This research investigates the presence of cognitive biases in the risk management processes utilized at NASA, Department of Defense, and within the broader aerospace sector. A widely applied risk management tool in the aerospace sector is the risk matrix. Risk matrices were used in practice across the historical projects sampled for this research and were also utilized as part of the current data collection and assessment process. However, risk matrices have come under criticism particularly with respect to their reliance on subjective judgment, which is known to be susceptible to many forms of human cognitive biases. The research was conducted through a two-part experiment study. In part I of the research, the author examined empirical data from the risk matrices on risk identification and evaluation for twenty-eight robotic space projects. Through a review of the literature hypotheses covering cognitive biases were developed. These hypotheses proposed that cognitive biases such as optimism, planning fallacy, anchoring and ambiguity effect have a statistically significant influence on the risk identification and analysis processes used in the aerospace sector. Data for the hypotheses testing are in the form of hundreds of identified and estimated risks across the twenty-eight projects, which were assessed in categories relative to the manifested cost change impacts in the same categories. The thesis uses statistical analysis to assess, test and confirm these hypotheses. External sociopolitical and environmental risks such as Congressional budget action and agency policy actions were also confirmed to be under-represented, and under-valued in the risk management process relative to their ultimate manifested cost impact. This work also further illustrated some of the limitations and ambiguity issues in the application of the risk matrix. In part II, this research contributes an operational checklist for mitigating cognitive biases in the aerospace sector risk management process. The Risk Identification and Evaluation Bias Reduction Checklist includes steps for grounding the risk identification and evaluation activities in past project experiences, through historical data, and the importance of incorporating multiple methods and perspectives to guard against optimism and a singular project instantiation focused view. A survey is used to elicit subject matter expert (SME) judgment on the value of the checklist to support its use in government and industry as a risk management tool. The survey also provided insights on bias mitigation strategies and lessons learned. This final checklist addresses the deficiency in the literature in providing operational steps for the practitioner for bias reduction in risk management in the aerospace sector.
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