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      Contextualizing Human Psychology


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          The study of psychology has been handicapped by the difficulty of measuring how individual traits affect interactions with the surrounding social structures and how this interaction affects both individual life outcomes and group characteristics. With the advent of continuous, fine-grain data from cell phones, credit cards, and online interactions, the field of human psychology can become better at understanding the role of social context by combining these new data sources with standard experimental methods. This article will examine how these new tools can shed light on the influence individual psychological traits have on life outcomes, as well as on social properties such as inequality. Use of these new data sources requires special care to uphold ethical standards, and so new methodologies have been developed.

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          Social science. Computational social science.

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            Predicting poverty and wealth from mobile phone metadata.

            Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. In industrialized economies, novel sources of data are enabling new approaches to demographic profiling, but in developing countries, fewer sources of big data exist. We show that an individual's past history of mobile phone use can be used to infer his or her socioeconomic status. Furthermore, we demonstrate that the predicted attributes of millions of individuals can, in turn, accurately reconstruct the distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households. In resource-constrained environments where censuses and household surveys are rare, this approach creates an option for gathering localized and timely information at a fraction of the cost of traditional methods.
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              Urban characteristics attributable to density-driven tie formation


                Author and article information

                Technology, Mind, and Behavior
                American Psychological Association
                September 1, 2020
                : 1
                : 1
                [1]Connection Science Program, Massachusetts Institute of Technology
                Author notes
                Action Editor: Danielle S. McNamara was the action editor for this article.
                Disclosure: The author declares no conflict of interest.
                Disclaimer: Interactive content is included in the online version of this article.
                [*] Alex Pentland, Department of Connection Science, Massachusetts Institute of Technology, E15-387, 20 Ames St, MIT, Cambridge, MA 02139, USA pentland@mit.edu
                Author information
                © 2020 The Author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND). This license permits copying and redistributing the work in any medium or format for noncommercial use provided the original authors and source are credited and a link to the license is included in attribution. No derivative works are permitted under this license.

                Self URI (journal-page): https://tmb.apaopen.org/

                Education,Psychology,Vocational technology,Engineering,Clinical Psychology & Psychiatry
                life outcomes,social networks,technology and psychology,child development,computational social science


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