# Imprecise Belief Fusion Improves Multi-agent Social Learning

arXiv: https://arxiv.org/abs/2608.01367 · HTML: https://mlsanity.com/paper/2608.01367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 2 Aug 2026.

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

- [When slower is faster](https://mlsanity.com/paper/1506.06796/) (2015)
- [Probability of Inconsistencies in Theory Revision: A multi-agent model for updating logically interconnected beliefs under bounded confidence](https://mlsanity.com/paper/1405.3505/) (2013)
- [Distributed Possibilistic Learning in Multi-Agent Systems](https://mlsanity.com/paper/2001.07145/) (2020)
- [The Impact of Network Connectivity on Collective Learning](https://mlsanity.com/paper/2106.00655/) (2021)
- [Controlling Robot Swarm Aggregation through a Minority of Informed Robots](https://mlsanity.com/paper/2205.03192/) (2022)

## Abstract (arXiv)

In social learning, agents learn not only from direct evidence but also through interactions with their peers. We investigate the role of imprecision in such interactions and ask whether it can improve the effectiveness of the collective learning process. To that end we propose a model of social learning where beliefs are equivalent to formulas in a propositional language, and where agents learn from each other by combining their beliefs according to a fusion operator. The latter is parametrised so as to allow for different levels of imprecision, where a more imprecise fusion operator tends to generates a more imprecise fused belief when the two combined beliefs differ. In this context we describe both difference equation models and agent-based simulations of social learning under a variety of conditions and with different initial biases. The results presented suggest that for populations with a strong initial bias towards incorrect beliefs some level of imprecision in fusion can improve learning accuracy across a range of learning conditions. Furthermore, such benefits of imprecision are consistent with a stability analysis of the fixed points of the proposed difference equation models.