Ph.D. Thesis Defense

By

Vadim Kim

(Advisor: Prof. Dimitri N. Mavris)

8:00AM, Thursday, July 07, 2016

Weber Space Science and Technology Building (SST-II)

Collaborative Visualization Environment (CoVE)

A Design Space Exploration Method for Identifying Emergent Behavior in Complex Systems

ABSTRACT:

As engineered systems become more complex, their behavior is more difficult to characterize and predict. More critically, complex systems exhibit unexpected behaviors that only become apparent when the system is integrated. This poses an enormous challenge for engineering these systems. In complex systems, there is an emergence of behaviors at higher levels of organization which cannot be predicted at the subsystem level. These unexpected behaviors can be a result of a myriad intricate interdependencies and interactions between components, sensitivity to initial conditions or boundary conditions, enforcement of higher-level constraints on the system, or latent functions or variables in the system. Emergence can be defined as the phenomenon in a complex system that is characterized by unexpected qualitative changes in macro-level behavior due to context-dependence of the micro-level components. The goal of this research is to develop a method for identifying emergent behavior, both beneficial and detrimental, in complex systems.

The research objective is to develop a methodology for evaluating computer simulations of complex systems in order to identify conditions that lead to emergent behavior. This research proposes a new quantitative measure of emergence which can identify critical transitions in macro-level performance/function of the system due to changes in system context (i.e., environmental conditions or system parameters). The methodology provides the framework for performing a design space exploration using this measure of emergence to identify critical regions in the design space. These regions help to characterize the design space and will help guide the design process by providing insight into design points where the system behavior is unexpected or changing rapidly, which are possible indicators of emergent behavior.

The proposed methodology to detect emergence is based on a statistical analysis approach. The design space is efficiently sampled using Design of Experiments methods. At each of these design points, the system behavior is characterized statistically using repeated runs of the simulation. The proposed measure of emergence is then evaluated across the design space and critical regions are identified using data visualization and clustering methods.

A case study is performed on a multi-UAV distributed surveillance problem to investigate whether this framework is capable of identifying emergent behavior. The results show that this methodology provides insights into the landscape of system performance across the design space. It provides for a more rigorous, traceable, and thorough design process for systems which have been difficult to understand and design using traditional engineering methods. This method successfully identifies critical transitions in system behavior in a four-dimensional design space.

Committee Members:

Professor Dimitri N. Mavris

Professor Daniel P. Schrage

Professor Graeme J. Kennedy

Dr. Kelly Griendling

Dr. Shuo-Ju Chou