A big data analysis of the relationship between future thinking and decision-making

• Type of Material: Article
• Authors: Robert Thorstad, Emory University; Phillip Wolff, Emory University
• Language: English
• Date: 2018-02-20
• Publisher: National Academy of Sciences
• Publication Version: Final Published Version
• Copyright Statement: © 2018 Published under the PNAS license.
• Final Published Version (URL): http://dx.doi.org/10.1073/pnas.1706589115
• Title of Journal or Parent Work: Proceedings of the National Academy of Sciences
• ISSN: 0027-8424
• Volume: 115
• Issue: 8
• Start Page: E1740
• End Page: E1748
• Grant/Funding Information: This research was supported by John Templeton Foundation Grant “Prospective Psychology Stage 2: A Research Competition” (to Martin Seligman, which was awarded to P.W.).; The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of the John Templeton Foundation.
• Keywords: big data; decision-making; future thinking
• Research Categories: Psychology, General

Abstract

We use big data methods to investigate how decision-making might depend on future sightedness (that is, on how far into the future people’s thoughts about the future extend). In study 1, we establish a link between future thinking and decision-making at the population level in showing that US states with citizens having relatively far future sightedness, as reflected in their tweets, take fewer risks than citizens in states having relatively near future sightedness. In study 2, we analyze people’s tweets to confirm a connection between future sightedness and decision-making at the individual level in showing that people with long future sightedness are more likely to choose larger future rewards over smaller immediate rewards. In study 3, we show that risk taking decreases with increases in future sightedness as reflected in people’s tweets. The ability of future sightedness to predict decisions suggests that future sightedness is a relatively stable cognitive characteristic. This implication was supported in an analysis of tweets by over 38,000 people that showed that future sightedness has both state and trait characteristics (study 4). In study 5, we provide evidence for a potential mechanism by which future sightedness can affect decisions in showing that far future sightedness can make the future seem more connected to the present, as reflected in how people refer to the present, past, and future in their tweets over the course of several minutes. Our studies show how big data methods can be applied to naturalistic data to reveal underlying psychological properties and processes.