Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-29T02:11:17.341Z Has data issue: false hasContentIssue false
  • English
  • Français

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 Open the ORCID record for Christopher Y. Olivola [Opens in a new window] ,
Helen Susannah Moat Open the ORCID record for Helen Susannah Moat [Opens in a new window]  and
Tobias Preis Open the ORCID record for Tobias Preis [Opens in a new window]
Christopher Y. Olivola
Affiliation:
Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA [email protected]://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 [email protected]@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 [email protected]@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.
Get access Rights & Permissions [Opens in a new window]

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
Information
Behavioral and Brain Sciences , Volume 42 , 2019 , e206
Check for updates
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.Google Scholar
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 37:92–93.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Stewart, N., Chater, N. & Brown, G. D. A. (2006) Decision by sampling. Cognitive Psychology 53:1–26.Google Scholar