Addressing Deep Uncertainty in Event-Based Disaster Response Operations
Abstract
Identification of the phases of a large-scale natural disaster is often clouded by classes and sources of deep uncertainty, further proliferating as disaster events unfold. Focusing on three distinct phases of natural disaster relief operations, it is not necessary nor viable to eliminate all uncertainty from a natural disaster system. Instead, reducing the amount of time taken to minimize particular uncertainties may be sufficient to execute the preparation phase to carry out a response. The goal of this research is to understand the intricacies associated with forecastable and rapidonset natural disaster events and restructure already-established tools to assist first responders and relevant decision-makers in the planning and response phases. Understanding specific foraging actions will support the considerations that must be made during the preparation phase while tying in other notable concepts, including use of a problem-structuring technique from the decisionmaking under deep uncertainty literature to contextualize the system of interest. The restructuring of a planning-based to a response-based problem-structuring tool will also highlight the added value in shifting from a static to a dynamic perspective. Following contextualization, utilizing an adaptive pathway approach will serve as a practical decision-support tool, allowing for open and flexible progression through the response phase of a natural disaster as events unfold, inclusive of specific triggers indicating a new event occurrence and thus, a new decision point. This paper addresses conditional criterion-based decision-making, focusing on an adaptive pathways approach in response to flooding incidents.
Degree
M.Sc.
Advisors
Caldwell, Purdue University.
Subject Area
Management|Public administration
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