![]() This five-step management process may be used for issues that are nonprobabilistic in nature, or risks. Figure 2 depicts an overview of the risk and issue management process, which is an organized and iterative decision-making technique designed to improve the probability of project success ( DoD, 2017). For example, the Department of Defense Risk, Issue, and Opportunity Management Guide for Defense Acquisition Programs represents but one of numerous DoD policy and guidance documents that focus on risk management. The Department of Defense (DoD) and the National Aeronautics and Space Administration (NASA) have established risk management processes. Risk management includes a documented process, and both formal and informal practices applied in government programs and commercial industries alike. Finally, the review of the literature concludes with a discussion of the approaches to reduce the cognitive biases. Although the research is tailored to the aerospace sector, important insights from the transportation sector are also considered. Bias enabling conditions in the project environment are also examined, and the characteristics common to both the transportation and aerospace sectors are highlighted. These biases are persistent across industries, individual experts, and teams, and affect humans’ ability to impartially identify and assess risks. Subsequently, the nature of cognitive biases is described. The review of the literature begins with a discussion of risk management in the aerospace sector and a description of the risk identification practices and challenges. The checklist presented herein is grounded in the academic literature surrounding cognitive bias mitigation and, in particular, the Nobel-prize-winning efforts of Kahneman and Tversky (1977) in reference class forecasting. ![]() The research approach is highlighted in Figure 1. Finally, a discussion follows on the checklist implementation and potential challenges. ![]() The authors incorporated the feedback from both the Likert survey and the open-ended questions into the final checklist. Nonetheless, the authors recognize that like any of the measurement methods in the science disciplines, the social or attitude survey method is not error-free ( Fowler, 2013). The survey design, data collection, and analysis followed the academic literature guidelines for garnering attitudes and feedback on the effectiveness of the checklist as a risk management tool. The answers to the open-ended questions of the survey provided insights, lessons learned, as well as other measures that are used by practitioners to reduce biases. A Likert-scale instrument was used for the survey, which is an appropriate instrument for measuring attitudes and beliefs. After the development of the initial checklist, they designed and administered the survey to seek feedback and validation of the checklist as a risk management tool. The authors reviewed and synthesized the bias mitigation literature and developed the initial bias reduction checklist. The biases of optimism, planning fallacy, anchoring, and ambiguity effect were deemed particularly influential to the risk identification and evaluation processes. The authors’ research began with a review of the literature, which covered the areas of risk management, cognitive biases, and bias enabling conditions.
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