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]

In preparation

Becker, J. & Centola, D.  From Efficient Markets to Effective Investors:  Can individuals benefit from the wisdom of crowds?  (To be presented at the Annual Meeting of the American Sociological Association, 2018.)

Becker, J., Centola, D., & Porter, E.  The Wisdom of Political Crowds:  Improved Accuracy and Decreased Polarization in Echo Chambers.  (To be presented at the Annual Meeting of the American Sociological Association, 2018.)

Currently Cpllecting Data

Improving Medical Decisions Making Through the Wisdom of Crowds. Robert Wood Johnson Pioneer Grant with Damon Centola, Devon Brackbill, Jaya Aysola, and Jingwen Zhang

coordination decisions

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]

In Preparation

Becker, j.  The Network Dynamics of Equilibrium Selection in Coordination Decisions. (To be presented at the Annual Meeting of the American Sociological Association, 2018.) [read]

Computational methods

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 Experiments

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


Network Analysis

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