Jianyu Xu, Dan Qiao, Yu-Xiang Wang
arXiv 23 Sep 2022 · Machine Learning · 1 citations (OpenAlex)
arXiv:2209.11837 · PDF · DOI · OpenAlex · Extracted main text
We study the problem of online dynamic pricing with two types of fairness constraints: a "procedural fairness" which requires the proposed prices to be equal in expectation among different groups, and a "substantive fairness" which requires the accepted prices to be equal in expectation among different groups. A policy that is simultaneously procedural and substantive fair is referred to as "doubly fair". We show that a doubly fair policy must be random to have higher revenue than the best trivial policy that assigns the same price to different groups. In a two-group setting, we propose an online learning algorithm for the 2-group pricing problems that achieves $\tilde{O}(\sqrt{T})$ regret, zero procedural unfairness and $\tilde{O}(\sqrt{T})$ substantive unfairness over $T$ rounds of learning. We also prove two lower bounds showing that these results on regret and unfairness are both information-theoretically optimal up to iterated logarithmic factors. To the best of our knowledge, this is the first dynamic pricing algorithm that learns to price while satisfying two fairness constraints at the same time.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kleinberg, R. and Leighton, T (2003) The value of knowing a demand curve: Bounds on regret for online posted-price auctions | 0.855 | 8 | 4 | 62% |
| 2 | Cohen, M. C., Elmachtoub, A. N., and Lei, X (2022) Price discrimination with fairness constraints | 0.811 | 4 | 2 | 100% |
| 3 | Chapuis, J. M (2012) Price fairness versus pricing fairness | 0.737 | 4 | 4 | 50% |
| 4 | Eyster, E., Madarász, K., and Michaillat, P (2021) Pricing under fairness concerns | 0.737 | 3 | 3 | 67% |
| 5 | Richards, T. J., Liaukonyte, J., and Streletskaya, N. A (2016) Personalized pricing and price fairness | 0.737 | 3 | 3 | 67% |
| 6 | Wang, Y., Chen, B., and Simchi-Levi, D (2021) Multimodal dynamic pricing self | 0.644 | 4 | 2 | 50% |
| 7 | Chen, X., Zhang, X., and Zhou, Y (2021) Fairness-aware online price discrimination with nonparametric demand models | 0.644 | 3 | 2 | 67% |
| 8 | Auer, P., Cesa-Bianchi, N., Freund, Y., and Schapire, R. E (2002) The nonstochastic multiarmed bandit problem | 0.644 | 2 | 2 | 100% |
| 9 | Kaufmann, P. J., Ortmeyer, G., and Smith, N. C (1991) Fairness in consumer pricing | 0.644 | 2 | 2 | 100% |
| 10 | Javanmard, A. and Nazerzadeh, H (2019) Dynamic pricing in high-dimensions | 0.511 | 4 | 2 | 25% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Pricing with Contextual Elasticity and Heteroscedastic Valuation | 0.000 | 1 | 1 |
| 2 | Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand | 0.000 | 1 | 1 |