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

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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.