Achieving Balanced Representation in School Choice with Diversity Goals
Authors: Zhaohong Sun, Makoto Yokoo
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper addresses the selection of students by a single school under a one-to-one convention... we introduce a new property called balanced representation, which ensures fair representation across all types and type combinations. We propose a straightforward choice function that uniquely satisfies four fundamental properties: maximal diversity, non-wastefulness, justified envy-freeness, and balanced representation... Additionally, we present efficient algorithms for implementing our choice function within both the bipartite graph and flow network frameworks. |
| Researcher Affiliation | Collaboration | Zhaohong Sun1, 2, Makoto Yokoo1 1Kyushu University, Japan 2AI Lab, Cyber Agent, Japan |
| Pseudocode | Yes | Algorithm 1: Maximum and Balanced Choice Function Algorithm 2: Checking Whether an Instance is Valid Algorithm 3: Implementation of the Choice Function in Algorithm 1 Based on Rank-maximal Matching Algorithm 4: Computing a Crucial Vector Algorithm 5: Implementation of the Choice Function in Algorithm 1 Based on Flow Network |
| Open Source Code | No | The paper mentions: "The complete version of this paper is accessible at https://arxiv.org/abs/2412.13622." This link points to the full paper, not to source code. There is no explicit statement about releasing code for the described methodology, nor is there a direct link to a code repository. |
| Open Datasets | No | The paper discusses concepts in school choice programs in contexts like "Israeli gap year programs", "college admissions in India", "school choice in Chile", and "Brazilian college admissions". However, it does not refer to any specific public datasets that were used for experiments or evaluation. The examples provided (Example 1, Example 2) are illustrative rather than based on concrete, publicly accessible data. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving datasets, therefore, there is no mention of dataset splits. |
| Hardware Specification | No | The paper is theoretical, focusing on algorithm design and properties. It does not describe any experimental setup or the hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical modeling and algorithm design. It does not mention any specific software dependencies or versions required to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and properties. It does not provide details about experimental setup, hyperparameters, or training configurations. |