A single seller wishes to sell n items to a single unit-demand buyer. We consider a robust versionof this revenue-maximization pricing problem, where the seller knows the buyer's marginal distributions of values for each item  , but not the joint distribution, and wishes to maximize worst-case revenue over all possible correlation structures. We devise a computationally efficient (polynomial in the support size of the marginals) algorithm that computes the worst-case joint distribution for any choice of item prices. And yet, in sharp contrast to the additive buyer case (Carroll, 2017), we show that it is NP-hard to approximate the optimal choice of prices to within any factor better than n^{1/2-\epsilon}. For the special case of marginal distributions that satisfy the monotone hazard rate property, we show how to guarantee a constant fraction of the optimal worst-case revenue using item pricing; this pricing equates revenue across all possible correlations and can be computed efficiently.
We consider a robust version of the revenue maximization problem, where a single seller wishes to sell n items to a single unit-demand buyer. In this robust version, the seller knows the buyer's marginal value distribution for each item separately , but not the joint distribution, and prices the items to maximize revenue in the worst case over all compatible correlation structures. We devise a computationally efficient (polynomial in the support size of the marginals) algorithm that computes the worst-case joint distribution for any choice of item prices. And yet, in sharp contrast to the additive buyer case (Carroll, 2017), we show that it is NP-hard to approximate the optimal choice of prices to within any factor better than n^{1/2-\epsilon}. For the special case of marginal distributions that satisfy the monotone hazard rate property, we show how to guarantee a constant fraction of the optimal worst-case revenue using item pricing; this pricing equates revenue across all possible correlations and can be computed efficiently.
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We consider a robust version of the revenue maximization problem, where a
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We consider a robust versionof this revenue-maximization pricing problem, where
In this robust version,
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distributions of values
value distribution
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separately
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wishes to maximize worst-case revenue over all possible
prices the items to maximize revenue in the worst case over all compatible