Prior-independent Dynamic Auctions for a Value-maximizing Buyer
Authors: Yuan Deng, Hanrui Zhang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a novel dynamic mechanism achieving O(T 2/3) regret against a strategic automated bidder, where T is the time horizon. Our dynamic mechanism adopts the explore-and-exploit scheme by first estimating the bidder s value distribution. We then offer a robust version of the single-stage revenue-optimal mechanism to extract the revenue. A cornerstone of our dynamic mechanism in the exploration phase is a novel prior-independent single-stage mechanism that is incentive-compatible for automated bidders. This mechanism forces the bidder to make a trade-off between her future gain from misreporting her values and her immediate loss, leading to a Wasserstein distance bound on the magnitude of misreporting from the automated bidder which is precisely the form of error that our exploitation mechanisms are robust against. |
| Researcher Affiliation | Collaboration | Yuan Deng Google Research dengyuan@google.com Hanrui Zhang Duke University hrzhang@cs.duke.edu |
| Pseudocode | Yes | Figure 1: A prior-indepentent no-regret mechanism for a value-maximizing buyer. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any open-source code for the described methodology. |
| Open Datasets | No | The paper does not use a publicly available or open dataset. It defines a theoretical 'value distribution D ([0, 1])' for its analysis. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with training, validation, and test splits. |
| Hardware Specification | No | The paper describes a theoretical mechanism and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper describes a theoretical mechanism and does not mention any software dependencies with version numbers. |
| Experiment Setup | No | The paper describes a theoretical mechanism and does not provide details about an experimental setup, hyperparameters, or training settings. |