Robust Pricing in Non-Clairvoyant Dynamic Mechanism Design

26 Pages Posted: 19 Jun 2019

Date Written: June 11, 2019

Abstract

Dynamic mechanisms offer powerful techniques to improve on both revenue and efficiency by linking sequential auctions using state information, but these techniques rely on exact distributional information of the buyers’ valuations (present and future), which limits their use in learning settings. In this paper, we consider the problem of contextual auctions where the seller gradually learns a model of the buyer's valuation as a function of the context (e.g., item features) and seeks a pricing policy that optimizes revenue. Building on the concept of a bank account mechanism – a special class of dynamic mechanisms that is known to be revenue-optimal – we develop a non-clairvoyant dynamic mechanism that is robust to both estimation errors in the buyer's value distribution and strategic behavior on the part of the buyer. We then tailor its structure to achieve a policy with provably low regret against a constant approximation of the optimal dynamic mechanism in contextual auctions. Our result substantially improves on previous results that only provide revenue guarantees against static benchmarks.

Keywords: Dynamic Pricing; Non-clairvoyance; Dynamic Mechanism Design

JEL Classification: D44; D82; C73

Suggested Citation

Deng, Yuan and Lahaie, Sebastien and Mirrokni, Vahab, Robust Pricing in Non-Clairvoyant Dynamic Mechanism Design (June 11, 2019). Available at SSRN: https://ssrn.com/abstract=3402204 or http://dx.doi.org/10.2139/ssrn.3402204

Yuan Deng (Contact Author)

Duke University ( email )

100 Fuqua Drive
Durham, NC 27708-0204
United States

Sebastien Lahaie

Google Research

111 8th Ave
New York, NY 10011
United States

Vahab Mirrokni

Google Inc. ( email )

1600 Amphitheatre Parkway
Second Floor
Mountain View, CA 94043
United States

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