# VoteLab: A Modular and Adaptive Experimentation Platform for Online Collective Decision Making

arXiv: https://arxiv.org/abs/2307.10903 · HTML: https://mlsanity.com/paper/2307.10903/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2023.

## Built on by (most cited first, 1 of 1)

- [Bayesian Optimization for Hyperparameters Tuning in Neural Networks](https://mlsanity.com/paper/2410.21886/) (2024)

## Abstract (arXiv)

Digital democracy and new forms for direct digital participation in policy making gain unprecedented momentum. This is particularly the case for preferential voting methods and decision-support systems designed to promote fairer, more inclusive and legitimate collective decision-making processes in citizens assemblies, participatory budgeting and elections. However, a systematic human experimentation with different voting methods is cumbersome and costly. This paper introduces VoteLab, an open-source and thoroughly-documented platform for modular and adaptive design of voting experiments. It supports to visually and interactively build reusable campaigns with a choice of different voting methods, while voters can easily respond to subscribed voting questions on a smartphone. A proof-of-concept with four voting methods and questions on COVID-19 in an online lab experiment have been used to study the consistency of voting outcomes. It demonstrates the capability of VoteLab to support rigorous experimentation of complex voting scenarios.