Coalition Manipulation of Gale-Shapley Algorithm

Authors: Weiran Shen, Pingzhong Tang, Yuan Deng

AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 ericdy@cs.duke.edu Weiran Shen, Pingzhong Tang Institute for Interdisciplinary Information Sciences Tsinghua University Beijing, China {emersonswr,kenshinping}@gmail.com
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.