Hostname: page-component-7c8c6479df-995ml Total loading time: 0 Render date: 2024-03-29T07:08:01.843Z Has data issue: false hasContentIssue false

More on maps, terrains, and behaviors

Published online by Cambridge University Press:  26 February 2014

R. Alexander Bentley
Affiliation:
Department of Archaeology and Anthropology, University of Bristol, Bristol BS8 1UU, United Kingdom. r.a.bentley@bristol.ac.ukhttp://www.alex-bentley.com
Michael J. O'Brien
Affiliation:
Department of Anthropology, University of Missouri, Columbia, MO 65211. obrienm@missouri.eduhttp://cladistics.coas.missouri.edu
William A. Brock
Affiliation:
Department of Economics, University of Missouri, Columbia, MO 65211 Department of Economics, University of Wisconsin, Madison, WI 53706. wbrock@scc.wisc.eduhttp://www.ssc.wisc.edu/~wbrock/

Abstract

In a recent New York Times column (April 15, 2013), David Brooks discussed how the big-data agenda lacks a coherent framework of social theory – a deficiency that the Bentley, O'Brien, and Brock (henceforth BOB) model was meant to overcome. Or, stated less pretentiously, the model was meant as a first step in that direction – a map that hopefully would serve as a minimal, practical, and accessible framework that behavioral scientists could use to analyze big data. Rather than treating big data as a record of, and also a predictor of, where and when certain behaviors might take place, the BOB model is interested in what big data reveal about how decisions are being made, how collective behavior evolves from daily to decadal time scales, and how this varies across communities.

