Simulation, social network analysis, system dynamics, discrete simulation, hybrid simulation


Currently the network centric operation and network centric warfare have generated a new area of research focused on determining how hierarchical organizations composed by human beings and machines make decisions over collaborative environments. One of the most stressful scenarios for these kinds of organizations is the so-called extreme events. This dissertation provides a hybrid simulation methodology based on classical simulation paradigms combined with social network analysis for evaluating and improving the organizational structures and procedures, mainly the incident command systems and plans for facing those extreme events. According to this, we provide a methodology for generating hypotheses and afterwards testing organizational procedures either in real training systems or simulation models with validated data. As long as the organization changes their dyadic relationships dynamically over time, we propose to capture the longitudinal digraph in time and analyze it by means of its adjacency matrix. Thus, by using an object oriented approach, three domains are proposed for better understanding the performance and the surrounding environment of an emergency management organization. System dynamics is used for modeling the critical infrastructure linked to the warning alerts of a given organization at federal, state and local levels. Discrete simulations based on the defined concept of "community of state" enables us to control the complete model. Discrete event simulation allows us to create entities that represent the data and resource flows within the organization. We propose that cognitive models might well be suited in our methodology. For instance, we show how the team performance decays in time, according to the Yerkes-Dodson curve, affecting the measures of performance of the whole organizational system. Accordingly we suggest that the hybrid model could be applied to other types of organizations, such as military peacekeeping operations and joint task forces. Along with providing insight about organizations, the methodology supports the analysis of the "after action review" (AAR), based on collection of data obtained from the command and control systems or the so-called training scenarios. Furthermore, a rich set of mathematical measures arises from the hybrid models such as triad census, dyad census, eigenvalues, utilization, feedback loops, etc., which provides a strong foundation for studying an emergency management organization. Future research will be necessary for analyzing real data and validating the proposed methodology.


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Graduation Date





Sepulveda, Jose


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science

Degree Program

Modeling and Simulation








Length of Campus-only Access


Access Status

Doctoral Dissertation (Open Access)

Included in

Engineering Commons