Strongly Budget Balanced Auctions for Multi-Sided Markets
Authors: Rica Gonen, Erel Segal-Halevi1998-2005
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Full version, including omitted proofs and simulation experiments, is available at https://arxiv.org/abs/1911.08094. An open-source implementation of our auctions, including example runs and experiments, is available at https://github.com/erelsgl/auctions. |
| Researcher Affiliation | Academia | Rica Gonen The Open University of Israel ricagonen@gmail.com Erel Segal-Halevi Ariel University, Ariel, Israel erelsgl@gmail.com |
| Pseudocode | No | The paper describes the steps of the auction mechanisms in a numbered list format, but it does not present them as formal pseudocode or in a clearly labeled algorithm block. |
| Open Source Code | Yes | An open-source implementation of our auctions, including example runs and experiments, is available at https://github.com/erelsgl/auctions. |
| Open Datasets | No | The paper mentions 'simulation experiments' and provides a link to an 'open-source implementation' including 'example runs and experiments', but it does not specify or provide access information for any publicly available or open dataset used in these simulations. |
| Dataset Splits | No | The paper describes simulation experiments but does not provide specific details on dataset splits (e.g., training, validation, or test percentages/counts) as it does not rely on traditional machine learning datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments or simulations. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, that were used for the experiments. |
| Experiment Setup | No | The paper focuses on theoretical exposition and proofs of auction mechanisms, and therefore does not provide specific experimental setup details like hyperparameter values or training configurations for empirical evaluation. |