Type
Authors' Response
Copyright
Copyright © Cambridge University Press 2014 

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

Acerbi, A., Lampos, V., Garnett, P. & Bentley, R. A. (2013) The expressions of emotions in 20th century books. PLoS ONE 8(3):e59030.Google Scholar
Anderson, S., de Palma, A. & Thisse, J. F. (1992) Discrete choice theory of product differentiation. MIT Press.Google Scholar
Aral, S. & Walker, D. (2012) Identifying influential and susceptible members of social networks. Science 337:337–41.Google Scholar
Aral, S., Muchnik, L. & Sundararajan, A. (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences USA 106:21544–49.Google Scholar
Axelrod, R. & Cohen, M. D. (1999) Harnessing complexity: Organizational implications of a scientific frontier. Free Press.Google Scholar
Ben-Akiva, M., de Palma, A., McFadden, D., Abou-Zeid, M., Chiappori, P-A., de Lapparent, M., Durlauf, S. N., Fosgerau, M., Fukuda, D., Hess, S., Manski, C., Pakes, A., Picard, N. & Walker, J. (2012) Process and context in choice models. Marketing Letters 23:439–56.Google Scholar
Bentley, R. A., Garnett, P., O'Brien M. J. & Brock, W. A. (2012) Word diffusion and climate science. PLoS ONE 7(11):e47966.Google Scholar
Bentley, R. A. & O'Brien, M. J. (2012) The buzzwords of the crowd. New York Times, December 1, 2012, p. SR4.Google Scholar
Berger, J. & Le Mens, G. (2009) How adoption speed affects the abandonment of cultural tastes. Proceedings of the National Academy of Sciences USA 106:8146–50.Google Scholar
Berger, J. & Milkman, K. L. (2012) What makes online content viral? Journal of Marketing Research 49:192205.Google Scholar
Berry, S., Levinsohn, J. & Pakes, A. (1995) Automobile prices in market equilibrium. Econometrica 63:841–90.Google Scholar
Blume, L. E., Brock, W. A. Durlauf, S. N. & Ioannides, Y. M. (2011) Identification of social interactions. In: Social economics, ed. Benhabib, J., Jackson, M. O. & Bisin, A., pp. 853964. North-Holland.Google Scholar
Bramoullé, Y. (2007) Anti-coordination and social interactions. Games and Economic Behavior 58:3049.Google Scholar
Brock, W. A. (1993) Pathways to randomness in the economy: Emergent nonlinearity and chaos in economics and finance. Estudios Economicos 8(1):355.Google Scholar
Brock, W. A. & Durlauf, S. N. (2001a) Discrete choice with social interactions. Review of Economic Studies 68:229–72.Google Scholar
Brock, W. A. & Durlauf, S. N. (2001b) Interactions-based models. In: Handbook of econometrics, vol. 5, ed. Heckman, J. & Leamer, E., pp. 3297–80. Elsevier Science.Google Scholar
Brock, W. A. & Durlauf, S. N. (2006) Multinomial choice with social interactions. In: The economy as an evolving complex system, ed. Blume, L. E. & Durlauf, S. N., pp. 175206. Oxford University Press.Google Scholar
Brock, W. A. & Durlauf, S. N. (2010) Adoption curves and social interactions. Journal of the European Economic Association 8:232–51.Google Scholar
Choi, H. & Varian, H. (2012) Predicting the present with Google Trends. Economic Record 88(Suppl. s1):29.Google Scholar
Gladwell, M. (2010) Small change: Why the revolution will not be tweeted. The New Yorker, October 4, 2010, pp. 42–49.Google Scholar
Google Inc. (2012) Project Glass. Available at: http://www.google.com/glass.Google Scholar
Granovetter, M. S. (1973) The strength of weak ties. American Journal of Sociology 78:1360–80.Google Scholar
Gureckis, T. M. & Goldstone, R. L. (2009) How you named your child: Understanding the relationship between individual decision making and collective outcomes. Topics in Cognitive Science 1:651–74.Google Scholar
Hacking, I. (1992) Multiple personality disorders and its hosts. History of the Human Sciences 5(2):331.Google Scholar
Hacking, I. (1995) The looping effect of human kinds. In: Causal cognition: A multidisciplinary debate, ed. Sperber, D., Premack, D. & Premack, A. J., pp. 351–83. Oxford University Press.Google Scholar
Hill, R. A. & Dunbar, R. I. M. (2003) Social network size in humans. Human Nature 14:5372.Google Scholar
Hommes, C. (2013) Behavioral rationality and heterogeneous expectations in complex economic systems. Cambridge University Press.Google Scholar
Hoppitt, W., Kandler, A., Kendal, J. R. & Laland, K. N. (2010) The effect of task structure on diffusion dynamics: Implications for diffusion curve and network-based analyses. Learning & Behavior 38:243–51.Google Scholar
Kandori, M., Mailath, G. J. & Rob, R. (1993) Learning, mutation, and long run equilibria in games. Econometrica 61:2956.Google Scholar
Lampos, V. & Cristianini, N. (2012) Nowcasting events from the social Web with statistical learning. ACM Transactions on Intelligent Systems and Technology 3(4), Article 72:122.Google Scholar
Manski, C. F. (1993) Identification of endogenous social effects: The reflection problem. Review of Economic Studies 60:531–42.Google Scholar
McKelvey, R. D. & Palfrey, T. R. (1995) Quantal response equilibria for normal form games. Games and Economic Behavior 10:638.Google Scholar
McLuhan, M. (1964) Understanding media: The extensions of man. McGraw-Hill.Google Scholar
Mesoudi, A. (2008) An experimental simulation of the “copy-successful-individuals” cultural learning strategy: Adaptive landscapes, producer–scrounger dynamics, and informational access costs. Evolution and Human Behavior 29:350–63.Google Scholar
Mesoudi, A. (2010) The experimental study of cultural innovation. In: Innovation in cultural systems: Contributions from evolutionary anthropology, ed. O'Brien, M. J. & Shennan, S. J., pp. 175–91. MIT Press.Google Scholar
Mesoudi, A. & O'Brien, M. J. (2008a) The cultural transmission of Great Basin projectile-point technology I: An experimental simulation. American Antiquity 73:328.Google Scholar
Mesoudi, A. & O'Brien, M. J. (2008b) The cultural transmission of Great Basin projectile point technology II: An agent-based computer simulation. American Antiquity 73:627–44.Google Scholar
O'Brien, M. J. & Bentley, R. A. (2011) Stimulated variation and cascades: Two processes in the evolution of complex technological systems. Journal of Archaeological Method and Theory 18:309–35.Google Scholar
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. 16.Google Scholar
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
Salganik, M. J., Dodds, P. S. & Watts, D. J. (2006) Experimental study of inequality and unpredictability in an artificial cultural market. Science 311(5762):854–56. doi:10.1126/science.1121066.Google Scholar
Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W., Dakos, V., van der Koppel, J., van de Leemput, I. A., Levin, S. A., van Nes, E. H., Pascual, M. & Vandermeer, J. (2012) Anticipating critical transitions. Science 338:344–48.Google Scholar
Shalizi, C. R. & Thomas, A. C. (2010) Homophily and contagion are genetically confounded in observational social network studies. Sociological Methods and Research 40:211–39.Google Scholar
Shultz, T. R. (2003) Computational developmental psychology. MIT Press.Google Scholar
Tausczik, Y. & Pennebaker, J. W. (2010) The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology 29:2454.Google Scholar
Watts, D. J. & Hasker, S. (2006) Marketing in an unpredictable world. Harvard Business Review 84(9):2530.Google Scholar
Watts, D. J. & Strogatz, S. H. (1998) Collective dynamics of “small-world' networks. Nature 393(6684):440–42. Available at: http://www.ncbi.nlm.nih.gov/pubmed/9623998 Google Scholar