Pricing Randomized Allocations

TL;DR

This paper explores how randomized pricing mechanisms, or lotteries, can increase revenue in selling heterogeneous items to unit-demand bidders, revealing computational advantages and limitations depending on purchase models.

Contribution

It introduces polynomial-time algorithms for envy-free lottery pricing in the buy-one model and establishes bounds on revenue gains and computational hardness in the buy-many model.

Findings

Lottery pricing can outperform item pricing significantly in certain models.

Efficient algorithms exist for the buy-one model, overcoming previous inapproximability.

Revenue gains are bounded in the buy-many model, with hardness results indicating computational limits.

Abstract

Randomized mechanisms, which map a set of bids to a probability distribution over outcomes rather than a single outcome, are an important but ill-understood area of computational mechanism design. We investigate the role of randomized outcomes (henceforth, "lotteries") in the context of a fundamental and archetypical multi-parameter mechanism design problem: selling heterogeneous items to unit-demand bidders. To what extent can a seller improve her revenue by pricing lotteries rather than items, and does this modification of the problem affect its computational tractability? Our results show that the answers to these questions hinge on whether consumers can purchase only one lottery (the buy-one model) or purchase any set of lotteries and receive an independent sample from each (the buy-many model). In the buy-one model, there is a polynomial-time algorithm to compute the revenue-maximizing envy-free prices (thus overcoming the inapproximability of the corresponding item pricing problem) and the revenue of the optimal lottery system can exceed the revenue of the optimal item pricing by an unbounded factor as long as the number of item types exceeds 4. In the buy-many model with n item types, the profit achieved by lottery pricing can exceed item pricing by a factor of O(log n) but not more, and optimal lottery pricing cannot be approximated within a factor of O(n^eps) for some eps>0, unless NP has subexponential-time randomized algorithms. Our lower bounds rely on a mixture of geometric and algebraic techniques, whereas the upper bounds use a novel rounding scheme to transform a mechanism with randomized outcomes into one with deterministic outcomes while losing only a bounded amount of revenue.