Deliberation and Voting in Approval-Based Multi-Winner Elections
Authors: Kanav Mehra, Nanda Kishore Sreenivas, Kate Larson
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results show that deliberation generally improves welfare and representation guarantees, but the results are sensitive to how the deliberation process is organized. We also show, experimentally, that simple voting rules, such as approval voting, perform as well as more sophisticated rules such as proportional approval voting or method of equal shares if deliberation is properly supported. This has ramifications on the practical use of such voting rules in citizen-focused democratic processes. |
| Researcher Affiliation | Academia | Kanav Mehra , Nanda Kishore Sreenivas and Kate Larson David R. Cheriton School of Computer Science, University of Waterloo {kanav.mehra, nksreenivas, kate.larson}@uwaterloo.ca |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for the experiments is available at: https://github.com/kanav-mehra/deliberation-voting. |
| Open Datasets | No | The paper describes generating synthetic data using a Mallows model ('Agents initial preferences are sampled using a Mallows model, with ϕ = 0.2') rather than using or providing access to a publicly available dataset. |
| Dataset Splits | No | The paper describes a simulation setup and repeats the simulation multiple times ('This entire simulation is repeated 10, 000 times') but does not specify training, validation, or test dataset splits in the context of model evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'the Python library abcvoting [Lackner et al., 2021]' but does not specify version numbers for Python or the library itself, which is required for reproducibility. |
| Experiment Setup | Yes | Our election setup consists of 50 candidates (|C| = 50) and 100 voters, with 80 agents in the majority group (Nmaj) and 20 in the minority group (Nmin). Agents initial preferences are sampled using a Mallows model, with ϕ = 0.2. For the BC model, all three parameters (i, αi, βi) are sampled from uniform distributions over the full range for each parameter. When deliberating, agents are divided into 10 groups (except for the large group strategy). For iterative deliberation, the deliberation continues for R = 5 rounds. We consider different approval-based multi-winner voting rules to elect k = 5 winners. We use a flexible ballot size, such that each agent s ballot is of size bi, where bi is sampled from N(2k, 1.0). An initial approval profile A0 is eligible only if RR(AV, A0) < 0.9 UR(CC, A0) < 0.9. This entire simulation is repeated 10, 000 times and the average values are reported. |