  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 non-domain 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 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; 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.
