A decentralised collective learning problem is investigated in which a population of agents attempts to learn the true state of the world based on direct evidence from the environment and belief fusion carried out during local interactions between agents. A parameterised fusion operator is introduced that returns beliefs of varying levels of imprecision. This is used to explore the effect of fusion imprecision on learning performance in a series of agent-based simulations. In general, the results suggest that imprecise fusion operators are optimal when the frequency of fusion is high relative to the frequency with which evidence is obtained from the environment.

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