. Y., Preis, T. & Chater, N. (2016) Searching choices: Quantifying decision making processes using search engine data. Topics in Cognitive Science 8(3):685–96.CrossRefGoogle ScholarPubMed
Moat, H. S., Preis, T., Olivola, C. Y., Liu, C. & Chater, N. (2014) Using big data to predict collective behavior in the real world. Behavioral and Brain Sciences 37:9293.CrossRefGoogle ScholarPubMed
Noguchi, T., Stewart, N., Olivola, C. Y., Moat, H. S. & Preis, T. (2014) Characterizing the time-perspective of nations with search engine query data. PLoS One 9(4): e95209.CrossRefGoogle ScholarPubMed
Olivola, C. Y. & Chater, N. (2017) Decision by sampling: Connecting preferences to real-world regularities. In: Big data in cognitive science, ed. Jones, M. N.. Routledge.Google Scholar
Preis, T., Moat, H. S., Stanley, H. E. & Bishop, S. R. (2012) Quantifying the advantage of looking forward. Scientific Reports 2:350.CrossRefGoogle ScholarPubMed
Read, D., Olivola, C. Y. & Hardisty, D. J. (2017) The value of nothing: Asymmetric attention to opportunity costs drives intertemporal decision making. Management Science 63(12):4277–97.CrossRefGoogle Scholar
Stewart, N., Chater, N. & Brown, G. D. A. (2006) Decision by sampling. Cognitive Psychology 53:126.CrossRefGoogle ScholarPubMed
2
Cited by

Linked content

Please note a has been issued for this article.

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Using big data to map the relationship between time perspectives and economic outputs
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Using big data to map the relationship between time perspectives and economic outputs
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Using big data to map the relationship between time perspectives and economic outputs
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *