Advancing network statistical models for social and economic behaviour

推进社会和经济行为的网络统计模型

• 批准号: DE160100471
• 负责人: A/Prof Peng Wang
• 金额: $ 26.17万
• 依托单位: Swinburne University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE160100471
• 关键词: Advancing; network; statistical; models; social

This project aims to improve our understanding of how social networks affect social and economic behaviour. Social networks underpin our everyday lives and are fundamental to issues such as disease transmission and healthcare system design. It has long been recognised that existing models of social and economic behaviour are missing critical unmeasured elements. In many instances, these are the invisible connections between people and organisations that allow bonds of trust to form and informal information to flow. Network analysis can elucidate these hidden channels. This project aims to develop a statistical co-evolution modelling framework for social networks for both cross-sectional and longitudinal analysis. Further, the project aims to deliver advanced network modelling software.

这个项目旨在提高我们对社交网络如何影响社会和经济行为的理解。社交网络支撑着我们的日常生活，对疾病传播和医疗保健系统设计等问题至关重要。人们早就认识到，现有的社会和经济行为模式缺少关键的、不可衡量的元素。在许多情况下，这些是人和组织之间的无形联系，使信任纽带得以形成，非正式信息得以流动。网络分析可以阐明这些隐藏的渠道。该项目旨在为社会网络开发一个统计协同进化模型框架，用于横断面和纵向分析。此外，该项目旨在提供先进的网络建模软件。