Sample Complexity of Posted Pricing for a Single Item
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Abstract (arXiv)
Selling a single item to n self-interested buyers is a fundamental problem in economics, where the two objectives typically considered are welfare maximization and revenue maximization. Since the optimal mechanisms are often impractical and do not work for sequential buyers, posted pricing mechanisms, where fixed prices are set for the item for different buyers, have emerged as a practical and effective alternative. This paper investigates how many samples are needed from buyers' value distributions to find near-optimal posted prices, considering both independent and correlated buyer distributions, and welfare versus revenue maximization. We obtain matching upper and lower bounds (up to logarithmic factors) on the sample complexity for all these settings.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Jun 2024.
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Citing papers per year: 2024: 1; 2025: 7; 2026: 3.
11 papers.
- 2025Improved Regret and Contextual Linear Extension for Pandora's Box and Prophet Inequalitycited by 5
- 2025Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Taskscited by 2
- 2025Algorithm Design and Stronger Guarantees for the Improving Multi-Armed Bandits Problemcited by 2
- 2024Survey of Data-driven Newsvendor: Unified Analysis and Spectrum of Achievable Regretscited by 1
- 2025Learning Optimal Posted Prices for a Unit-Demand Buyercited by 1
- 2025Online Algorithms for Repeated Optimal Stopping: Balancing Baseline Guarantees and Regretcited by 1
- 2025Gradient Descent with Provably Tuned Learning-rate Schedulescited by 1
- 2025Contextual Learning for Stochastic Optimizationcited by 0
- 2026Pricing Query Complexity of Multiplicative Revenue Approximationcited by 0
- 2026Optimal No-Regret Learning for Repeated Prophet Inequalitycited by 0
- 2026Pessimistic Minimax Learning for Public-Private Information Games under Unilateral Coveragecited by 0
- 2026Pessimistic Minimax Learning for Public-Private Information Games under Unilateral Coverage4 Oct 2026
- 2026Optimal No-Regret Learning for Repeated Prophet Inequality20 Sep 2026
- 2026Pricing Query Complexity of Multiplicative Revenue Approximation11 Feb 2026
- 2025Gradient Descent with Provably Tuned Learning-rate Schedules4 Dec 2025
- 2025Algorithm Design and Stronger Guarantees for the Improving Multi-Armed Bandits Problem13 Nov 2025
- 2025Online Algorithms for Repeated Optimal Stopping: Balancing Baseline Guarantees and Regret6 Nov 2025
- 2025Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks7 Jul 2025
- 2025Learning Optimal Posted Prices for a Unit-Demand Buyer2 Jun 2025
- 2025Improved Regret and Contextual Linear Extension for Pandora's Box and Prophet Inequality24 May 2025
- 2025Contextual Learning for Stochastic Optimization22 May 2025
- 2024Survey of Data-driven Newsvendor: Unified Analysis and Spectrum of Achievable Regrets5 Sep 2024