Honors College Thesis


Integrated Framework for Identifying Signal Patterns from Data Applied in a Living System Public Deposited

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  • Various methods, such as CUSUM charts and least square regression, exist in order to help analyze the trend of complex systems. However, the current methods are convoluted and lack accessibility for those without a strong statistical background. To address this disparity, the behavior of living systems first needs to be understood in order to propose a simpler methodology of analyzing them. Living systems are defined as systems that have a natural growth trend and adapt depending on the environment in which they exist. While they are continually evolving, ultimately the growth of living systems over a specific time is a stationary trend as specified by Hamilton’s Principle. The framework proposed in this research combines the premises of Signal Detection Theory and Hamilton’s Principle in order to create a solid foundation. Data were collected on the 2018 midterm elections for the purpose of this research. The data set was analyzed with the proposed methodology in order to determine if it was an adequate means of analysis. It was found that the proposed methodology produced results that are very similar to those of existing methods. The findings from this research support the notion that complex systems can be analyzed with less complex means. The proposed method contributes to reducing the gap between the advanced level of system and statistical knowledge held by scholars and the traditional level held by industry members by simplifying the process of analyzing data obtained from a living system.
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