Proportional Aggregation of Preferences for Sequential Decision Making
Authors: Nikhil Chandak, Shashwat Goel, Dominik Peters
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present empirical results for these rules based on synthetic data and U.S. political elections. We also run experiments using the moral machine dataset about ethical dilemmas. |
| Researcher Affiliation | Academia | Nikhil Chandak1, Shashwat Goel1, Dominik Peters2 1IIIT Hyderabad 2CNRS, LAMSADE, Université Paris Dauphine PSL |
| Pseudocode | No | The paper describes the methods (Sequential Phragmén, Method of Equal Shares, Proportional Approval Voting) in prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions data availability ('available at https://osf.io/t6p7s/') but does not provide a specific link or explicit statement about the release of its own source code for the methodology described. |
| Open Datasets | Yes | In addition to synthetic data, we evaluated the rules on data from U.S. political general elections which we collected (available at https://osf.io/t6p7s/). [...] to empirically test our work on a dataset that has structured features which allow preference learning, we consider virtual democracy applied to the moral machine (Awad et al. 2018). |
| Dataset Splits | No | The paper mentions training models but does not explicitly provide specific percentages, absolute sample counts, or predefined splits for training, validation, and testing datasets. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware components such as GPU or CPU models, memory, or cloud instance types used for running experiments. |
| Software Dependencies | No | The paper discusses the use of the Plackett-Luce (PL) model but does not specify version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | For analysis based on synthetic data, we follow a similar setup to the one used by Lackner (2020) which is based on the popular approach of sampling both voters and alternatives as points in a two-dimensional Euclidean space (Elkind et al. 2017). We use n = 20 voters who are split into a group of 6 and a group of 14 voters. [...] We use T = 20 rounds with 20 alternatives per round. [...] We collected instances from 16 counties in California and Colorado from 2020 and 2022 (for which data was available). [...] As a baseline, we train a combined model on respondents from all countries, using 100 samples from each country for a balanced representation in the training data. [...] We produce 100 decision rounds together with 100 alternatives for each round. |