  We consider a binary decision aggregation problem in the presence of both
truthful and adversarial experts. The truthful experts will report their
private signals truthfully with proper incentive, while the adversarial experts
can report arbitrarily. The decision maker needs to design a robust aggregator
to forecast the true state of the world based on the reports of experts. The
decision maker does not know the specific information structure, which is a
joint distribution of signals, states, and strategies of adversarial experts.
We want to find the optimal aggregator minimizing regret under the worst
information structure. The regret is defined by the difference in expected loss
between the aggregator and a benchmark who makes the optimal decision given the
joint distribution and reports of truthful experts.
  We prove that when the truthful experts are symmetric and adversarial experts
are not too numerous, the truncated mean is optimal, which means that we remove
some lowest reports and highest reports and take averaging among the left
reports. Moreover, for many settings, the optimal aggregators are in the family
of piecewise linear functions. The regret is independent of the total number of
experts but only depends on the ratio of adversaries. We evaluate our
aggregators by numerical experiment in an ensemble learning task. We also
obtain some negative results for the aggregation problem with adversarial
experts under some more general information structures and experts' report
space.
