Individual-Based Stability in Hedonic Diversity Games
Authors: Niclas Boehmer, Edith Elkind1822-1829
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results by empirical analysis, comparing the IS outcomes found by our algorithm, the algorithm of Bredereck et al. and a natural better-response dynamics. In Section 6, we empirically compare the outcomes produced (1) by our algorithm for finding individually stable outcomes, (2) by the algorithm of Bredereck et al. and (3) by a natural better-response dynamics with respect to several measures, such as the average social welfare and the diversity of resulting groups. |
| Researcher Affiliation | Academia | Niclas Boehmer TU Berlin Berlin, Germany niclas.boehmer@tu-berlin.de Edith Elkind University of Oxford Oxford, UK elkind@cs.ox.ac.uk |
| Pseudocode | Yes | Algorithm 1 Computing an individually stable outcome |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic instances for its experiments (e.g., "For each s = 2, . . . , 50, we generate 1000 HDGs"), rather than using a pre-existing, publicly available dataset that requires access information. |
| Dataset Splits | No | The paper describes generating instances for empirical comparison of algorithms but does not specify training, validation, or testing splits of a dataset in the context of model training or evaluation reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the hardware specifications (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., libraries, frameworks, or solvers) with version numbers that would be needed to reproduce the experiment. |
| Experiment Setup | Yes | Preference Models As Bredereck et al. s algorithm is only defined for single-peaked HDGs, we only use single-peaked instances in our analysis. We consider three intuitively appealing ways of sampling strict preferences over ratios that are single-peaked on Θ. Uniform single-peaked preferences (u SP)... Uniform-peak single-peaked preferences (up SP)... Symmetric single-peaked preferences (sym SP)... For each s = 2, . . . , 50, we generate 1000 HDGs with s red and s blue agents; thus, n = 2s takes even values from 4 to 100. |