{"type":"ScholarlyArticle","title":"Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs","authors":[{"name":"Maria-Florina Balcan","slug":"maria-florina-balcan","given":"Maria-Florina","family":"Balcan","affiliation":"School of Computer Science , Carnegie Mellon University"},{"name":"Siddharth Prasad","slug":"siddharth-prasad","given":"Siddharth","family":"Prasad","affiliation":"School of Computer Science , Carnegie Mellon University"},{"name":"Tuomas Sandholm","slug":"tuomas-sandholm","given":"Tuomas","family":"Sandholm","affiliation":"School of Computer Science , Carnegie Mellon University; Optimized Markets, Inc.; Strategy Robot, Inc.; Strategic Machine, Inc."}],"abstract":"A two-part tariff is a pricing scheme that consists of an up-front lump sum fee and a per unit fee. Various products in the real world are sold via a menu, or list, of two-part tariffs---for example gym memberships, cell phone data plans, etc. We study learning high-revenue menus of two-part tariffs from buyer valuation data, in the setting where the mechanism designer has access to samples from the distribution over buyers' values  rather than an explicit description thereof. Our algorithms have clear direct uses, and provide the missing piece for the recent generalization theory of two-part tariffs. We present a polynomial time algorithm for optimizing one two-part tariff. We also present an algorithm for optimizing a length-L menu of two-part tariffs with run time exponential in L but polynomial in all other problem parameters. We then generalize the problem to multiple markets. We prove how many samples suffice to guarantee that a two-part tariff scheme that is feasible on the samples is also feasible on a new problem instance with high probability. We then show that computing revenue-maximizing feasible prices is hard even for buyers with additive valuations. Then, for buyers with identical valuation distributions, we present a condition that is sufficient for the two-part tariff scheme from the unsegmented setting to be optimal for the market-segmented setting. Finally, we prove a generalization result that states how many samples suffice so that we can compute the unsegmented solution on the samples and still be guaranteed that we get a near-optimal solution for the market-segmented setting with high probability.","doi":"10.24963/ijcai.2020/47","url":"https://ijcai.org/proceedings/2020/47","pdf":"https://ijcai.org/proceedings/2020/0047.pdf","markdown":"https://ijcai.org/proceedings/2020/47.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/47","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":47,"pages":{"first":332,"last":338},"keywords":["Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24356/file_24356.m3u8","length_sec":300,"provider":"hls"}],"references":[{"position":1,"raw":"[Armstrong and Vickers, 2001] Mark Armstrong and John Vickers. 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