Accomplice Manipulation of the Deferred Acceptance Algorithm
Authors: Hadi Hosseini, Fatima Umar, Rohit Vaish
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the experimental front, we work with preferences generated uniformly at random, and find that accomplice manipulation outperforms self manipulation with respect to the frequency of occurrence, the quality of matched partners, and the fraction of women who can improve their matches (Section 6). |
| Researcher Affiliation | Academia | Hadi Hosseini1 , Fatima Umar2 and Rohit Vaish3 1Pennsylvania State University 2Rochester Institute of Technology 3Tata Institute of Fundamental Research hadi@psu.edu, fu1476@rit.edu, rohit.vaish@tifr.res.in |
| Pseudocode | No | The paper describes the Deferred Acceptance algorithm and various theoretical results, but it does not include any pseudocode or algorithm blocks formatted with structured steps. |
| Open Source Code | No | The paper does not contain any explicit statements about the release of source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic data ("generate 1000 preference profiles uniformly at random") but does not provide concrete access information (link, DOI, repository, or citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes simulating data ("generate 1000 preference profiles uniformly at random") but does not provide specific details on how this data is split into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers used in the experiments. |
| Experiment Setup | Yes | We simulate a two-sided matching scenario for an increasingly larger set of agents (specifically, n {3, . . . , 40}, where n is the number of men/women) and for each setting, generate 1000 preference profiles uniformly at random. |