System dynamics can be a very useful tool to expand the boundaries of one's mental models to better understand the underlying behavior of systems. But despite its utility, there remains challenges associated with system dynamics modeling that the current research addresses by expanding the system dynamics modeling toolbox. The first challenge relates to imprecision or vagueness, for example, with respect to human perception and linguistic variables. The most common approach is to use table or graph functions to capture the inherent vagueness in these linguistic (qualitative) variables. Yet, combining two or more table functions may lead to further complexity and, moreover, increased difficulty when analyzing the resulting behavior. As part of this research, we extend the system dynamics toolbox by applying fuzzy logic. Then, we select a problem of congestion pricing in mitigating traffic congestion to verify the effectiveness of our integration of fuzzy logic into system dynamics modeling.
Another challenge, in system dynamics modeling, is defining proper equations to predict variables based on numerous studies. In particular, we focus on published equations in models for energy balance and weight change of individuals. For these models there is a need to define a single robust prediction equation for Basal Metabolic Rate (BMR), which is an element of the energy expenditure of the body. In our approach, we perform an extensive literature review to explore the relationship between BMR and different factors including age, body composition, gender, and ethnicity. We find that there are many equations used to estimate BMR, especially for different demographic groups. Further, we find that these equations use different independent variables and, in a few cases, generate inconsistent conclusions. It follows then that selecting a single equation for BMI can be quite difficult for purposes of modeling in a systems dynamics context. Our approach involves conducting a meta-regression to summarize the available prediction equations and identifying the most appropriate model for predicting BMR for different sub-populations. The results of this research potentially could lead to more precise predictions of body weight and enhanced policy interventions to help mitigate serious health issues such as obesity. / Ph. D.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/77330 |
| Date | 26 April 2012 |
| Creators | Seyed Zadeh Sabounchi, Nasim |
| Contributors | Industrial and Systems Engineering, Rahmandad, Hazhir, Taylor, G. Don, Sarangi, Sudipta, Triantis, Konstantinos P. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation, Text |
| Format | application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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