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Collective information processing in human phase separation

机译:人类分离中的集体信息处理

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摘要

In our digital societies, individuals massively interact through digital interfaces whose impact on collective dynamics can be important. In particular, the combination of social media filters and recommender systems can lead to the emergence of polarized and fragmented groups. In some social contexts, such segregation processes of human groups have been shown to share similarities with phase separation phenomena in physics. Here, we study the impact of information filtering on collective segregation behaviour of human groups. We report a series of experiments where groups of 22 subjects have to perform a collective segregation task that mimics the tendency of individuals to bond with other similar individuals. More precisely, the participants are each assigned a colour (red or blue) unknown to them, and have to regroup with other subjects sharing the same colour. To assist them, they are equipped with an artificial sensory device capable of detecting the majority colour in their 'environment' (defined as theirknearest neighbours, unbeknownst to them), for which we control the perception range,k= 1, 3, 5, 7, 9, 11, 13. We study the separation dynamics (emergence of unicolour groups) and the properties of the final state, and show that the value ofkcontrols the quality of the segregation, although the subjects are totally unaware of the precise definition of the 'environment'. We also find that there is a perception rangek= 7 above which the ability of the group to segregate does not improve. We introduce a model that precisely describes the random motion of a group of pedestrians in a confined space, and which faithfully reproduces and allows interpretation of the results of the segregation experiments. Finally, we discuss the strong and precise analogy between our experiment and the phase separation of two immiscible materials at very low temperature. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.
机译:在我们的数字社会中,个人通过数字接口大规模互动,其对集体动态的影响可能很重要。特别地,社交媒体过滤器和推荐系统的组合可以导致极化和碎片组的出现。在一些社会背景下,已显示人群的这种分离过程将相似性与物理学中的相分离现象共享。在这里,我们研究了信息过滤对人群集体分离行为的影响。我们举报了一系列实验,其中22个受试者必须执行集体隔离任务,以模仿个人与其他类似个人债券的趋势。更精确地,参与者每个都分配了它们的颜色(红色或蓝色),并且必须与共享相同颜色的其他主题重新组合。为了帮助它们,它们配备了一种人工感官装置,其能够检测到其“环境”中的大多数颜色(定义为他们的最佳邻居,不知数,不知数),我们控制着感知范围,K = 1,3,5, 7,9,11,13。我们研究了分离动力学(Unicolour Group的出现)和最终状态的性质,并表明,虽然受试者完全没有意识到精确定义'环境'。我们还发现,上面有一个感知范围= 7,其中群体分离的能力不会改善。我们介绍了一种模型,该模型精确地描述了一群人的一群行人,并忠实地再现并允许解释分离实验的结果。最后,我们讨论了我们的实验与两个不混溶材料在非常低温下的相位分离之间的强烈和精确的类比。本文是主题问题“生物系统中集体迁移的多规模分析和建模”的一部分。

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