  We consider the problem of converting an arbitrary approximation algorithm
for a single-parameter optimization problem into a computationally efficient
truthful mechanism. We ask for reductions that are black-box, meaning that they
require only oracle access to the given algorithm and in particular do not
require explicit knowledge of the problem constraints. Such a reduction is
known to be possible, for example, for the social welfare objective when the
goal is to achieve Bayesian truthfulness and preserve social welfare in
expectation. We show that a black-box reduction for the social welfare
objective is not possible if the resulting mechanism is required to be truthful
in expectation and to preserve the worst-case approximation ratio of the
algorithm to within a subpolynomial factor. Further, we prove that for other
objectives such as makespan, no black-box reduction is possible even if we only
require Bayesian truthfulness and an average-case performance guarantee.
