Mechanism Design for School Choice with Soft Diversity Constraints
Authors: Haris Aziz, Serge Gaspers, Zhaohong Sun
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally compare our algorithms with two existing approaches in terms of achieving diversity goals and satisfying fairness.Our generated data uses similar features as the private data set used by Gonczarowski et al. [2019]. We show that our new algorithm performs well across several axes, including fairness, diversity goals as well as running time. |
| Researcher Affiliation | Academia | UNSW Sydney, Australia 2Data61 CSIRO, Australia |
| Pseudocode | Yes | Algorithm 1 Generalized Deferred Acceptance (GDA) Input: A set of contracts X X, Ch S, Ch C Output: An outcome Y X; Algorithm 2 GDA-TC Input: IT =(S, C, q C, T, η, X, S, C) Output: An outcome X X; Algorithm 3 Choice function Ch T C c of school c Input: An instance IT , quotas δ for U, a set of contracts X Output: A set of contracts Y X |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | Our generated data uses similar features as the private data set used by Gonczarowski et al. [2019]. |
| Dataset Splits | No | The paper generates 100 instances for each setting but does not specify training, validation, and test splits for a dataset. It generates full instances for experimental evaluation. |
| Hardware Specification | No | The paper mentions running time performance but does not provide any specific hardware details used for the experiments. |
| Software Dependencies | No | The paper mentions linear programming (LP 1) but does not specify any software names with version numbers for ancillary software. |
| Experiment Setup | Yes | Setup of Experiments We consider a market with |S| = 2000, |C| = 40 and qc = 50, which are close to the number of students, the number of schools and the average number of slots at each institution in the Mechinot market [Gonczarowski et al., 2019]. The number of types is |T| {2, 4, 6, 8}. For a given type t, its percentage per(t) is determined by the number of students |St| associated with type t divided by the total number of students |S|. The percentage of each type is randomly chosen from the set {0.1, 0.2, 0.3, 0.4, 0.5}. The same minimum vector ηc is imposed on all schools. The minimum target ηt c for type t is set as ηt c = |St|/|C| α where α is the target ratio. The target ratio α takes two values from {1.0, 1.3}. The preference profile of students and the priority profile of schools are generated by Mallows Model (MM). Let Φ be the set of all possible preference orders. MM is a distribution over permutations of Φ determined by two parameters, a reference order σ Φ and a dispersion parameter θ (0, 1] [Lu and Boutilier, 2011]. We generate 100 instances for each setting and compute the average results. Because the relative performances of the three algorithms are consistent, we present the results for target ratio α = 1.3 with two dispersion parameters θ = 0.1 and 0.9, as shown in Figure 2 and Figure 3 respectively. |