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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.
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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
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Copyright
Copyright © Cambridge University Press 2019 

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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