Decision Models and Group Decision Support Systems for Emergency Management and City Resilience

Decision Models and Group Decision Support Systems for Emergency Management and City Resilience

Yumei Chen, Xiaoyi Zhao, Eliot Rich, Luis Felipe Luna-Reyes
Copyright: © 2018 |Pages: 16
DOI: 10.4018/IJEPR.2018040103
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This paper introduces the concept of Group Decision Support Systems (GDSS) as a tool to support emergency management and resilience in coastal cities. As an illustration of the potential value of GDSS, we discuss the use of the Pointe Claire teaching case. Participants in the exercise work in groups to approach the case using four different computer-supported decision models to explore and recommend policies for emergency mitigation and city resilience. The case, as well as the decision models, can be a valuable GDSS tool, particularly in the mitigation stages of the emergency management cycle. We present preliminary results from the use of the case, models and a simulation environment in a graduate course. We finish the paper by presenting our experience as a framework for building more efficient and secure emergency management systems through the use of GDSS.
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A city’s resilience is its capability to respond rapidly to unforeseen change, even when faced with chaotic disruption. It is the ability to bounce back and move forward with speed, grace, determination and precision (Barishansky, 2015). Resilience is a quality covering the complete emergency management cycle, from the mitigation stages to recovery. Local resiliency with regard to disasters means that a locale is able to withstand an extreme natural event without suffering devastating losses, damage, diminished productivity, or quality of life and without a large amount of assistance from outside the community (Mileti, 1999). A resilient city is a sustainable network of physical systems and human communities (Huber, 1984). Resiliency has been recognized as a key characteristic of a Smart City (Gil-Garcia, Zhang & Puron-Cid, 2016). A city without resilient physical systems will be extremely vulnerable to uncertain and severe events. Thus, cities around the world are establishing emergency response centers as infrastructures to coordinate responses to emergency. Although data and technology infrastructures are critical components in building resiliency, collaboration and the use of decision-making models when anticipating and during incidents becomes also another critical factor to support the cities’ recovery and resumption of sustainable activity.

In this paper, we explore the use of Group Decision Support Systems (GDSS) and facilitated learning to emergency planning and improve city resilience. We posit that GDSS provides methods, models and processes needed to use technology in facilitating problem definition and decision making, and also provide a milieu to build relationships and trust among stakeholders, which constitute prerequisites to effective collaborations (Luna-Reyes, 2013; Vangen & Huxham, 2003). Our main contribution is to propose a framework that integrates the use of GDSS to current data and technology infrastructures to build city resilience, particularly through better trained human resources. The framework provides some conceptualized insight about the ways GDSS can complement planning and action across the emergency management cycle and provide sustainable help in the improvement of city resilience. Given the diversity of natural hazards, we focus on how to improve the resilience of coastal cities to storms and typhoons or hurricanes.

To accomplish this purpose, this paper is organized in five sections including this introduction. The second section includes main concepts of smart cities and city resilience and emergency management, as well as the impact of GDSS on these three areas. Section three includes a description of the Pointe Claire case and companion materials used as a tool to facilitate group decision support. The fourth section introduces preliminary use of the case in the context of a classroom in a school of Public Administration, as well as some preliminary results of its use in this environment. In the discussion and conclusion section, we incorporate our proposed model. Limitations and future research plan are also included in this section.

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