On the Impossibility of Black-Box Truthfulness Without Priors
نویسندگان
چکیده
We consider the problem of converting an arbitrary approximation algorithm for a singleparameter social welfare problem into a computationally efficient incentive compatible 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. We demonstrate that, while such transformations are known to exist in Bayesian settings of partial information, any computationally efficient transformation that guarantees deterministic ex post incentive compatibility or universal truthfulness must sometimes incur a polynomially large loss in worst-case performance.
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