Robust Pricing in Dynamic Mechanism Design
Authors: Yuan Deng, Sebastien Lahaie, Vahab Mirrokni
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose robust dynamic mechanism design. We develop a new framework to design dynamic mechanisms that are robust to both estimation errors in value distributions and strategic behavior. We apply the framework in learning environments, leading to the first policy that achieves provably low regret against the optimal dynamic mechanism in contextual auctions, where the dynamic benchmark has full and accurate distributional information. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Duke University, Durham, NC, USA 2Google Research, New York City, NY, USA. |
| Pseudocode | No | The paper describes mathematical programs and theoretical concepts but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on mechanism design and regret bounds. It describes models like "contextual auctions, where a buyer s valuation for an item depends on the context that describes the item," and refers to "feature vectors ζt Rd" and "noise term nt", but does not use or provide access to any specific, publicly available dataset for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, thus no training, validation, or test dataset splits are specified. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any specific experimental setup details such as hyperparameters or training configurations. |