Lida Huang, Guoray Cai, Hongyong Yuan, Jianguo Chen, Yan Wang, & Feng Sun. (2018). Modeling Threats of Mass Incidents Using Scenario-based Bayesian Network Reasoning. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 121–134). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: Mass incidents represent a global problem, putting potential threats to public safety. Due to the complexity and uncertainties of mass incidents, law enforcement agencies lack analytical models and structured processes for anticipating potential threats. To address such challenge, this paper presents a threat analysis framework combining the scenario analysis method and Bayesian network (BN) reasoning. Based on a case library
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Victor A. Bañuls, Cristina López-Vargas, Fernando Tejedor, Murray Turoff, & Miguel Ramirez de la Huerga. (2016). Validating Cross-Impact Analysis in Project Risk Management. In A. Tapia, P. Antunes, V.A. Bañuls, K. Moore, & J. Porto (Eds.), ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management. Rio de Janeiro, Brasil: Federal University of Rio de Janeiro.
Abstract: Companies work increasingly more on projects as a means of executing organizational decisions. However, too many enterprise projects result in failure. Hence, firms should follow a risk management method that drives their projects toward success. Nevertheless, project managers often deal with risks intuitively. This is partly because they lack the proper means to correctly manage the underlying risks which affect the entire cycle of their projects. Therefore, one purpose is to identify the critical events that managers may encounter before the beginning of the project and during its development. In addition, we propose CIA-ISM to represent existing relationships between the unforeseen events in the project?s lifetime and their key performance indicators. This also predicts the influence of risks on project performance over time by means of scenarios. The tool proposed would thus help practitioners to manage enterprise projects risks in a more effective and proactive way. We have validated the predictive capability of the CIA-ISM model with 22 real projects. The results show a high level of predictive capability in terms of risk analysis and key performance indicators.
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Miguel Ramirez de la Huerga, Victor A. Bañuls, & Murray Turoff. (2015). A Scenario-based approach for analyzing complex cascading effects in Operational Risk Management. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: This is the first paper to apply Cross Impact Analysis (CIA) and Interpretative Structural Modeling (ISM) methods for analyzing complex cascading effects in Operational Risk Management in an industrial environment. Its main objective is to improve the understanding of the overall picture of an organization?s risks. The paper summarizes the development of a CIA-ISM method of the interaction of 18 critical events of an industrial plant as a first step to improving organizational resilience based on the company?s own estimations as well as the estimates of a panel. The main benefit of using these methods is to know the relationships between different risks and consequences, direct links, indirect and cascading effects. Having the possibility of knowing a full risk map and being able to make a forecast will help to mitigate the unexpected effects and have a better response after an emergency situations is the same as being more resilient.
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Alayne Da Costa Duarte, Marcos R. S. Borges, Jose Orlando Gomes, & De Paulo V. R. Carvalho. (2013). ASC model: A process model for the evaluation of simulated field exercises in the emergency domain. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 551–555). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The undefined flow of execution of activities in an evaluation process hampers its implementation. A consistent evaluation process defines interrelated methodological steps that make it easier for the evaluator to lead the process. This article presents a process model for the evaluation of simulated field exercises in the emergency domain, including their sub processes and activities. The proposed model was derived from observations made during real situations of a simulated evacuation exercise of communities in high-risk areas in Rio de Janeiro (Brazil). The motivation came from the finding that the assessment of simulated field exercises is conducted by completing an activity report that does not follow a structural model, an evaluation program or a formal standard. The results of this research show the experts' satisfaction with the application of the model proposed for the development of an evaluation process. The same occurs when comparing to reports currently used by them for this purpose.
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Victor A. Bañuls, Murray Turoff, & Joaquin Lopez. (2010). Clustering scenarios using cross-impact analysis. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Scenarios are frequently used in Emergency Planning and Preparedness. These scenarios are developed based on the hypothesis of occurrence or not of significant events. This is a complex process because of the interrelations between events. This fact, along with the uncertainty about the occurrence or non-occurrence of the events, makes the scenario generation process a challenging issue for emergency managers. In this work a new step-by-step model for clustering scenarios via cross-impact is proposed. The authors. proposal adds tools for detecting critical events and graphical representation to the previous scenario-generation methods based on Cross-Impact Analysis. Moreover, it allows working with large sets of events without using great computational infrastructures. These contributions are expected to be useful for supporting the analysis of critical events and risk assessment tasks in Emergency Planning and Preparedness. Operational issues and practical implications of the model are discussed by means of an example.
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