# Towards Group Learning: Distributed Weighting of Experts

arXiv: https://arxiv.org/abs/2206.02566 · HTML: https://mlsanity.com/paper/2206.02566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 3 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [A Mathematical Model for Optimal Decisions in a Representative Democracy](https://mlsanity.com/paper/1807.06157/) (2018)
- [Tracking Truth by Weighting Proxies in Liquid Democracy](https://mlsanity.com/paper/2103.09081/) (2021)
- [When Can Liquid Democracy Unveil the Truth?](https://mlsanity.com/paper/2104.01828/) (2021)
- [Peer Selection with Noisy Assessments](https://mlsanity.com/paper/2107.10121/) (2021)
- [The Optimal Size of an Epistemic Congress](https://mlsanity.com/paper/2107.01042/) (2021)

## Abstract (arXiv)

Aggregating signals from a collection of noisy sources is a fundamental problem in many domains including crowd-sourcing, multi-agent planning, sensor networks, signal processing, voting, ensemble learning, and federated learning. The core question is how to aggregate signals from multiple sources (e.g. experts) in order to reveal an underlying ground truth. While a full answer depends on the type of signal, correlation of signals, and desired output, a problem common to all of these applications is that of differentiating sources based on their quality and weighting them accordingly. It is often assumed that this differentiation and aggregation is done by a single, accurate central mechanism or agent (e.g. judge). We complicate this model in two ways. First, we investigate the setting with both a single judge, and one with multiple judges. Second, given this multi-agent interaction of judges, we investigate various constraints on the judges' reporting space. We build on known results for the optimal weighting of experts and prove that an ensemble of sub-optimal mechanisms can perform optimally under certain conditions. We then show empirically that the ensemble approximates the performance of the optimal mechanism under a broader range of conditions.