Interorganizational Information Exchange and Efficiency: Organizational Performance in Emergency Environments

Achieving efficiency in coordinated action in rapidly changing environments has challenged both researchers and practitioners. Emergency events require both rapid response and effective coordination among participating organizations. We created a simulated operations environment using agent-based modeling to test the efficiency of six different organizational designs that varied the exercise of authority, degree of uncertainty, and access to information. Efficiency is measured in terms of response time, identifying time as the most valuable resource in emergency response. Our findings show that, contrary to dominant organizational patterns of hierarchical authority that limit communication among members via strict reporting rules, any communication among members increases the efficiency of organizations operating in uncertain environments. We further found that a smaller component of highly interconnected, self adapting agents emerges over time to support the organization\'s adaptation in changing conditions. In uncertain environments, heterogeneous agents prove more efficient in sharing information that guides coordination than homogeneous agents.Agent-Based Simulation, Emergency Management, Network Evolution, Performance

Local Probability Distributions in Bayesian Networks: Knowledge Elicitation and Inference

Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowledge and have been applied successfully in many domains for over 25 years. The strength of Bayesian networks lies in the graceful combination of probability theory and a graphical structure representing probabilistic dependencies among domain variables in a compact manner that is intuitive for humans. One major challenge related to building practical BN models is specification of conditional probability distributions. The number of probability distributions in a conditional probability table for a given variable is exponential in its number of parent nodes, so that defining them becomes problematic or even impossible from a practical standpoint. The objective of this dissertation is to develop a better understanding of models for compact representations of local probability distributions. The hypothesis is that such models should allow for building larger models more efficiently and lead to a wider range of BN applications

The SERIES model: development of a practitioner focused emergency response evaluation system

Purpose - Effective Emergency Response Management (ERM) system evaluation is vital to the process of continual improvement within emergency response organizations. The purpose of this paper is to investigate if an entire ERM system can be captured and encoded within a standardized framework. Design/Methodology/Approach - Employing an exploratory approach we apply a mixed methods case study design and inductive reasoning to analyse documentary evidence provided during the inquest into the London Bombings 2005. We use content analysis to investigate the nature of ERM system data availability and apply principals of Network Theory to iteratively develop a framework within which data can be encoded. Findings - We find that complex ERM system data can be captured and stored within a standardized framework. We present a conceptual framework and multi-stage mixed methods process, the Standardized Emergency Response Incident Evaluation System (SERIES) model, to support data collection, storage and interpretation. Our findings demonstrate that ERM system evaluation can benefit from the adoption of a standardized mixed-methods approach employing data transformation and triangulation. We also demonstrate the potential of the proposed standardized model, by integrating qualitative and quantitative data, to support interpretation and reporting through the use of appropriate data visualization. Originality / Value – The SERIES model provides a practical tool and procedural guidelines to capture and share vital ERM system data and information across all emergency services. It also presents an opportunity to develop a large comprehensive multi-incident dataset to support academic inquiry and partnership between academics and practitioners

Information Systems for Crisis Management - Current Applications and Future Directions

The management of crisis situations is undergoing rapid changes due to advances of Information Technology. We discuss the role of information systems in supporting decision making processes in crisis management. The crisis management poses particular challenges for information systems - it is characterized by dynamic, complex environments involving many actors in often very unique and extreme situations. We review the roles, benefits and challenges of information systems' application to different phases of the crisis management. We argue that for the different phases of the crisis management, the different types of information systems varying in the aspects such as use scenarios, user requirements, critical technologies, etc. are suitable. Finally, we outline the directions for future research and applications for the field of the support systems for the emergency management

Modelling Vulnerability of Transportation Network Using Influence Diagrams

The transportation network (TN) is important to serve the national priorities such as economic sustainability and growth, but it as well plays important role in disaster management and is subject to hazards. The understanding of the TN resilience and vulnerabilities is important for the national security. In this work we focus on the notion of TN vulnerability. We propose the use of a decision-theoretic approach based on Influence Diagrams (IDs). We argue that the use of IDs (1) provides an improved framework for risk assessment through more elaborate combining probabilities and consequences, and (2) facilitates knowledge elicitation from human experts through a structured approach to the problem. The proposed methodology is intended to be applied to the critical infrastructure

Analytics for Protecting Critical Infrastructure

In this paper we review the key trends related to application of information technology and, in particular, automated data analysis to the problem of protecting critical infrastructure. We focus on technologies that use automated data collection and analyses that can be exploited for improving security provision for critical infrastructure in the future. Of our particular interest are technologies that are at relatively early stage of adaptation and in our judgement have potential to significantly affect how the security is provided in the future, the technologies that support physical aspect of security. Then we discuss analytics in context of cyber-security with applications