By Topic

Crowdsourcing Predictors of Behavioral Outcomes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Josh C. Bongard ; College of Engineering and Mathematical Sciences, University of Vermont, Burlington, VT, USA ; Paul D. H. Hines ; Dylan Conger ; Peter Hurd
more authors

Generating models from large data sets-and determining which subsets of data to mine-is becoming increasingly automated. However, choosing what data to collect in the first place requires human intuition or experience, usually supplied by a domain expert. This paper describes a new approach to machine science which demonstrates for the first time that nondomain experts can collectively formulate features and provide values for those features such that they are predictive of some behavioral outcome of interest. This was accomplished by building a Web platform in which human groups interact to both respond to questions likely to help predict a behavioral outcome and pose new questions to their peers. This results in a dynamically growing online survey, but the result of this cooperative behavior also leads to models that can predict the user's outcomes based on their responses to the user-generated survey questions. Here, we describe two Web-based experiments that instantiate this approach: The first site led to models that can predict users' monthly electric energy consumption, and the other led to models that can predict users' body mass index. As exponential increases in content are often observed in successful online collaborative communities, the proposed methodology may, in the future, lead to similar exponential rises in discovery and insight into the causal factors of behavioral outcomes.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics: Systems  (Volume:43 ,  Issue: 1 )