School Choice with Flexible Diversity Goals and Specialized Seats
Authors: Haris Aziz, Zhaohong Sun
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a new and rich model of school choice with flexible diversity goals and specialized seats. Our method of expressing flexible diversity goals is also applicable to other settings in moral multiagent decision making where competing policies need to be balanced when allocating scarce resources. For our matching model, we present a polynomial-time algorithm that satisfies desirable properties, including strategyproofness and stability under several natural subdomains of our problem. We complement the results by providing a clear understanding about what results do not extend when considering the general model. |
| Researcher Affiliation | Academia | Haris Aziz , Zhaohong Sun UNSW Sydney {haris.aziz, zhaohong.sun}@unsw.edu.au |
| Pseudocode | Yes | Algorithm 1 Choice function Chb of school b, Algorithm 2 Generalized Deferred Acceptance (GDA), Algorithm 3 Sequential Allocation Under Dynamic Diversity Goals, Algorithm 4 Modified GDA-FD |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and uses illustrative examples (e.g., Example 1, Example 2) rather than experimental data. Therefore, it does not mention or provide access to a publicly available training dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation on datasets with splits. There is no mention of validation sets or related information. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and properties, not implementation details or software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and describes algorithms and models, but does not include details about an experimental setup, hyperparameters, or system-level training settings. |