Complex and emergent behavior modeling

In existing attempts to model complex systems, one critical aspect that has not been clearly ad-dressed involves the underlying mechanism for integrating the numerous “pieces” and “parts” that make up the target. Combining pieces is the process of aggregation and must handle inconsistencies among the pieces. Combining parts is the process of composition in which the parts are encapsulations of information with a set of meaningful operations defined on them. Parts are functional in nature and thus are driven by function composition. Extant research has not directly addressed this resulting in mathematically ad-hoc models opaque to analysis. We propose to develop a singular, rigorous, comprehensive computation framework that is axiomatic and provides the capabilities needed to model complex systems based on a new model of complex adaptive Bayesian Knowledge Bases and a novel, powerful analytical framework capable of wholistic end-to-end quantitative analysis of performance, robustness, vulnerability, and impacts of change on our targets being modeled. Furthermore, our results will be applicable to numerous domains of public purpose from crisis and catastrophe management for natural disasters and disease outbreaks to assessing the well-being of our financial system and national infrastructures.

Faculty contact: Eugene Santos Jr.