  Bayesian experts who are exposed to different evidence often make
contradictory probabilistic forecasts. An aggregator, ignorant of the
underlying model, uses this to calculate her own forecast. We use the notions
of scoring rules and regret to propose a natural way to evaluate an aggregation
scheme. We focus on a binary state space and construct low regret aggregation
schemes whenever there are only two experts which are either Blackwell-ordered
or receive conditionally i.i.d. signals. In contrast, if there are many experts
with conditionally i.i.d. signals, then no scheme performs (asymptotically)
better than a $(0.5,0.5)$ forecast.
