Browsing by Subject "Risk detection"

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Decision analysis and risk management : application to climate change and risk detection
(2011-08) Agrawal, Shubham; Bickel, J. Eric; Bickel, J. Eric; Morton, David
We have analyzed the application of decision analysis and risk management tools to solve practical problems associated with Climate Change and Risk Detection in the financial services industry. Geoengineering, which is described as an intentional modification of earth’s environment to mitigate the harmful effects of climate change, is evaluated as a policy alternative using the aforementioned tools. We compared the performance of geoengineering with optimal emission controls and a business as usual strategy under various scenarios and found that geoengineering passes the cost benefit test for a majority of the scenarios. We modified the DICE model (Nordhaus, 2008) and used it to evaluate the performance of different environmental policies. Our results show geoengineering as a potential alternative to solve climate change problems. Through this application, and by comparing our findings against Goes et al. (2011), we showed that how framing of the decision problem can lead to completely different results. We also analyzed the application of risk management in the financial services industry. The industry faces three main types of risk: Market risk, Credit risk and Operational risk. Market risk is managed using a diversified portfolio, derivatives, insurance and contracts. More challenging is the task of preventing credit and fraud risk. Statistical models used by the industry to detect and prevent these types of risk are explained in the thesis.
Phenotyping perinatal depression : exploring interactions among biopsychosocial and behavioral determinants
(2023-08-16) Longoria, Kayla D.; Wright, Michelle (Michelle Lynn), 1979-; Walker, Lorraine Olszewski; Widen, Elizabeth; Kessler, Shelli; Hsu, Kean
Perinatal depression is complex, and the etiology remains poorly understood which likely contributes to its underdiagnosis and being a leading cause of maternal mortality. This dissertation used a mixed methods approach to explore biopsychosocial and behavioral factors influencing perinatal depression and aimed to generate evidence for the development of a comprehensive risk phenotype. The systematic review indicated a total of 14 categories of determinants were investigated: biological (5), behavioral (4), social and environmental (5). Though only 28% of studies simultaneously considered determinants under more than one domain, a pattern of interactions with the tryptophan pathway emerged once determinants across domains were aggregated. Methodological limitations related to depression measures and biospecimen collection were identified. Qualitative findings indicated a network of structural factors act as barriers or facilitators to psychological well-being during the perinatal period. Social support, physical health, and logistics were commonly perceived as barriers during pregnancy, while physical health, logistics, and physical activity were seen as facilitators. In the postpartum period, barriers included logistics, social support, and physical health while the most common facilitators were social support, logistics, and nutrition. Mothers reported feelings of loneliness, isolation, worry, anxiety, frustration, and mental fatigue when facing barriers, which were heightened postpartum due to unmet expectations of increased support. Conversely, facilitators improved mothers’ ability to cope with stress and helped mothers find positive aspects about their situation when faced with challenges in motherhood. The metabolomics pilot study found gut gamma-aminobutyric acid (GABA) to be significantly lower postpartum compared to levels in the second and third trimesters. However, correlations among changes in individual pregnancy metabolites and postpartum depression were weak and no predictive models were significant suggesting individual metabolites may not serve as robust predictors of postpartum depression. These findings support perinatal depression is a multifactorial condition, and exploration of complex interactions between biopsychosocial and behavioral factors may provide a more holistic understanding of the pathophysiology. Thus, future research considering a comprehensive panel of variables in large and diverse samples is needed to replicate and build upon the present findings and further refine the phenotypic characteristics of perinatal depression described in this dissertation.