Ph.D. Defense: Eric J. Inclan

Tue Apr 13 2021 11:00 AM
"A Method for System of Systems Definition and Modeling Using Patterns of Collective Behavior"

You're invited to attend


Ph.D. Defense




Eric J. Inclan

(Advisor: Prof. Dimitri Mavris)



"A Method for System of Systems Definition and
Modeling Using Patterns of Collective Behavior"



Tuesday, April 13
11:00 a.m. (EST)



The Department of Defense ship and aircraft acquisition process, with its capability-based assessments and fleet synthesis studies, relies heavily on the assumption that a functional decomposition of higher-level system of systems (SoS) capabilities into lower-level system and subsystem behaviors is both possible and practical. However, SoS typically exhibit “non-decomposable” behaviors for which no such representation exists. The International Council on Systems Engineering has identified the development of methods for predicting and managing non-decomposable behaviors as one of the top research priorities for the Systems Engineering profession. Therefore, this thesis develops a method for rendering non-decomposable, quantifiable SoS properties and behaviors traceable to patterns of interaction of their constitutive systems, so that exploitable patterns identified during the early stages of design can be accounted for. This method is designed to fill two gaps in the literature. First, the lack of an approach for mining data to derive a model (i.e. an equation) of the non-decomposable behavior. Second, the lack of an approach for qualitatively and quantitatively associating non-decomposable behaviors with the components that cause the behavior.

In order to facilitate the development of this method, this research relies on a model-based framework. Systems that self-organize are determined to have a physical structure that is non-decomposable (this includes physical, chemical, and biological systems, as well as many military conflicts and some man-made systems). The first hypothesis proposed in this thesis is that self-organized structure implies the presence of data compression, and this compression can be used to explicitly calculate an upper bound on the number of non-decomposable behaviors that a system can possess. Non-decomposable behaviors are referred to as emergent behaviors. This thesis outlines and tests a set of numerical criteria for detecting (weak and functional) emergent behavior.

This thesis then applies the method to a simulated flock of birds, a notional aerial combat model, and simulated swarms of unmanned quadcopter drones. It is shown that targeting the system-level properties of these self-organized systems can be more effective than affecting any given component of the system, according to a problem-specific measure of merit.  Using the method developed in this thesis, exploitable properties are identified and component behaviors are modified to attempt the exploit. Overall, the method is shown to be an effective, systematic approach to non-decomposable behavior exploitation, and an improvement over the modern, largely ad hoc approach.



  • Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)
  • Dr. Michael J. Steffens – School of Aerospace Engineering
  • Prof. Daniel P. Schrage – School of Aerospace Engineering
  • Dr. Jean Charles Domerçant – Georgia Tech Research Institute
  • Dr. Santiago Balestrini-Robinson – Georgia Tech Research Institute