Prior-Free Dynamic Auctions with Low Regret Buyers
Authors: Yuan Deng, Jon Schneider, Balasubramanian Sivan
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that even in this prior-free setting, it is possible to extract a (1 ε)approximation of the full economic surplus for any ε > 0. The number of options offered to a buyer in any round scales independently of the number of rounds T and polynomially in ε. We show that this is optimal up to a polynomial factor; any mechanism achieving this approximation factor, even when values are drawn stochastically, requires at least Ω(1/ε) options. |
| Researcher Affiliation | Collaboration | Yuan Deng Duke University ericdy@cs.duke.edu Jon Schneider Google Research jschnei@google.com Balasubramanian Sivan Google Research balusivan@google.com |
| Pseudocode | Yes | Table 1: Construction of the i-th option |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not use or refer to publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits (training, validation, test) for empirical evaluation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments requiring specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments requiring specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe a specific experimental setup with concrete hyperparameter values or training configurations. |