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This thesis examines investigative decision making, cognitive biases, talent sharing, and the relationship between the random nature of lone-actor violence and a set of predefined decision-making protocols. This research included running four simulations using the Monte Carlo technique, which illustrated that with the dedication of additional resources came a concomitant effect of diminishing returns, opportunity cost, and exposure to liability. The simulations also suggested that regardless of an investigative agency’s decision-making processes, the outcome relies on the randomness of the event. To demonstrate a prototype for a new method of threat analysis, a superforecasting team of analysts participated in an experimental survey. Nine participants reviewed five threat scenarios and assigned a score based on factors including the potential for violence and immediacy of the threat. Analysis in the survey was accurate for four out of five scenarios. Survey participants also answered six prospect theory questions, set in a homeland security context, to assess their decision making under uncertainty. Considered together, the results from the simulations and the two-part survey explain the relative strength of certain threat assessments. They distinguish what may be detectable from what is statistically unpredictable through the use of a collaborative and multidisciplinary method of analysis.

Over the course of several years, the Department of Homeland Security (DHS) has worked diligently to improve the quality of its leaders. Such efforts have focused almost exclusively on initiating or expanding programs related to leadership development. To date, the impact of that exertion might be charitably described as tepid. While the issues associated with existing leaders have received ample attention, the selection process that precipitated them has not. This gap represents an opportunity to explore a nascent space and suggest new solutions that target the problem at the source. This thesis examines the process of leadership selection at a network level and finds several systemic problems related to measurement, structure, and decision-making. These problems bear a striking resemblance to those observed in the intelligence community and its ability to accurately predict complex future geopolitical events. One method that has dramatically improved the accuracy of geopolitical predictions is superforecasting. At its core, leadership selection is a prediction or a forecast. It is an educated but nonetheless imperfect best guess about how a candidate observed today will perform tomorrow. These features collectively suggest a novel question. Could DHS use a superforecasting methodology to improve its leadership selection process? This thesis follows the progression of that question to an unexpected destination and offers several concrete recommendations.

Over the course of several years, the Department of Homeland Security (DHS) has worked diligently to improve the quality of its leaders. Such efforts have focused almost exclusively on initiating or expanding programs related to leadership development. To date, the impact of that exertion might be charitably described as tepid. While the issues associated with existing leaders have received ample attention, the selection process that precipitated them has not. This gap represents an opportunity to explore a nascent space and suggest new solutions that target the problem at the source. This thesis examines the process of leadership selection at a network level and finds several systemic problems related to measurement, structure, and decision-making. These problems bear a striking resemblance to those observed in the intelligence community and its ability to accurately predict complex future geopolitical events. One method that has dramatically improved the accuracy of geopolitical predictions is superforecasting. At its core, leadership selection is a prediction or a forecast. It is an educated but nonetheless imperfect best guess about how a candidate observed today will perform tomorrow. These features collectively suggest a novel question. Could DHS use a superforecasting methodology to improve its leadership selection process? This thesis follows the progression of that question to an unexpected destination and offers several concrete recommendations.

Over the course of several years, the Department of Homeland Security (DHS) has worked diligently to improve the quality of its leaders. Such efforts have focused almost exclusively on initiating or expanding programs related to leadership development. To date, the impact of that exertion might be charitably described as tepid. While the issues associated with existing leaders have received ample attention, the selection process that precipitated them has not. This gap represents an opportunity to explore a nascent space and suggest new solutions that target the problem at the source. This thesis examines the process of leadership selection at a network level and finds several systemic problems related to measurement, structure, and decision-making. These problems bear a striking resemblance to those observed in the intelligence community and its ability to accurately predict complex future geopolitical events. One method that has dramatically improved the accuracy of geopolitical predictions is superforecasting. At its core, leadership selection is a prediction or a forecast. It is an educated but nonetheless imperfect best guess about how a candidate observed today will perform tomorrow. These features collectively suggest a novel question. Could DHS use a superforecasting methodology to improve its leadership selection process? This thesis follows the progression of that question to an unexpected destination and offers several concrete recommendations.