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..
Coalition Manipulation of Gale-Shapley Algorithm
Authors: Weiran Shen, Pingzhong Tang, Yuan Deng
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we consider manipulations by any subset of women with arbitrary preferences. We show that a strong Nash equilibrium of the induced manipulation game always exists among the manipulators and the equilibrium outcome is unique and Pareto-dominant. In addition, the set of matchings achievable by manipulations has a lattice structure. We also examine the super-strong Nash equilibrium in the end. |
| Researcher Affiliation | Academia | Yuan Deng Department of Computer Science Duke University Durham, NC 27708, USA EMAIL Weiran Shen, Pingzhong Tang Institute for Interdisciplinary Information Sciences Tsinghua University Beijing, China EMAIL |
| Pseudocode | No | The paper describes the steps of an algorithm based on Theorem 7, but it does not present it in a structured pseudocode block or a clearly labeled algorithm section. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the methodology or analysis described, nor does it include a link to a code repository. |
| Open Datasets | No | The paper focuses on theoretical analysis and uses illustrative examples with small preference lists, not publicly available datasets for training empirical models. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets with explicit training, validation, or testing splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments, thus it does not provide hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational implementation details or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and analyses, thus it does not include details on experimental setup, hyperparameters, or training configurations. |