Data from: How random is social behaviour? Disentangling social complexity through the study of a wild house mouse population

Out of all the complex phenomena displayed in the behaviour of animal groups, many are thought to be emergent properties of rather simple decisions at the individual level. Some of these phenomena may also be explained by random processes only. Here we investigate to what extent the interaction dynamics of a population of wild house mice (Mus domesticus) in their natural environment can be explained by a simple stochastic model. We first introduce the notion of perceptual landscape, a novel tool used here to describe the utilisation of space by the mouse colony based on the sampling of individuals in discrete locations. We then implement the behavioural assumptions of the perceptual landscape in a multi-agent simulation to verify their accuracy in the reproduction of observed social patterns. We find that many high-level features -- with the exception of territoriality -- of our behavioural dataset can be accounted for at the population level through the use of this simplified representation. Our findings underline the potential importance of random factors in the apparent complexity of the mice's social structure. These results resonate in the general context of adaptive behaviour versus elementary environmental interactions.在动物群体行为所展现的各类复杂现象中，多数被认为是个体层面简单决策的涌现属性（emergent properties），其中部分现象或许仅能通过随机过程予以解释。本研究旨在探究：自然环境下的野生小家鼠（Mus domesticus）种群互动动态，在多大程度上可通过简单随机模型加以阐释。我们首先引入感知景观（perceptual landscape）这一新颖工具，依托离散位置的个体采样数据，描述鼠群的空间利用模式。随后，我们在多智能体（multi-agent）仿真中植入感知景观的行为假设，以验证其在复现观测到的社会模式时的准确性。研究结果显示，本行为数据集的诸多高阶特征，除领地性（territoriality）外，均可通过该简化表征在种群层面得到合理解释。我们的发现凸显了随机因素在小鼠社会结构表观复杂性中的潜在重要性。这些研究结果在适应性行为与基础环境交互的通用研究语境中具有呼应价值。