Developing Latent Hierarchical Network Models for Cross-Cultural Comparisons of Social and Economic Inequality

来自社会科学各个领域的研究人员越来越多地使用网络分析工具来代表他们所研究的群体，绘制出学生之间的友谊、公司之间的商业关系以及村民之间的支持关系。这些网络通常基于调查，人们对他们关系的报告被结合起来代表整个社区的整体结构。虽然这些网络数据集越来越细粒度和复杂，但用于研究它们的工具通常需要简化和假设，这让社会科学家感到不舒服（例如，假设人们的回忆是完美的，或者将人们视为孤立的个体，而不是家庭等更大的社会群体的成员）。我们将开发网络模型，充分利用社交网络数据集中通常包含的信息的各个方面。在这样做的过程中，我们偏离了当代社会网络分析中的流行模型，这些模型将观察到的网络数据集视为代表“真实”网络。相反，我们假设真正的网络是“潜在的”，因此没有经验观察到，并进一步将观察到的网络数据框架为我们正在建模的不完美测量。在提出这个概率框架时，我们将首先考虑影响人们说出谁的名字（以及他们不说出谁的名字）的各种个人层面的偏见。然后，我们将扩展我们的模型，以允许节点（这里是人）形成分层嵌套的组（例如，家庭），从而捕获系统中存在的不同级别的单元。最后，我们将扩展这个模型，以考虑不同层次上的个体单位及其关系随时间的变化，从而捕获相关的时间演变。为了做到这一点，我们将汇集一个对网络数据分析和建模感兴趣的不同研究团队。我们正在补充人类学家（Power），心理学家（Redhead）和统计物理学家（De Bacco）的技能和观点，并增加：一位研究复杂网络的数学家（伦敦玛丽女王大学的Ginestra Bianconi教授），一位研究应用因果推理的计算机科学家（哥伦比亚大学的Dhanya Sridhar博士），一位研究机器学习和概率推理的工程师和计算机科学家（萨尔大学的Isabel Valera教授），一位开发多层次网络模型的统计学家（马里兰大学的Tracy Sweet副教授），一位开发贝叶斯统计工具的人类学家和统计学家（马克斯普朗克进化人类学研究所的Richard McElreath教授），一位收集和开发社交网络数据工具的人类学家（辛辛那提大学的Jeremy Koster副教授），以及一位擅长纵向多层模型的社会统计学家（伦敦经济学院的Fiona Steele教授）。有了这种多样化的观点（无论是学科、应用还是职业阶段），我们相信我们的合作将导致通用的、健壮的生成网络模型的发展，在科学领域具有广泛的应用潜力。我们致力于促进这些模型的吸收，因此我们将聘请一名研究人员为他们开发用户友好的R和Python包。这个项目基于“ENDOW项目”的分析需求，这是一个由美国国家科学基金会资助的项目，主要研究网络结构以及人们在该网络中的位置如何与社会内部和社会之间的财富不平等分配相关联。40多名研究人员正在为这个项目从世界各地的农村社区收集社会网络数据。我们在这里开发的模型将帮助我们理解（并有可能纠正）世界各地社会和经济不平等的一些驱动因素。

Researchers from across the social sciences are increasingly using the tools of network analysis to represent the groups they study, mapping out friendships between students, business relations between companies, and supportive relationships between villagers. These networks are often based on surveys, where people's reports of their relationships are combined to represent the overall structure of entire communities. While these network datasets are increasingly fine-grained and complex, the tools used to study them often require simplifications and assumptions that social scientists are uncomfortable with (e.g., assuming that people's recollections are perfect, or treating people as isolated individuals rather than members of larger social groupings like households). We will develop network models that fully exploit the various facets of the information typically contained in social network datasets. In doing this, we depart from prevalent models in contemporary social network analysis that treat an observed network data set as representing the "true" network. Instead, we assume that the true network is "latent" and, therefore not empirically observed, and further frame the observed network data as an imperfect measurement of what we are modelling. In proposing this probabilistic framework, we will first account for the various individual-level biases that shape who people name (and who they do not). We will then extend our model to allow for nodes (here, people) to form into hierarchically nested groups (for example, households) and thus capture units at the different levels that are present in the system. Finally, we will expand this model to account for changes over time of both the individual units at different levels of the hierarchy and their relationships, thus capturing relevant time evolution.To do this, we will bring together a diverse team of researchers interested in the analysis and modelling of network data. We are complementing the skills and perspectives of the anthropologist (Power), psychologist (Redhead), and statistical physicist (De Bacco) co-investigators with the addition of: a mathematician studying complex networks (Prof Ginestra Bianconi, Queen Mary University of London), a computer scientist working on applied causal inference (Dr Dhanya Sridhar, Columbia University), an engineer and computer scientist working on machine learning and probabilistic inference (Prof Isabel Valera, Saarland University), a statistician developing multilevel network models (Assoc Prof Tracy Sweet, University of Maryland), an anthropologist and statistician developing Bayesian statistical tools (Prof Dir Richard McElreath, Max Planck Institute for Evolutionary Anthropology), an anthropologist gathering and developing tools for social network data (Assoc Prof Jeremy Koster, University of Cincinnati), and a social statistician with expertise in longitudinal multilevel models (Prof Fiona Steele, London School of Economics). With this diversity of perspectives (whether of discipline, application, or career stage), we are confident that our collaboration will result in the development of general, robust generative network models with wide potential for application across the sciences. We are committed to facilitating the uptake of these models, so we will be hiring a research officer to develop user-friendly R and Python packages for their use. This project is grounded in the analytical needs of the "ENDOW project," a US National Science Foundation-funded project that is primarily examining how network structure, and people's position within that network, is associated with the distribution of wealth inequality both within and between societies. Over forty researchers are collecting social network data from rural communities around the world for this project. The models we develop here will help us understand (and potentially then rectify) some of the drivers of social and economic inequality around the world.