  Bayesian model averaging enables one to combine the disparate predictions of
a number of models in a coherent fashion, leading to superior predictive
performance. The improvement in performance arises from averaging models that
make different predictions. In this work, we tap into perhaps the biggest
driver of different predictions---different analysts---in order to gain the
full benefits of model averaging. In a standard implementation of our method,
several data analysts work independently on portions of a data set, eliciting
separate models which are eventually updated and combined through a specific
weighting method. We call this modeling procedure Bayesian Synthesis. The
methodology helps to alleviate concerns about the sizable gap between the
foundational underpinnings of the Bayesian paradigm and the practice of
Bayesian statistics. In experimental work we show that human modeling has
predictive performance superior to that of many automatic modeling techniques,
including AIC, BIC, Smoothing Splines, CART, Bagged CART, Bayes CART, BMA and
LARS, and only slightly inferior to that of BART. We also show that Bayesian
Synthesis further improves predictive performance. Additionally, we examine the
predictive performance of a simple average across analysts, which we dub Convex
Synthesis, and find that it also produces an improvement.
