Dynamic Proportional Rankings
Authors: Jonas Israel, Markus Brill
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental Evaluation In order to better understand the behavior of the dynamic ranking rules considered in this paper, we conducted computational experiments using randomly generated approval profiles. |
| Researcher Affiliation | Academia | Jonas Israel and Markus Brill Research Group Efficient Algorithms Technische Universit at Berlin 10587 Berlin, Germany {j.israel, brill}@tu-berlin.de |
| Pseudocode | No | All presented ranking rules can be computed in polynomial time; see the full version of this paper for an asymptotic runtime analysis and pseudocode. |
| Open Source Code | No | No explicit statement about open-source code for the methodology is provided. The arXiv link [Israel and Brill, 2021] refers to the full version of the paper, not a code repository. |
| Open Datasets | No | Since we were mainly interested in the proportional representation of groups of voters with similar preferences, we generated profiles according to two probabilistic models that lead to polarized electorates with easily identifiable groups. |
| Dataset Splits | No | All of our profiles consist of 60 voters and 20 candidates, and the approval sets are generated according to two different models. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4) that would be needed to replicate the experiment. |
| Experiment Setup | Yes | Setup. All of our profiles consist of 60 voters and 20 candidates, and the approval sets are generated according to two different models. ... The selection behavior of the DM is modeled via Google clickthrough rates. In particular, the probability of selection decreases when going down the ranking. |