  Teaching how to derive minimax decision rules can be challenging because of
the lack of examples that are simple enough to be used in the classroom.
Motivated by this challenge, we provide a new example that illustrates the use
of standard techniques in the derivation of optimal decision rules under the
Bayes and minimax approaches. We discuss how to predict the value of an unknown
quantity, $\theta \! \in \! \{0,1\}$, given the opinions of $n$ experts. An
important example of such crowdsourcing problem occurs in modern cosmology,
where $\theta$ indicates whether a given galaxy is merging or not, and $Y_1,
\ldots, Y_n$ are the opinions from $n$ astronomers regarding $\theta$. We use
the obtained prediction rules to discuss advantages and disadvantages of the
Bayes and minimax approaches to decision theory. The material presented here is
intended to be taught to first-year graduate students.
