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

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.

Emergency Service Provision Using a Novel Hybrid SOM-Spiral STC Model for Group Decision Support under Dynamic Uncertainty

In emergency scenarios service vehicles must identify potential route(s) and use the best available route. However, route identification requires intelligent decision-support systems which generally use non-traditional approaches with tools characterised by flexible non-hierarchical structures. Conventional models using group decision-support systems have been applied; however, when used in smart urban environments, emergency response services have limitations in their ability to identify unobstructed paths (routes) in dynamic operating environments. In this paper we introduce a novel path planning method for autonomous vehicle control in emergency situations. The proposed model uses self-organizing maps in an integrated Spiral STC algorithm termed the: Hybrid SOM-Spiral STC model which uses hedge algebras and Kansei evaluation in group decision-support. The proposed model has been designed to quantify qualitative factors using sensor derived data processed with human sensibilities and preferences in emergency decision support. The experimental results show that the proposed model achieves significant improvements in group decision-support under dynamic uncertainty. We posit that our novel approach holds the prospect of improvements in the provision of emergency services.

Emergency Service Provision using a Novel Hybrid SOM-Spiral STC Model for Group Decision Support Under Dynamic Uncertainty

In emergency scenarios service vehicles must identify potential route(s) and use the best available route. However, route identification requires intelligent decision-support systems which generally use non-traditional approaches with tools characterised by flexible non-hierarchical structures. Conventional models using group decision-support systems have been applied; however, when used in smart urban environments, emergency response services have limitations in their ability to identify unobstructed paths (routes) in dynamic operating environments. In this paper we introduce a novel path planning method for autonomous vehicle control in emergency situations. The proposed model uses self-organizing maps in an integrated Spiral STC algorithm termed the: Hybrid SOM-Spiral STC model which uses hedge algebras and \emph{Kansei} evaluation in group decision-support. The proposed model has been designed to quantify qualitative factors using sensor derived data processed with human sensibilities and preferences in emergency decision support. The experimental results show that the proposed model achieves significant improvements in group decision-support under dynamic uncertainty. We posit that our novel approach holds the prospect of improvements in the provision of emergency services.

Arguing with Behavior Influence: A Model for Web-Based Group Decision Support Systems

In this work, we propose an argumentation-based dialogue model designed for Web-based Group Decision Support Systems, that considers the decision-makers’ intentions. The intentions are modeled as behavior styles which allow agents to interact with each other as humans would in face-to-face meetings. In addition, we propose a set of arguments that can be used by the agents to perform and evaluate requests, while considering the agents’ behavior style. The inclusion of decision-makers’ intentions intends to create a more reliable and realistic process. Our model proved, in different contexts, that higher levels of consensus and satisfaction are achieved when using agents modeled with behavior styles compared to agents without any features to represent the decision-makers’ intentions.

Effectively Communicating With Group Decision Support Systems Using Information Theory

This chapter describes the results from a case study using information theory to examine the effectiveness of communicating using group decision support system (GDSS) technology. At its most basic level, information theory provides the means to measure the efficiency of communication systems. Using information theory as the theoretical foundation, this chapter examines how the use of GDSS facilitated computer-mediated communication (CMC) for one particular business with respect to entropy, redundancy, and noise, which are key components in information theory.