Posted Pricing sans Discrimination
Authors: Shreyas Sekar
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is a framework for static item pricing in the face of production costs, i.e., the seller posts one price per good and buyers arrive sequentially and purchase utility-maximizing bundles. The framework yields constant factor approximations to the optimum welfare when buyer valuations are fractionally subadditive, and extends to settings where the seller is completely oblivious to buyer valuations. Our results indicate that even in markets with complex buyer valuations and nonlinear costs, it is possible to obtain good guarantees without price discrimination, i.e., charging buyers differently for the same good. |
| Researcher Affiliation | Academia | Shreyas Sekar Rensselaer Polytechnic Institute, Troy, NY-12180 shreyas.sekar@gmail.com |
| Pseudocode | Yes | Algorithm 1 Allocation Algorithm for Xo S buyers |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on mechanism design and approximation algorithms. It does not describe experiments using publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |