标题： 定义和分类群组模型：高阶网络的社会本体论 显示英文标题

标题： Defining and classifying models of groups: The social ontology of higher-order networks

摘要： 在复杂系统研究中，近年来对高阶相互作用的研究迅速发展。 研究人员已经形式化了各种类型的群体相互作用，例如公共物品博弈、生物传播和信息广播，展示了高阶网络如何比成对模型更直接地捕捉群体效应。 然而，将超边（涉及多于两个代理的边）等同于群体可能会产生误导，因为它掩盖了“群体相互作用”的多义性。 例如，许多高阶相互作用的模型关注超边的内部状态，在群体层面指定动态规则。 这些模型常常忽视与外部群体的相互作用如何影响群体内部的行为和动态。 然而，人类学家和哲学家提醒我们，规范、因素和影响群体间行为的外部力量对于定义群体内动态至关重要。 在本文中，我们将与高阶网络新兴物理学相关的社会本体论概念进行综合。 我们提出了一种分类群体相互作用模型的类型学，基于两个视角。 第一个视角关注群体内部个体参与集体行动，其中共享代理作为粘合剂。 第二个视角采用群体优先的方法，强调超越具体涉及个体的制度事实。 在这些视角的基础上，我们引入了四个维度来分类群体相互作用模型：持续性、耦合性、可约性和对齐性。 对于高阶网络的物理学，我们提供了一套嵌套的数学模型层次结构，以探索社会群体的复杂特性。 我们强调了文献中尚未探讨的社会互动，并提出了未来的研究方向，以促进社会本体论与复杂系统物理学之间的合作。 显示英文摘要

摘要： In complex systems research, the study of higher-order interactions has exploded in recent years. Researchers have formalized various types of group interactions, such as public goods games, biological contagion, and information broadcasting, showing how higher-order networks can capture group effects more directly than pairwise models. However, equating hyperedges-edges involving more than two agents-with groups can be misleading, as it obscures the polysemous nature of ``group interactions''. For instance, many models of higher-order interactions focus on the internal state of the hyperedge, specifying dynamical rules at the group level. These models often neglect how interactions with external groups can influence behaviors and dynamics within the group. Yet, anthropologists and philosophers remind us that external norms, factors, and forces governing intergroup behavior are essential to defining within-group dynamics. In this paper, we synthesize concepts from social ontology relevant to the emerging physics of higher-order networks. We propose a typology for classifying models of group interactions based on two perspectives. The first focuses on individuals within groups engaging in collective action, where shared agency serves as the binding force. The second adopts a group-first approach, emphasizing institutional facts that extend beyond the specific individuals involved. Building on these perspectives, we introduce four dimensions to classify models of group interactions: persistence, coupling, reducibility, and alignment. For the physics of higher-order networks, we provide a hierarchy of nested mathematical models to explore the complex properties of social groups. We highlight social interactions not yet explored in the literature on higher-order networks and propose future research avenues to foster collaboration between social ontology and the physics of complex systems.