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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Towards Structural Tractability in Hedonic Games
Authors: Dominik Peters
AAAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 9,076 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,699 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 investigate a structural way of achieving tractability, by requiring that agents preferences interact in a well-behaved manner. Precisely, we show that stable outcomes can be found in linear time for hedonic games that satisfy a notion of bounded treewidth and bounded degree. Theorem 2 (Peters 2016b). For any logical sentence φ of HG-logic that may quantify over (connected) partitions, coalitions, and agents, the problem of deciding whether a hedonic game given by an HC-net satisfies φ is fixed-parameter tractable with parameters the treewidth and degree of the dependency graph of the game. Proof idea. Encode HG-logic into monadic second-order logic, and use Courcelle s theorem. |
| Researcher Affiliation | Academia | Dominik Peters Department of Computer Science University of Oxford, UK EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe the use of specific datasets with public access information. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe a concrete experimental setup with hyperparameters or training configurations. |