Pricing Query Complexity of Revenue Maximization

Leme, Renato Paes; Sivan, Balasubramanian; Teng, Yifeng; Worah, Pratik

doi:10.1137/1.9781611977554.ch17

Cited by 4 publications

(1 citation statement)

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“…An auctioneer learns a winning distribution by adaptively choosing reserve prices. Although both of these settings are limited to binary feedback: observing whether the item is bought, the literature (Kleinberg and Leighton, 2003;Leme et al, 2021a;Blum et al, 2015;Leme et al, 2021b) in these contexts assume the distribution is fixed or slowly-changing and often while trying to minimize a notion of regret with respect to the best fixed price in hindsight. We don't make any assumptions about the distribution.…”

Section: Related Workmentioning

confidence: 99%

Non-Stochastic CDF Estimation Using Threshold Queries

Princewill¹,

Gupta²,

Kleinberg³

et al. 2023

Preprint

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Estimating the empirical distribution of a scalar-valued data set is a basic and fundamental task. In this paper, we tackle the problem of estimating an empirical distribution in a setting with two challenging features. First, the algorithm does not directly observe the data; instead, it only asks a limited number of threshold queries about each sample. Second, the data are not assumed to be independent and identically distributed; instead, we allow for an arbitrary process generating the samples, including an adaptive adversary. These considerations are relevant, for example, when modeling a seller experimenting with posted prices to estimate the distribution of consumers' willingness to pay for a product: offering a price and observing a consumer's purchase decision is equivalent to asking a single threshold query about their value, and the distribution of consumers' values may be non-stationary over time, as early adopters may differ markedly from late adopters.Our main result quantifies, to within a constant factor, the sample complexity of estimating the empirical CDF of a sequence of elements of [n], up to ε additive error, using one threshold query per sample. The complexity depends only logarithmically on n, and our result can be interpreted as extending the existing logarithmic-complexity results for noisy binary search to the more challenging setting where noise is non-stochastic. Along the way to designing our algorithm, we consider a more general model in which the algorithm is allowed to make a limited number of simultaneous threshold queries on each sample. We solve this problem using Blackwell's Approachability Theorem and the exponential weights method. As a side result of independent interest, we characterize the minimum number of simultaneous threshold queries required by deterministic CDF estimation algorithms.

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Section: Related Workmentioning

confidence: 99%