Aarland, M., Radianti, J., & Gjøsæter, T. (2023). Using System Dynamics to Simulate Trust in Digital Supply Chains. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 516–529). Omaha, USA: University of Nebraska at Omaha.
Abstract: The power industry is outsourcing and digitalising their services to provide better, faster, and more reliable supply of electric power to the society. As a result, critical infrastructure increases in complexity and tight couplings between multiple suppliers and systems in digital supply chains. It also introduces new risks and challenges that are difficult to manage for critical infrastructure owners. To address the vulnerability in digital supply chains, we have developed a system dynamics model that represent important challenges to manage cybersecurity in digital supply chains, based on input from an expert group in the power industry. The system dynamics model illustrates how trust in suppliers as well as the need for control play important roles in outsourcing. Scenarios were developed and simulated.
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Abdelgawad, A. A. (2023). An Updated System Dynamics Model for Analysing the Cascading Effects of Critical Infrastructure Failures. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 595–608). Omaha, USA: University of Nebraska at Omaha.
Abstract: Aiming at examining the cascading effects of the failure of Critical Infrastructure (CI), this work-in-progress research introduces an improved System Dynamics model. We represent an improvement over the previous models aimed at studying CIs interdependencies and their cascading effects. Our model builds on earlier models and corrects their flaws. In addition to introducing structural enhancements, the improvements include using unpublished data, a fresh look at a previously collected dataset and employing a new data processing to address and resolve some longstanding issues. The dataset was fed to an optimisation model to produce a new dataset used in our model. The structure of our SD model, its dataset and the data processing techniques we employed to create this dataset are all described in the study. Although the model has passed the fundamental validation criteria, more validation testing and scenario exploration are yet to be conducted.
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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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Jose J. Gonzalez, Geir Bø, & John Einar Johansen. (2013). A system dynamics model of the 2005 hatlestad slide emergency management. 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. 658–667). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: It has long been recognized that the management of emergencies requires that response organisations act flexibly, becoming an “emergent organisation” to better manage the fact that disasters do not follow scripts. Nevertheless, recent research shows that crisis response organisations prefer to follow patterns adequate for normal situations. Arguably, the resistance to become an emergent organisation could be related to poor understanding of how to move from disorganisation to self-organisation. We extend a recent system dynamics work by Tu, Wang and Tseng, describing the transition from disorganisation to self-organisation in the Palau case, to analyse the management of disorganisation in the fatal Hatlestad landslide in Norway. We suggest that the causal structure of the system dynamics model describing the Palau and the Hatlestad case should be considered a candidate for an emergent “middle-range theory” describing the management of disorganisation in emergencies. We propose specific data collection to test the candidate theory.
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Starr Roxanne Hiltz, Jose J. Gonzalez, & Murray Turoff. (2013). ICT support and the effectiveness of decision making in disasters: A preliminary system dynamics model. 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. 668–673). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: A high level conceptual model is presented of factors hypothesized to be key determinants of the effectiveness of decision making in large scale disasters, grounded in the literature on disaster management. ICT robustness (including the use of social media) sensemaking, and the effectiveness of decision making processes by the multi-organizational Partially Distributed Teams that must cooperate are accorded key roles in the process model. The outcomes of the decision making processes modeled are decisions, in terms of timeliness and quality.
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Juan Francisco Carías, Leire Labaka, Jose Maria Sarriegi, Andrea Tapia, & Josune Hernantes. (2019). The Dynamics of Cyber Resilience Management. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: With the latent problem of security breaches, denial of service attacks, other types of cybercrime, and cyber incidents in general, the correct management of cyber resilience in critical infrastructures has become a high priority. However, the very nature of cyber resilience, requires managing variables whose effects are hard to predict, and that could potentially be expensive. This makes the management of cyber resilience in critical infrastructures a substantially hard task.
