Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Do Capacity Constraints Constrain Coalitions?
Authors: Michal Feldman, Ofir Geri
AAAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,917 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,027 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study strong equilibria in symmetric capacitated costsharing games. Our contribution is two-fold. First, we provide a topological characterization of networks that always admit a strong equilibrium. Second, we establish tight bounds on the efficiency loss that may be incurred due to strategic behavior, as quantified by the strong price of anarchy (and stability) measures. The paper focuses on theoretical analysis, characterization, and establishment of bounds, including theorems and proofs, rather than empirical evaluation or data analysis. |
| Researcher Affiliation | Academia | Michal Feldman Tel Aviv University EMAIL Ofir Geri Tel Aviv University EMAIL |
| Pseudocode | Yes | Algorithm 1 Compute a SE for a network of parallel edges. Algorithm 2 Choosing the optimal profile s |
| Open Source Code | No | The paper does not provide an unambiguous statement or a direct link for accessing the source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training or empirical evaluation. Therefore, no information on public dataset access is relevant or provided. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical evaluation with dataset splits. Therefore, no information on training/validation/test splits is relevant or provided. |
| Hardware Specification | No | This is a theoretical paper and does not involve empirical experiments requiring specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | This is a theoretical paper primarily focused on mathematical and algorithmic analysis, not on software implementation or empirical execution. Therefore, specific software dependencies with version numbers are not mentioned. |
| Experiment Setup | No | This is a theoretical paper and does not involve empirical experiments with specific hyperparameters or system-level training settings. Therefore, no experimental setup details are provided. |