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. In this work, we present theoretical predictions and experimental findings demonstrating that, in decentralized networks, social influence generates learning dynamics that reliably improve the accuracy of collective beliefs. In centralized networks, however, the influence of opinion leaders undermines social learning.
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]
Submitted / Under Review
Becker, J., Centola, D., & Porter, E. The Wisdom of Political Crowds: Improved Accuracy and Decreased Polarization in Echo Chambers. (R&R at PNAS)
Becker, J., Guilbeault, D., & Smith, E.B. Making Decisions: The Crowd Classification Problem.
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.
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
Using web-based experiments provides unprecedented control over experimental conditions, 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.
Tools: MTurk API, PhP (Zend Framework), ReactJS, jQuery, HTML/CSS
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