Elisa Canzani. (2016). Modeling Dynamics of Disruptive Events for Impact Analysis in Networked Critical Infrastructures. 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: Governments have strongly recognized that the proper functioning of critical infrastructures (CIs) highly determines the societal welfare. If a failed infrastructure is unable to deliver services and products to the others, disruptive effects can cascade into the larger system of CIs. In turn, decision-makers need to understand causal interdependencies and nonlinear feedback behaviors underlying the entire CIs network toward more effective crisis response plans. This paper proposes a novel block building modeling approach based on System Dynamics (SD) to capture complex dynamics of CIs disruptions. We develop a SD model and apply it to hypothetical scenarios for simulation-based impact analysis of single and multiple disruptive events. With a special focus on temporal aspects of system resilience, we also demonstrate how the model can be used for dynamic resilience assessment. The model supports crisis managers in understanding scenarios of disruptions and forecasting their impacts to improve strategic planning in Critical Infrastructure Protection (CIP).
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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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Murray Turoff, Victor A. Bañuls, Linda Plotnick, Starr Roxanne Hiltz, & Miguel Ramirez de la Huerga. (2015). Collaborative Evolution of a Dynamic Scenario Model for the Interaction of Critical Infrastructures. 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 paper reviews current work on a model of the cascading effects of Critical Infrastructure (CI) failures during disasters. Based upon the contributions of 26 professionals, we have created a reliable model for the interaction among sixteen CIs. An internal CI model can be used as a core part of a number of larger models, each of which are tailored to a specific disaster in a specific location.
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Joaquín López-Silva, Victor A. Bañuls, & Murray Turoff. (2015). Scenario Based Approach for Risks Analysis in Critical Infrastructures. 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 paper proposes a Cross Impact Analysis for supporting critical infrastructures risk analysis. This methodology contributes to decision-makers and planners with analytical tools for modeling complex situations. These features are generally useful in emergency management and particularly within the critical infrastructures scope, where complex scenarios for risk analysis and emergency plans design have to be analyzed. This paper will show by an example how CIA methodology can be applied for risks and identification analysis with an application to a Data Centre of a Critical Infrastructure.
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