  Mixability is a property of a loss which characterizes when fast convergence
is possible in the game of prediction with expert advice. We show that a key
property of mixability generalizes, and the exp and log operations present in
the usual theory are not as special as one might have thought. In doing this we
introduce a more general notion of $\Phi$-mixability where $\Phi$ is a general
entropy (\ie, any convex function on probabilities). We show how a property
shared by the convex dual of any such entropy yields a natural algorithm (the
minimizer of a regret bound) which, analogous to the classical aggregating
algorithm, is guaranteed a constant regret when used with $\Phi$-mixable
losses. We characterize precisely which $\Phi$ have $\Phi$-mixable losses and
put forward a number of conjectures about the optimality and relationships
between different choices of entropy.
