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..
Congestion Games with Distance-Based Strict Uncertainty
Authors: Reshef Meir, David Parkes
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We put forward a new model of congestion games... we model agents who either minimize their worst-case cost (WCC) or their worstcase regret (WCR), and study implications on equilibrium existence, convergence through adaptive play, and ef๏ฌciency. |
| Researcher Affiliation | Academia | Reshef Meir and David Parkes Harvard University |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block found in the paper. |
| Open Source Code | No | The paper does not provide any link or explicit statement about the availability of open-source code for the described methodology. It only mentions 'Due to space constraints most of our proofs are omitted, and are available in the full version of this paper.2http://arxiv.org/abs/1411.4943'. |
| Open Datasets | No | The paper is theoretical and does not use or refer to datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation using dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |