Wisdom of crowds
Since the discovery of the wisdom of crowds over 100 years ago, theories of collective intelligence have held that social influence undermines collective judgments. This work presents theoretical predictions and experimental findings demonstrating that, in the right conditions, social influence generates learning dynamics that reliably improve the accuracy of collective beliefs. My work resolves the tension between theories of “Groupthink” and research showing the benefits of group decision-making by showing when groups can form accurate beliefs and why they sometimes fail.
Becker, J., Centola, D., & Porter, E. The Wisdom of Partisan Crowds. Proceedings of the National Academy of Sciences, In Press. [read]
Guilbeault, D. Becker, J. & Centola, D. (2018) Social learning and partisan bias in the interpretation of climate trends Proceedings of the National Academy of Science, 115 (39) [read]
Becker, J., Brackbill, D., & Centola, D. (2017). Network dynamics of social influence in the wisdom of crowds. Proceedings of the National Academy of Sciences, 114 (26), E5070–E5076 [read]
Becker, J., Guilbeault, D., & Smith, E.B. Against Voting? The Crowd Classification Problem. (Under Review at Management Science) [read]
Becker, J., Smith, E.B., Horvat, A. A Network Solution to Groupthink. Generously funded by the Dispute Resolution Research Center and the Northwestern Institute for Complex Systems.
Smith, E.B. and Becker, J. Evidence for a Collective Intelligence Theory of Market Efficiency.
Guilbeault, D., Becker, J., Woolley, S. Nuance Matters: Collective Intelligence Dynamics in the Spread of Misinformation.
Becker, J. & Centola, D. From Efficient Markets to Effective Investors: Can individuals benefit from the wisdom of crowds?
Centola, D., Becker, J., Zhang, J., Guilbeault, D. Social learning improves clinical diagnostic accuracy.
Becker, J. The Wisdom of Crowds: From Collected Intelligence to Collective Intelligence.
From social conventions such as greetings and language, to technological conventions such as digital communication protocol, a wide variety of social behaviors are shaped by coordination incentives.
Centola, D., Becker, J., Brackbill, D., & Baronchelli, A. (2018). Experimental evidence for tipping points in social convention. Science, 360(6393), 1116-1119. [read]
Becker, J. The Network Dynamics of Social and Technological Conventions. (Under review at Organization Science.) [read]
Agent Based Modeling
By formalizing social theory with computational models, researchers can excel in two key goals. First, we can use models to determine whether a theoretical account provides sufficient conditions to explain a phenomenon of interest. Second, we can use models to provide precise theoretical predictions, developing clearly defined hypotheses to be tested with experimental design and observational data.
Tools: Java, R, NetLogo
Web-based experiments provide unprecedented control over experimental procedures, allowing researchers not only to design every aspect of the subject experience, but also to obtain high-fidelity data that records every interaction by every participant.
Be sure to check out the amazing empirica.ly platform.
Tools: MTurk API, PhP/Zend, Meteor, ReactJS
Graph theory provides a lingua franca that enables a broad interdisciplinary dialogue among social researchers, physicists, and computer scientists. While my current research focuses on agent based modeling and experimental design, my coursework and research fellowship has given me in depth experience in observational network analysis including time-series analysis (multiplex networks) for social networks as well as text analysis using semantic networks.
Tools: R (igraph), Java (Gephi), Twitter API, Selenium