Prediction and Kinetic Visualization System (PKVS) for Analysis of Behavior in Dynamic Meta-Networks

This project develops methods and tools to assist with analysis, interpretation and decision making regarding data about the co-evolution of multiple, interdependent, temporally-embedded, complex, social meta-networks. Models that narrowly focus on networks with only one relational type do not adequately account for interdependent relationships and effects; therefore, integral to our system concept is the identification of four theoretically distinct relational types: resource exchange, information, influence, and affect. We will image these four dynamic networks of relational types as animated, full-color, multi-dimensional visualizations using virtual reality modeling language. Our visualizations will transcend previous VRML network representations by incorporating several critical new functionalities such as: allowing different dimensions to represent network distances for different relations; introducing the ability to"swap"dimensions and so allowing users to envision networks consisting of more than three relational types; and, intelligently guiding the visualization using known properties of various relational types. Additionally, we will leverage inherent information about how rational, cognitively realistic agents negotiate their interactions subject to the"economy of relations"to both visualize and numerically predict future network states so that decision makers not only observe how the network has changed in the past but how it will change in the future as well.