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首页> 外文期刊>Autonomous agents and multi-agent systems >Task dynamics in self-organising task groups: expertise, motivational, and performance differences of specialists and generalists
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Task dynamics in self-organising task groups: expertise, motivational, and performance differences of specialists and generalists

机译:自组织任务组中的任务动态:专家和通才的专业知识,动机和绩效差异

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摘要

Multi-agent simulation is applied to explore how different types of task variety cause workgroups to change their task allocation accordingly. We studied two groups, generalists and specialists. We hypothesised that the performance of the specialists would decrease when task variety increases. The generalists, on the other hand, would perform better in a high task variety condition. The results show that these hypotheses were only partly supported because both learning and motivational effects changed the task allocation process in a much more complex way. We conclude that although no task variety leads to specialisation and high task variety leads to generalisation, in general, performance is better when task variety is low. Further, in case of no task variety, specialists outperform generalists. In case of moderate variety the opposite is true. With high task variety, since there is no space for any expertise and motivational development, the behaviour of specialists and generalists becomes more similar, and, consequently also their performance.
机译:应用多主体仿真来探索不同类型的任务种类如何导致工作组相应地更改其任务分配。我们研究了两个小组,通才和专家。我们假设,随着任务种类的增加,专家的表现将下降。另一方面,通才专家在任务繁多的情况下会表现更好。结果表明,这些假设仅得到部分支持,因为学习和动机影响都以更为复杂的方式改变了任务分配过程。我们得出的结论是,虽然没有任务多样性会导致专业化,而任务多样性会导致泛化,但总的来说,当任务多样性较低时,性能会更好。此外,在没有任务多变的情况下,专家的表现要优于通才。如果品种适中,则相反。任务种类繁多,由于没有任何专业知识和动机发展的空间,因此专家和通才的行为变得更加相似,因此他们的表现也越来越相似。

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