Research map / Bandits and online learning / Online convex optimization and games
Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Transform PRRFES with pre so offered prices never decrease | PRRFES · Repeated posted-price auctions with a strategic buyer | strategic regret growth: \Theta(\log\log T) | Helped |
| Apply prePRRFES to a myopic (truthful) buyer instead of a strategic buyer (ablation) | prePRRFES · Repeated posted-price auctions with a myopic buyer | regret growth: linear regret | Hurt |
| Optimize κ rather than use κ = 1 (ablation) | PRRFES · Geometric discounting with γ=0.05 | regret-bound factor reduction: 33.2% | Helped |
| Optimize κ rather than use κ = 1 (ablation) | PRRFES · Geometric discounting with γ=0.25 | regret-bound factor reduction: 22.9% | Helped |
| Optimize κ rather than use κ = 1 (ablation) | PRRFES · Geometric discounting with γ=0.75 | regret-bound factor reduction: 1.5% | Helped |
| Optimize κ rather than use κ = 1 (ablation) | PRRFES · Geometric discounting with γ=0.95 | regret-bound factor reduction: 2.8% | Helped |
| Optimize κ rather than use κ = 1 (ablation) | PRRFES · Geometric discounting with γ=0.99 | regret-bound factor reduction: 6.3% | Helped |
| Allow a weakly consistent algorithm to decrease prices twice along a path (ablation) | Horizon-independent weakly consistent pricing algorithms · Repeated posted-price auctions with a strategic buyer | strategic regret: \Omega(T) | Hurt |
| Use geometrically concave decreasing discount sequences rather than restricting to geometric discounting | PRRFES · Repeated posted-price auctions with a strategic buyer | strategic regret growth: \Theta(\log\log T) | Helped |
Closest papers by meaning
- Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer (NeurIPS 2019 · similarity 0.93)
- Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders (ICML 2020 · similarity 0.93)
- Learning Safe Strategies for Value Maximizing Buyers in Uniform Price Auctions (ICML 2025 · similarity 0.89)
- Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization (NeurIPS 2025 · similarity 0.88)
- Sample Complexity of Posted Pricing for a Single Item (NeurIPS 2024 · similarity 0.88)
- Improved Algorithms for Contextual Dynamic Pricing (NeurIPS 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.