Selling Partially-Ordered Items: Exploring the Space between Single- and Multi-Dimensional Mechanism Design

Series: 
ACO Student Seminar
Friday, April 20, 2018 - 1:05pm
1 hour (actually 50 minutes)
Location: 
Skiles 005
,  
CSE, University of Washington
,  
Organizer: 
Consider the problem of selling items to a unit-demand buyer. Most work on maximizing seller revenue considers either a setting that is single dimensional, such as where the items are identical, or multi-dimensional, where the items are heterogeneous. With respect to revenue-optimal mechanisms, these settings sit at extreme ends of a spectrum: from simple and fully characterized (single-dimensional) to complex and nebulous (multi-dimensional). In this paper, we identify a setting that sits in between these extremes. We consider a seller who has three services {A,B,C} for sale to a single buyer with a value v and an interest G from {A,B,C}, and there is a known partial ordering over the services. For example, suppose the seller is selling {internet}, {internet, phone}, and {internet, cable tv}. A buyer with interest {internet} would be satisfied by receiving phone or cable tv in addition, but a customer whose interest is {internet, phone} cannot be satisfied by any other option. Thus this corresponds to a partial-ordering where {internet} > {internet, phone} and {internet} > {internet, cable tv}, but {internet, phone} and {internet, cable tv} are not comparable. We show formally that partially-ordered items lie in a space of their own, in between identical and heterogeneous items: there exist distributions over (value, interest) pairs for three partially-ordered items such that the menu complexity of the optimal mechanism is unbounded, yet for all distributions there exists an optimal mechanism of finite menu complexity. So this setting is vastly more complex than identical items (where the menu complexity is one), or even “totally-ordered” items as in the FedEx Problem [FGKK16] (where the menu complexity is at most seven, for three items), yet drastically more structured than heterogeneous items (where the menu complexity can be uncountable [DDT15]). We achieve this result by proving a characterization of the class of best duals and by giving a primal recovery algorithm which obtains the optimal mechanism. In addition, we (1) extend our lower-bound to the Multi-Unit Pricing setting, (2) give a tighter and deterministic characterization of the optimal mechanism when the buyer’s distribution satisfies the declining marginal revenue condition, and (3) prove a master theorem that allows us to reason about duals instead of distributions. Joint work with Nikhil Devanur, Raghuvansh Saxena, Ariel Schvartzman, and Matt Weinberg.