Ontology and Evolution of Children's Playgroup Formation

幼儿如何在过渡和变化的背景下形成关系？为了回答这个问题以及其他一些关于人类发展和复杂社会过程的相关问题，本研究由一个跨学科团队实施，该团队由一个基于代理的建模师、几个人类发展学家和一个数学家组成，他们将使用一个基于代理的模型（PlayMate）来模拟幼儿形成游戏伙伴偏好，从而研究游戏群体的本体论和进化。结合基于代理的模型的开发，我们将收集60名幼儿（3-6岁）的日常观察，这些幼儿来自三个独立的学前班教室，为期一学年。每个孩子每个月将被观察75-125次，这样每个教室每个月可以产生大约1500 - 3000次观察。这些数据将使我们能够研究游戏群体的动态形成和进化，这是一种非常适合基于代理的建模的微观社会现象。除了对游戏活动的观察外，还将使用教师和观察员评分来收集儿童特征的数据。这些数据将用于确定仿真的真实性和有效性。这种建模方法为我们提供了机会，将这种方法建立为如何模拟微观社会过程的原型，并将结果与实际数据进行比较。通过将经验数据与理论相结合，本研究的结果将有助于建立不仅适用于儿童，而且适用于人类整个生命周期的社会影响模型。此外，我们的跨学科方法说明了跨学科工作的潜在丰富性，并为培养未来的科学家奠定了基础

How do young children form relationships in the context of transitions and change? In an attempt to answer this and several other related questions about human development and complex social processes, this study, implemented by a cross-disciplinary team consisting of an agent based modeler, several human developmentalists, and a mathematician, will examine the ontology and evolution of playgroups using an agent-based model (PlayMate) to simulate young children forming play partner preferences. In conjunction with the development of the agent based model, we will collect daily observations of 60 young children (ages 3-6) from three separate preschool classrooms over an academic year. Each child will be observed 75-125 times per month, allowing us to generate about 1500 - 3000 observations per month per classroom. These data will allow us to investigate the dynamic formation and evolution of playgroups, a micro-social phenomenon well suited for agent based modeling. In addition to observations of play activities, data will also be collected on child characteristics using teacher and observer ratings. These data will be used to determine the veridicality and validity of the simulation. This modeling approach provides us with the opportunity to establish this methodology as a prototype of how to simulate micro-social processes and compare the results against real data. By combining empirical data with theory, the findings of this study will contribute to building models of social influence that can be applied not only to children, but also to humans throughout the lifespan. Moreover, our interdisciplinary approach illustrates the potential richness of cross discipline work, and lays the foundation for training future scientists