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Using big data to predict collective behavior in the real world1

Published online by Cambridge University Press:  26 February 2014

Helen Susannah Moat ,
Tobias Preis ,
Christopher Y. Olivola ,
Chengwei Liu  and
Nick Chater
Helen Susannah Moat
Affiliation:
Department of Civil, Environmental and Geomatic Engineering, University College London (UCL), London, WC1E 6BT, United Kingdom. [email protected]://www.wbs.ac.uk/about/person/suzy-moat/ Behavioural Science Group, Warwick Business School, The University of Warwick, Coventry, CV4 7AL, United Kingdom. [email protected]://www.wbs.ac.uk/about/person/tobias-preis/[email protected]://www.wbs.ac.uk/about/person/chengwei-liu/[email protected]://www.wbs.ac.uk/about/person/nick-chater/
Tobias Preis
Affiliation:
Behavioural Science Group, Warwick Business School, The University of Warwick, Coventry, CV4 7AL, United Kingdom. [email protected]://www.wbs.ac.uk/about/person/tobias-preis/[email protected]://www.wbs.ac.uk/about/person/chengwei-liu/[email protected]://www.wbs.ac.uk/about/person/nick-chater/
Christopher Y. Olivola
Affiliation:
Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213. [email protected]://sites.google.com/site/chrisolivola/
Chengwei Liu
Affiliation:
Behavioural Science Group, Warwick Business School, The University of Warwick, Coventry, CV4 7AL, United Kingdom. [email protected]://www.wbs.ac.uk/about/person/tobias-preis/[email protected]://www.wbs.ac.uk/about/person/chengwei-liu/[email protected]://www.wbs.ac.uk/about/person/nick-chater/
Nick Chater
Affiliation:
Behavioural Science Group, Warwick Business School, The University of Warwick, Coventry, CV4 7AL, United Kingdom. [email protected]://www.wbs.ac.uk/about/person/tobias-preis/[email protected]://www.wbs.ac.uk/about/person/chengwei-liu/[email protected]://www.wbs.ac.uk/about/person/nick-chater/
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Abstract

Recent studies provide convincing evidence that data on online information gathering, alongside massive real-world datasets, can give new insights into real-world collective decision making and can even anticipate future actions. We argue that Bentley et al.’s timely account should consider the full breadth, and, above all, the predictive power of big data.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 37 , Issue 1 , February 2014 , pp. 92 - 93
Copyright
Copyright © Cambridge University Press 2014 

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Footnotes

1.

Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the U.S. Government.

References

Askitas, N. & Zimmermann, K. F. (2009) Google econometrics and unemployment forecasting. Applied Economics Quarterly 55:107–20.Google Scholar
Balcan, D., Colizza, V., Gonçalves, B., Hu, H., Ramasco, J. J. & Vespignani, A. (2009) Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences USA 106:21484–89.CrossRefGoogle ScholarPubMed
Bowers, K. J., Johnson, S. & Pease, K. (2004) Prospective hotspotting: The future of crime mapping? British Journal of Criminology 44:641–58.Google Scholar
Brownstein, J. S., Freifeld, C. C. & Madoff, L. C. (2009) Digital disease detection – harnessing the web for public health surveillance. New England Journal of Medicine 360:2153–57.Google Scholar
Choi, H. & Varian, H. (2012) Predicting the present with Google Trends. Economic Record 88 (Suppl. s1):2–9.Google Scholar
Ettredge, M., Gerdes, J. & Karuga, G. (2005) Using web-based search data to predict macroeconomic statistics. Communications of the ACM 48:87–92.Google Scholar
Giguère, G. & Love, B. C. (2013) Limits in decision making arise from limits in memory retrieval. Proceedings of the National Academy of Sciences USA 110:7613–18.Google Scholar
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S. & Brilliant, L. (2009) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012–14.CrossRefGoogle ScholarPubMed
Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M. & Watts, D. J. (2010) Predicting consumer behavior with web search. Proceedings of the National Academy of Sciences USA 107:17486–90.Google Scholar
Johnson, S. D., Birks, D. J., McLaughlin, L, Bowers, K. J. & Pease, K. (2007) Prospective crime mapping in operational context: Final report. Home Office, London.Google Scholar
Johnson, S. D. & Bowers, K. J. (2004) The burglary as clue to the future: The beginnings of prospective hot-spotting. European Journal of Criminology 1:237–55.Google Scholar
King, G. (2011) Ensuring the data-rich future of the social sciences. Science 331:719–21.CrossRefGoogle ScholarPubMed
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D. & Van Alstyne, M. (2009) Computational social science. Science 323:721–23.Google Scholar
Mitchell, T. M. (2009) Mining our reality. Science 326:1644–45.CrossRefGoogle ScholarPubMed
Moat, H. S., Curme, C., Avakian, A., Kenett, D. Y., Stanley, H. E. & Preis, T. (2013) Quantifying Wikipedia usage patterns before stock market moves. Scientific Reports 3:1801.Google Scholar
Mohler, G. O., Short, M. B., Brantingham, P. J., Schoenberg, F. P. & Tita, G. E. (2011) Self-exciting point process modeling of crime. Journal of the American Statistical Association 106:100–08.Google Scholar
Olivola, C. Y. & Sagara, N. (2009) Distributions of observed death tolls govern sensitivity to human fatalities. Proceedings of the National Academy of Sciences USA 106:22151–156.CrossRefGoogle ScholarPubMed
Preis, T., Moat, H. S. & Stanley, H. E. (2013) Quantifying trading behavior in financial markets using Google Trends. Nature Scientific Reports 3, No. 1684, pp. 1–6.Google ScholarPubMed
Preis, T., Moat, H. S., Stanley, H. E. & Bishop, S. R. (2012) Quantifying the advantage of looking forward. Nature Scientific Reports 2, No. 350.Google Scholar
Preis, T., Reith, D. & Stanley, H. E. (2010) Complex dynamics of our economic life on different scales: Insights from search engine query data. Philosophical Transactions of the Royal Society A 368:5707–19.Google Scholar
Simon, H. A. (1955) A behavioral model of rational choice. Quarterly Journal of Economics 69:99–118.Google Scholar
Stewart, N. (2009) Decision by sampling: The role of the decision environment in risky choice. Quarterly Journal of Experimental Psychology 62:1041–62.Google Scholar
Stewart, N., Chater, N. & Brown, G. D. A. (2006) Decision by sampling. Cognitive Psychology 53:1–26.Google Scholar
Tizzoni, M., Bajardi, P., Poletto, C., Ramasco, J. J., Balcan, D., Gonçalves, B., Perra, N., Colizza, V. & Vespignani, A. (2012) Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm. BMC Medicine 10:165.Google Scholar
Vespignani, A. (2009) Predicting the behavior of techno-social systems. Science 325:425–28.Google Scholar