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Quantifying uncertainty due to fission–fusion dynamics as a component of social complexity

机译:量化由于裂变融合动态而引起的不确定性这是社会复杂性的一部分

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摘要

Groups of animals (including humans) may show flexible grouping patterns, in which temporary aggregations or subgroups come together and split, changing composition over short temporal scales, (i.e. fission and fusion). A high degree of fission–fusion dynamics may constrain the regulation of social relationships, introducing uncertainty in interactions between group members. Here we use Shannon's entropy to quantify the predictability of subgroup composition for three species known to differ in the way their subgroups come together and split over time: spider monkeys (Ateles geoffroyi), chimpanzees (Pan troglodytes) and geladas (Theropithecus gelada). We formulate a random expectation of entropy that considers subgroup size variation and sample size, against which the observed entropy in subgroup composition can be compared. Using the theory of set partitioning, we also develop a method to estimate the number of subgroups that the group is likely to be divided into, based on the composition and size of single focal subgroups. Our results indicate that Shannon's entropy and the estimated number of subgroups present at a given time provide quantitative metrics of uncertainty in the social environment (within which social relationships must be regulated) for groups with different degrees of fission–fusion dynamics. These metrics also represent an indirect quantification of the cognitive challenges posed by socially dynamic environments. Overall, our novel methodological approach provides new insight for understanding the evolution of social complexity and the mechanisms to cope with the uncertainty that results from fission–fusion dynamics.
机译:动物群(包括人类)可能会表现出灵活的分组模式,其中临时的聚集体或亚组聚集在一起并分裂,从而在短时间范围内(即裂变和融合)改变组成。高度的裂变融合动力可能会限制社会关系的调节,从而在小组成员之间的互动中带来不确定性。在这里,我们使用Shannon熵来量化三个物种的亚组组成的可预测性,这三个物种的亚组聚集在一起并随时间分裂的方式有所不同:蜘蛛猴(Ateles geoffroyi),黑猩猩(Pan穴居人)和geladas(Theropithecus gelada)。我们制定了一个考虑子组大小变化和样本大小的熵的随机期望,可以将其与子组组成中观察到的熵进行比较。使用集合划分理论,我们还开发了一种方法,可以根据单个焦点子组的组成和大小来估计该组可能被划分为的子组的数量。我们的结果表明,对于不同程度的裂变-融合动力学的群体,香农的熵和给定时间存在的亚组的估计数量提供了社会环境中不确定性(必须在其中调节社会关系)的定量度量。这些指标还代表了社会动态环境所构成的认知挑战的间接量化。总体而言,我们新颖的方法论方法为理解社会复杂性的演变以及应对由裂变-融合动力学导致的不确定性的机制提供了新的见解。

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