Imprecise Belief Fusion Improves Multi-agent Social Learning
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...
| Authors | Zixuan Liu, Jonathan Lawry, Michael Crosscombe |
|---|---|
| Primary category | cs.MA |
| Published | 2 August 2026 |
| Last updated at source | 2 August 2026 |
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