DiD: MIning Relationships Among variables in large datasets from CompLEx systems (MIRACLE)

DiD：挖掘来自 CompLEx 系统的大型数据集中变量之间的关系 (MIRACLE)

• 批准号: 1430411
• 负责人: C Michael Barton
• 金额: $ 12.5万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1430411&HistoricalAwards=false
• 关键词: DiD; MIning; Relationships; Among; variables

Some of the most pressing questions for social scientists -- ranging from efforts to better understand the interactions between humans and the environment to predicting how an aging population will impact the US and global economy -- require making sense of large amounts of data. As a result, social scientists are increasingly using new computational modeling methods to explore the dynamics and consequences of human interactions. These new methods, including agent-based models, provide ways to explore research questions that cannot be investigated using traditional statistical approaches. But appropriate methods to mine, analyze, and synthesis large-scale complex model output data in order to answer social science research questions are still lacking. Traditional analysis methods are designed for data that are linear, continuous, and normally distributed, while data from models of complex socio-ecological systems are non-linear, discontinuous, and power-law distributed. In this project, the researchers seek to address these challenges by developing, applying, and disseminating an integrated environment for analysis and visualization of data generated by complex systems models. An important broader impact is that the research will lead to tools that will allow stakeholders, policy makers, and the general public to explore, interact with, and provide feedback on otherwise difficult-to-understand models.The project builds on ongoing research by the project team, and uses the NSF-supported CoMSES Net Computational Modeling Library as a platform to make this suite of collaborative, open-source tools broadly available. This community environment will allow any users to post model output along with associated metadata, visualize and analyze output data, comment on and share analyses, and conduct comparative and meta-analysis, drawing on data from other projects. This cyber-infrastructure will provide semi-automated means of discovering relationships that can lead to new theories about how social systems work, test the realism of simulations against knowledge from empirical systems, and propose new research directions to explore.

对于社会科学家来说，一些最紧迫的问题--从努力更好地理解人类与环境之间的相互作用，到预测人口老龄化将如何影响美国和全球经济--需要理解大量数据。因此，社会科学家越来越多地使用新的计算建模方法来探索人类互动的动力学和后果。这些新的方法，包括基于代理的模型，提供了探索无法使用传统统计方法调查的研究问题的方法。但对大规模复杂模型输出数据进行挖掘、分析和综合以回答社会科学研究问题的适当方法仍然缺乏。传统的分析方法是针对线性、连续和正态分布的数据，而来自复杂社会生态系统模型的数据是非线性、不连续和幂分布的。在这个项目中，研究人员试图通过开发、应用和传播一个用于分析和可视化复杂系统模型生成的数据的集成环境来解决这些挑战。一个重要的更广泛的影响是，这项研究将产生工具，允许利益相关者、政策制定者和普通公众探索其他难以理解的模型，与之互动并提供反馈。该项目建立在项目团队正在进行的研究的基础上，并使用NSF支持的CoMSES网络计算模型库作为平台，使这套协作的开源工具广泛可用。这个社区环境将允许任何用户发布模型输出以及相关的元数据，可视化和分析输出数据，评论和共享分析，并利用其他项目的数据进行比较和荟萃分析。这种网络基础设施将提供半自动的方法来发现关系，这些关系可以导致关于社会制度如何工作的新理论，测试模拟的真实性，对照经验系统的知识，并提出新的研究方向来探索。