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Title: Pricing ordered items
We study the revenue guarantees and approximability of item pricing. Recent work shows that with n heterogeneous items, item-pricing guarantees an O(logn) approximation to the optimal revenue achievable by any (buy-many) mechanism, even when buyers have arbitrarily combinatorial valuations. However, finding good item prices is challenging – it is known that even under unit-demand valuations, it is NP-hard to find item prices that approximate the revenue of the optimal item pricing better than O(√n). Our work provides a more fine-grained analysis of the revenue guarantees and computational complexity in terms of the number of item “categories” which may be significantly fewer than n. We assume the items are partitioned in k categories so that items within a category are totally-ordered and a buyer’s value for a bundle depends only on the best item contained from every category. We show that item-pricing guarantees an O(logk) approximation to the optimal (buy-many) revenue and provide a PTAS for computing the optimal item-pricing when k is constant. We also provide a matching lower bound showing that the problem is (strongly) NP-hard even when k=1. Our results naturally extend to the case where items are only partially ordered, in which case the revenue guarantees and computational complexity depend on the width of the partial ordering, i.e. the largest set for which no two items are comparable.  more » « less
Award ID(s):
2225259 2144298 2008006
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing
Page Range / eLocation ID:
722 to 735
Medium: X
Sponsoring Org:
National Science Foundation
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