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Mechanisms of reciprocity and diversity social networks: a modeling and comparative approach

机译:互惠和多样性社交网络的机制:一种建模与比较方法

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Three mechanisms have been proposed to underlie reciprocation of social behaviors in gregarious animals: "calculated reciprocity," "emotional bookkeeping," and "symmetry-based reciprocity." Among these explanations, emotional book-keeping has received the broadest support from experimental and observational studies. On the other hand, 3 individual-based models have shown that reciprocation may emerge via "symmetry-based reciprocity," "emotional bookkeeping," or a combination of both mechanisms. Here, we use these 3 models to assess their relative fit with empirical data on reciprocation and social network structure across different groups and species of macaques. We collected grooming data from 14 groups and 8 macaque species and simulated each group in each model. We analyzed and quantitatively compared social network metrics of the empirical and the models' grooming networks. The 3 models captured fairly well the features of observed networks, and fitted data from wild groups better than captive ones. The emotional bookkeeping model seemed on average to fit slightly better the social networks metrics observed in empirical data, but failed to reproduce some grooming patterns. The symmetry-based models, on the other hand, fitted better other network parameters (e.g., modularity). No model generally fitted the data better than the others, and the fit with some metrics (e.g., modularity, centralization index) was low even after optimization. Thus, our analyses indicate that in the models social interactions may be simpler than in reality and models may miss social processes (e.g., third-party awareness).
机译:已经提出了三种机制来实现血资动物社会行为的互动:“计算互惠”,“情绪簿记”和“基于对称的互惠”。在这些解释中,情感簿记已得到最广泛的实验和观察研究的支持。另一方面,已经通过“基于对称性的互惠”,“情绪簿记”或两种机制的组合来显示往复运动的3个基于个人的模型。在这里,我们使用这3种模型来评估它们的相对符合关于不同群体和猕猴的种类的往复和社交网络结构的实证数据。我们从14个组和8种猕猴的修饰数据收集,并在每个模型中模拟每个组。我们分析和定量地比较了实证和模型的梳理网络的社交网络指标。 3型型号相当捕获观察到的网络的特征,并比俘虏的网络从野生群体拟合数据。情感簿记模型似乎平均适合在经验数据中观察到的社交网络指标略微略微好转,但未能复制一些美容模式。另一方面,基于对称的模型拟合了更好的其他网络参数(例如,模块化)。没有模型通常比其他模型更好地拟合数据,即使在优化之后,符合某些度量(例如,模块化,集中指数)的拟合即使优化也很低。因此,我们的分析表明,在模型中,社交互动可能比现实和模型可能错过社会过程(例如,第三方意识)。

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