Decision Analytics, Machine Learning, and Field Experimentation for Defense and Emergency Response
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ItemObservations on the Effects of a Global Pandemic on the Time To Recovery (TTR) from Natural Disasters( 2021-01-05)Until the global outbreak of Coronavirus 2019 (COVID-19), little attention had been paid to the possibility that a significant number of critical personnel in both the infrastructure and disaster response and recovery supply chains could be incapacitated or otherwise unavailable due to an on-going pandemic. The purpose of this paper is to use CRISIS, an existing decision-support optimization tool for the restoration of civil infrastructure damaged by a hurricane, to investigate how a community’s Time To Recovery (TTR) following a hurricane could be extended due to an on-going pandemic and what the consequences could be. The results of preliminary modeling presented here suggests that the impacts could be significant and that our current understanding of such compound extreme events is inadequate to the potential threat.
ItemDefining Boundary Protection in Complexity Leadership Theory Through Fitness in Network Node Theory Approach( 2021-01-05)Complexity Leadership Theory (CLT) discussion revolves around the distribution of leadership authority and execution across the organization. Leadership must be distributed across the organization, leveraging all available expertise in decision-making and direction. Jerry Hazy and Mary Uhl-Bien developed three tenants of Complexity Leadership and refined them into five functions in their work (Hazy, Uhl-Bien, 2013). From these parts emerges a greater and more whole system. That system now includes Organizational Boundary Protection which, we believe, is the missing part of the Complexity Leadership Theory. This study works to explain that organizational boundary definition and protection is identified at the organizational system level. By leveraging Barabasi’s concept that network nodes are developed by fitness (Barabasi, 2014), we find that fitness is aligned to organizational priorities as defined in the mission or implied in its culture. This study begins from the accepted position that a Pareto Power Law distribution (commonly known as the 80/20 Rule) (Barabasi, 2014) should explain the ideal execution of tasking in an organization. That is to say that ideally an organization that aligns with complexity leadership theory and utilizes a distributed decisionmaker process executes work to a Pareto distribution: 20 percent of incoming tasking (information) is important to the organization and absorbs 80 percent of the organization’s resources and effort (transformed into organizational knowledge). Accordingly, the collective decision-makers should commit 20 percent of their time dispatching the 80 percent of inconsequential tasks (information that will not be transformed into organizational knowledge). To gain some early insight on this potential phenomena, this study collected a medium-size organization’s e-mail volumes and a qualitative self-assessment by e-mail recipients on the value of the information provided by the mail. The hypothesis of this study is that there will be a delta between the ideal Pareto Power Law distribution and the organization’s distribution. The study assesses that this delta is a measure of the organizations knowledge processing inefficiency. Finally, the study attempts a first-order validation of this hypothesized inefficiency through an online workforce survey. The survey participants are further categorized by level of experience and organizational position to determine the impact of these factors.