Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Strongly Budget Balanced Auctions for Multi-Sided Markets
Authors: Rica Gonen, Erel Segal-Halevi1998-2005
AAAI 2020 | Venue PDF | 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 EMAIL Erel Segal-Halevi Ariel University, Ariel, Israel EMAIL |
| 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. |