Abstract
Data Farming, network applications and approaches to integrate network analysis and processes to the data farming paradigm are presented as approaches to address complex system questions. Data Farming is a quantified approach that examines questions in large possibility spaces using modeling and simulation. It evaluates whole landscapes of outcomes to draw insights from outcome distributions and outliers. Social network analysis and graph theory are widely used techniques for the evaluation of social systems. Incorporation of these techniques into the data farming process provides analysts examining complex systems with a powerful new suite of tools for more fully exploring and understanding the effect of interactions in complex systems. The integration of network analysis with data farming techniques provides modelers with the capability to gain insight into the effect of network attributes, whether the network is explicitly defined or emergent, on the breadth of the model outcome space and the effect of model inputs on the resultant network statistics.
Highlights
Data Farming is a quantified approach that examines questions in large possibility spaces using modeling and simulation
Data Farming is documented as a process through the efforts of the 37 authors of the Final
Many of these data farmers plus more are applying data farming to questions of interest to NATO within MSG-124
Summary
Data Farming is a quantified approach that examines questions in large possibility spaces using modeling and simulation. Data farming uses simulation in a collaborative and iterative team process [3] that has been used primarily in defense applications [4]. This process normally requires input and participation by subject matter experts, modelers, analysts, and decision-makers. In 2010, the NATO Research and Technology Organization started the three-year Modeling and Simulation Task Group “Data Farming in Support of NATO” to assess and document the data farming methodology to be used for decision support. This group was called Modeling and Simulation. By applying the data farming experimental process across the network attribute space we gain insight into network-related cause and effects, examining whether network features affect the model outcomes or how model parameter variation affects network evolution and attributes
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