Strategyproof and Fair Matching Mechanism for Union of Symmetric M-convex Constraints
Authors: Yuzhe Zhang, Kentaro Yahiro, Nathanaël Barrot, Makoto Yokoo
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extend these results by conducting a computer simulation with the difference constraints (Def. 12) to quantitatively examine the weak domination of QRDA over ACDA and show that QRDA outperforms ACDA in terms of nonwastefulness. We considered a market with n = 800 students and m = 20 schools and generated student preferences with the Mallows model [Tubbs, 1992; Lu and Boutilier, 2014; Drummond and Boutilier, 2013]. We created 100 problem instances for each parameter setting. In Fig. 1 (a), we show the proportion of students who strictly prefer QRDA over ACDA depending on the allowed difference d in Def. 12. |
| Researcher Affiliation | Academia | Yuzhe Zhang1, Kentaro Yahiro1, Nathana el Barrot2,1, Makoto Yokoo1,2 1 Kyushu University 2 RIKEN, Center for Advanced Intelligence Project AIP |
| Pseudocode | Yes | Mechanism 1 (standard DA). Mechanism 2 (QRDA). Mechanism 3 (ACDA (based on a most balanced vector)). |
| Open Source Code | No | The paper does not provide any concrete access information (link or explicit statement) to the source code for the methodology described. |
| Open Datasets | No | We considered a market with n = 800 students and m = 20 schools and generated student preferences with the Mallows model [Tubbs, 1992; Lu and Boutilier, 2014; Drummond and Boutilier, 2013]. While the generation method is cited, the specific generated dataset instances used for the experiments are not publicly available or provided with concrete access information. |
| Dataset Splits | No | The paper describes generating '100 problem instances for each parameter setting' but does not specify any training, validation, or test dataset splits in the context of model training and evaluation. |
| Hardware Specification | No | The paper mentions 'conducting a computer simulation' but does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper does not mention any specific software or library names with version numbers. |
| Experiment Setup | Yes | We considered a market with n = 800 students and m = 20 schools and generated student preferences with the Mallows model [Tubbs, 1992; Lu and Boutilier, 2014; Drummond and Boutilier, 2013]. Here θ R denotes a spread parameter, bs is a central preference (uniformly randomly chosen from all possible preferences in our experiment), and ω( s, bs) represents the Kendall tau distance between s and bs. When θ = 0, it becomes identical to the uniform distribution and converges to Pr( bs) as θ increases. Similar trends are obtained when conducting experiment in a broad range of θ, thus we only discuss two realistic settings, θ = 0.1 and θ = 0.3. The preference of each school c is drawn uniformly at random. We created 100 problem instances for each parameter setting. |