  This paper examines how a decision maker might incentivize an expert to
reveal novel aspects of the decision problem, expanding the set of contracts
from which the decision maker can choose. The chosen contract will determine,
along with the resolution of uncertainty, the payoffs to both players. I show
that the set of achievable outcomes under any (incentive compatible) mechanism
is characterized by a small and tractable class of iterated revelation
mechanisms (IRMs). An IRM is a dynamic interaction wherein each round the
expert chooses to reveal some novel contingencies and the decision maker
proposes a contract that the expert can accept or reject; the IRM ends after
rejection or when nothing novel is revealed. I then consider the set of robust
IRMs -- those that maximize the worst case outcome across all types of expert
-- and show these are characterized by a principle of myopic optimality: at
each round, the decision maker maximizes his payoff as if the expert had
nothing further to reveal. The set of robust IRMs also delineate the payoffs
achievable by any efficient mechanism.
