We study the revenue maximization problem of a seller withnheterogeneous items for sale to a single buyer whose valuation function for sets of items is unknown and drawn from some distributionD. We show that ifDis a distribution over subadditive valuations with independent items, then the better of pricing each item separately or pricing only the grand bundle achieves a constant-factor approximation to the revenue of the optimal mechanism. This includes buyers who arek-demand, additive up to a matroid constraint, or additive up to constraints of any downward-closed set system (and whose values for the individual items are sampled independently), as well as buyers who are fractionally subadditive with item multipliers drawn independently. Our proof makes use of the core-tail decomposition framework developed in prior work showing similar results for the significantly simpler class of additive buyers. In the second part of the article, we develop a connection between approximately optimal simple mechanisms and approximate revenue monotonicity with respect to buyers’ valuations. Revenue non-monotonicity is the phenomenon that sometimes strictlyincreasingbuyers’ values for every set can strictlydecreasethe revenue of the optimal mechanism. Using our main result, we derive a bound on how bad this degradation can be (and dub such a bound a proof ofapproximaterevenue monotonicity); we further show that better bounds on approximate monotonicity imply a better analysis of our simple mechanisms.
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Rubinstein et al. (2018) studied this question.
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