The Limits of Optimal Pricing in the Dark
Authors: Quinlan Dawkins, Minbiao Han, Haifeng Xu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We fully characterize the optimal imitative value function that the buyer should imitate as well as the resultant seller revenue and buyer surplus under this optimal buyer manipulation. Our characterizations reveal many useful insights about what happens at equilibrium. |
| Researcher Affiliation | Academia | Quinlan Dawkins Department of Computer Science University of Virginia qed4wg@virginia.edu Minbiao Han Department of Computer Science University of Virginia mh2ye@virginia.edu Haifeng Xu Department of Computer Science University of Virginia hx4ad@virginia.edu |
| Pseudocode | No | The paper contains mathematical formulations, theorems, and proofs, but no pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation, therefore no public dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data, thus there are no training, validation, or test dataset splits mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report empirical experiments, thus no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no details regarding experimental setup, hyperparameters, or training settings are provided. |