Utilizing agent based simulation and game theory techniques to optimize an individual’s survival decisions during an epidemic

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dc.contributor.author James, Matthew King
dc.date.accessioned 2012-04-24T19:42:02Z
dc.date.available 2012-04-24T19:42:02Z
dc.date.issued 2012-04-24
dc.identifier.uri http://hdl.handle.net/2097/13636
dc.description.abstract History has shown that epidemics can occur at random and without warning — devastating the populations which they impact. As a preventative measure, modern medicine has helped to reduce the number of diseases that can instigate such an event, nevertheless natural and man-made disease mutations place us continuously at risk of such an outbreak. As a second line of defense, extensive research has been conducted to better understand spread patterns and the efficacy of various containment and mitigation strategies. However, these simulation models have primarily focused on minimizing the impact to groups of people either from an economic or societal perspective and little study has been focused on determining the utility maximizing strategy for an individual. Therefore, this work explores the decisions of individuals to determine emergent behaviors and characteristics which lead to increased probability of survival during an epidemic. This is done by leveraging linear program optimization techniques and the concept of Agent Based Simulation, to more accurately capture the complexity inherent in most real-world systems via the interactions of individual entities. This research builds on 5 years of study focused on rural epidemic simulation, resulting in the development of a 4,000-line computer code simulation package. This adaptable simulation can accurately model the interactions of individuals to discern the impact of any general disease type, and can be implemented on the population of any contiguous counties within Kansas. Furthermore, a computational study performed on the 17 counties of northwestern Kansas provides game theoretical based insights as to what decisions increase the likelihood of survival. For example, statistically significant findings suggest that an individual is four times more likely to become infected if they rush stores for supplies after a government issued warning instead of remaining at home. This work serves as a meaningful step in understanding emergent phenomena during an epidemic which, subsequently, provides novel insight to an individual’s utility maximizing strategy. Understanding the main findings of this research could save your life. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject ABS en_US
dc.subject Agent Based en_US
dc.subject Epidemiology en_US
dc.subject Epidemic en_US
dc.subject Decision Optimization en_US
dc.subject Game Theory en_US
dc.title Utilizing agent based simulation and game theory techniques to optimize an individual’s survival decisions during an epidemic en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Industrial & Manufacturing Systems Engineering en_US
dc.description.advisor Todd Easton en_US
dc.subject.umi Epidemiology (0766) en_US
dc.subject.umi Industrial Engineering (0546) en_US
dc.date.published 2012 en_US
dc.date.graduationmonth May en_US

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