Cellular automata, socio economic factors, adverse events, modelling and simulation


Department of Defense (DoD) implemented Human Social Cultural and Behavior (HSCB) program to meet the need to develop capability to understand, predict and shape human behavior among different cultures by developing a knowledge base, building models, and creating training capacity. This capability will allow decision makers to subordinate kinetic operations and promote non-kinetic operations to govern economic programs better in order to initiate efforts and development to address the grievances among the displeased by adverse events. These non-kinetic operations include rebuilding indigenous institutions’ bottom-up economic activity and constructing necessary infrastructure since the success in non-kinetic operations depends on understanding and using social and cultural landscape. This study aims to support decision makers by building a computational model to understand economic factors and their effect on adverse events. In this dissertation, the analysis demonstrates that the use of cellular automata has several significant contributions to support decision makers allocating development funds to stabilize regions with higher adverse event risks, and to better understand the complex socio-economic interactions with adverse events. Thus, this analysis was performed on a set of spatial data representing factors from social and economic data. In studying behavior using cellular automata, cells in the same neighborhood synchronously interact with each other to determine their next states, and small changes in iteration may yield to complex formations of adverse event risk after several iterations of time. The modeling methodology of cellular automata for social and economic analysis in this research was designed in two major implementation levels as follows: macro and micro-level. In the macro-level, the modeling framework integrates iv population, social, and economic sub-systems. The macro-level allows the model to use regionalized representations, while the micro-level analyses help to understand why the events have occurred. Macro-level subsystems support cellular automata rules to generate accurate predictions. Prediction capability of cellular automata is used to model the micro-level interactions between individual actors, which are represented by adverse events. The results of this dissertation demonstrate that cellular automata model is capable of evaluating socio-economic influences that result in changes in adverse events and identify location, time and impact of these events. Secondly, this research indicates that the socioeconomic influences have different levels of impact on adverse events, defined by the number of people killed, wounded or hijacked. Thirdly, this research shows that the socio-economic, influences and adverse events that occurred in a given district have impacts on adverse events that occur in neighboring districts. The cellular automata modeling approach can be used to enhance the capability to understand and use human, social and behavioral factors by generating what-if scenarios to determine the impact of different infrastructure development projects to predict adverse events. Lastly, adverse events that could occur in upcoming years can be predicted to allow decision makers to deter these events or plan accordingly if these events do occur.


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Graduation Date





Karwowski, Waldemar


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science


Industrial Engineering and Management Systems

Degree Program

Industrial Engineering








Release Date

August 2013

Length of Campus-only Access


Access Status

Doctoral Dissertation (Open Access)


Dissertations, Academic -- Engineering and Computer Science, Engineering and Computer Science -- Dissertations, Academic