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

开发潜在的分层网络模型以进行社会和经济不平等的跨文化比较

基本信息

批准号：
ES/V006495/1
负责人：
Eleanor Power
金额：
$ 18.28万
依托单位：
London School of Economics and Political Science
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FV006495%2F1
关键词：
Developing Latent Hierarchical Network Models

项目摘要

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.

来自社会科学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。这些网络往往以调查为基础，将人们关于其关系的报告结合起来，代表整个社区的总体结构。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社会科学家不舒服的简化和假设（例如，假设人们的记忆是完美的，或者把人们当作孤立的个体，而不是像家庭这样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。在这样做的时候，我们离开流行的模型在当代社会网络分析，处理观察到的网络数据集代表“真正的”网络。相反，我们假设真实的网络是“潜在的”，因此无法根据经验观察到，并进一步将观察到的网络数据框定为我们建模的不完美测量。在提出这一概率框架时，我们首先要解释各种个人层面的偏见，这些偏见决定了人们给谁起名字（以及不给谁起名字）。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，我们将扩展该模型，以考虑层次结构中不同级别的单个单元及其关系随时间的变化，从而捕获相关的时间演变。为此，我们将汇集对网络数据分析和建模感兴趣的不同研究人员团队。我们正在补充人类学家（Power）、心理学家（Redhead）和统计物理学家的技能和观点（De Beville）共同研究者，增加了：研究复杂网络的数学家（伦敦玛丽女王大学的吉内斯特拉·比安科尼教授），一位致力于应用因果推理的计算机科学家（Dhanya Sridhar博士，哥伦比亚大学），工程师和计算机科学家，致力于机器学习和概率推理（萨尔兰大学伊莎贝尔瓦莱拉教授），一位开发多级网络模型的统计学家（美国马里兰州大学特雷西·斯威特教授），人类学家和统计学家，开发贝叶斯统计工具（马克斯·普朗克进化人类学研究所主任理查德·麦克尔里斯教授）、收集和开发社交网络数据工具的人类学家（辛辛那提的阿斯伯格教授杰里米·科斯特）和擅长纵向多层次模型的社会统计学家（伦敦经济学院菲奥娜·斯蒂尔教授）。有了这种观点的多样性（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，强大的生成网络模型的发展，具有广泛的跨科学应用潜力。我们致力于促进这些模型的应用，因此我们将聘请一名研究人员来开发用户友好的R和Python软件包。该项目是基于美国国家科学基金会资助的“ENDOW项目”的分析需求，该项目主要研究网络结构和人们在该网络中的地位如何与社会内部和社会之间的财富分配不平等相关联。超过40名研究人员正在为这个项目收集来自世界各地农村社区的社交网络数据。我们在这里开发的模型将帮助我们理解（并可能纠正）世界各地社会和经济不平等的一些驱动因素。