mlsanityData to 14 Oct 2026

On the limitations of data-based price discrimination

arXiv 2204.12723 ↗First posted 27 Apr 2022cs.GT cs.IT cs.LGcited by 0

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Abstract (arXiv)

The classic third degree price discrimination (3PD) model requires the knowledge of the distribution of buyer valuations and the covariate to set the price conditioned on the covariate. In terms of generating revenue, the classic result shows that 3PD is at least as good as uniform pricing. What if the seller has to set a price based only on a sample of observations from the underlying distribution? Is it still obvious that the seller should engage in 3PD? This paper sheds light on these fundamental questions. In particular, the comparison of the revenue performance between 3PD and uniform pricing is ambiguous overall when prices are set based on samples. This finding is in the nature of statistical learning under uncertainty: a curse of dimensionality, but also other small sample complications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 34 references that are in the map. First posted on arXiv 27 Apr 2022.

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8 of 34 references.

References · 8

  1. 2015The Sample Complexity of Revenue Maximizationcited by 48
  2. 2019Settling the Sample Complexity of Single-parameter Revenue Maximizationcited by 24
  3. 2015The Sample Complexity of Auctions with Side Informationcited by 20
  4. 2014Making the Most of Your Samplescited by 15
  5. 2014Learning Economic Parameters from Revealed Preferencescited by 8
  6. 2015Randomization beats Second Price as a Prior-Independent Auctioncited by 5
  7. 2021Optimal Pricing with a Single Pointcited by 5
  8. 2018Are Two (Samples) Really Better Than One? On the Non-Asymptotic Performance of Empirical Revenue Maximizationcited by 4

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