  We consider a multihypothesis social learning problem in which an agent has
access to a set of private observations and chooses an opinion from a set of
experts to incorporate into its final decision. To model individual biases, we
allow the agent and experts to have general loss functions and possibly
different decision spaces. We characterize the loss exponents of both the agent
and experts, and provide an asymptotically optimal method for the agent to
choose the best expert to follow. We show that up to asymptotic equivalence,
the worst loss exponent for the agent is achieved when it adopts the 0-1 loss
function, which assigns a loss of 0 if the true hypothesis is declared and a
loss of 1 otherwise. We introduce the concept of hypothesis-loss neutrality,
and show that if the agent adopts a particular policy that is hypothesis-loss
neutral, then it ignores all experts whose decision spaces are smaller than its
own. On the other hand, if experts have the same decision space as the agent,
then choosing an expert with the same loss function as itself is not
necessarily optimal for the agent, which is somewhat counter-intuitive. We
derive sufficient conditions for when it is optimal for the agent with 0-1 loss
function to choose an expert with the same loss function.