To address the unpredictability of the variables involved in managing cyber resilience, we have developed a system dynamics model that represents the theoretical behaviors of variables involved in the management of cyber resilience. With this model, we have simulated different scenarios that show how the dynamics of different variables act, and to show how the system would react to different inputs.
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Leire Labaka, Josune Hernantes, Ana Laugé, & Jose Mari Sarriegi. (2011). Three units of analysis for Crisis Management and Critical Infrastructure Protection. In E. Portela L. S. M.A. Santos (Ed.), 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011. Lisbon: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Society's welfare is very dependent on the effective performance of Critical Infrastructure (CI). Nowadays, CI constitutes a network of interconnected and interdependent entities. This means that a serious event in one CI can originate cascading events in the rest, leading to a serious crisis. As a consequence, Crisis Management (CM) and Critical Infrastructure Protection (CIP) should converge and integrate their findings, providing a more unified approach. One relevant issue when developing integrated CM/CIP research is what type of unit of analysis should be used, as it conditions the research objectives and questions. This paper presents an analysis of three different units of analysis used in CM research, focusing on the research objectives and questions used in them. These three different units of analysis have been used in a European CIP research project where three simulation models have been developed based on these three units of analysis.
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Mihoko Sakurai, Jose J Gonzalez, Richard T. Watson, & Jiro Kokuryo. (2016). A Capital Model for Disaster Resilience. 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: This paper proposes a capital model for disaster resilience. A central notion to this effect is viewing an organization as a capital conversion and capital creation system (Mandviwalla et al. 2014). Systems resilience was originally defined as the measure of a system?s persistence and ability to absorb disturbances (Holling 1973). Our approach corresponds to ?resilience-1; Resilience as rebound from trauma and return to equilibrium as,? which according to Woods (2015) is one of the four main categories of disaster resilience. We develop a system dynamics model expressing the main features observed in selected municipalities affected by the Great East Japan Earthquake. We show that the model is able to describe qualitatively the processes of capital destruction by the earthquake with the associated tsunami and the subsequent capital recreation. We discuss how the system dynamics model can be used to further increase our understanding of capital conversion processes in disaster resilience.
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Elio Rich. (2006). Modeling risk dynamics in e-operations transitions. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 239–250). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Migrations to new modes of operation are perilous times for most organizations. For firms that routinely work in high-threat, high-reward situations, the risks of innovation are particularly challenging. This paper presents a systems-based approach to understanding these risks, drawing examples from one firm migrating to e-Operations for offshore oil platforms to increase profitability. The firm recently participated in two facilitated group model building exercises to examine the effects of the migration on the organization and resources needed to safely implement multiple changes over time. Based on these exercises, a simulation model of the timing and relative levels of risk, was developed. The results of the workshop and simulation demonstrate the effect of a combined qualitative and quantitative modeling approach to understanding complex problems.
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Shangde Gao, Yan Wang, & Lisa Platt. (2021). Modeling U.S. Health Agencies' Message Dissemination on Twitter and Users' Exposure to Vaccine-related Misinformation Using System Dynamics. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 333–344). Blacksburg, VA (USA): Virginia Tech.
Abstract: This research intends to answer: how do (i) generation frequency and (ii) retweeting count of health agencies' messages impact the exposure of the general users to vaccine-related misinformation on Twitter? We creatively employed a Susceptible-Infected-Recovered (SIR) System Dynamics paradigm to model interactions between message dissemination of 168 U.S. health agencies and proportions of users who are at different exposure statuses to misinformation, namely “Susceptible”, “Infected”, or “Recovered” status. The SIR model was built based on the vaccine-relevant tweets posted over November and December in 2020. Our preliminary outcomes suggest that augmenting the generation frequency of agencies' messages and increasing retweeting count can effectively moderate the exposure risk to vaccine-related misinformation. This model illustrates how health agencies may combat vaccine hesitancy through credible information dissemination on social media. It offers a novel approach for crisis informatics studies to model different information categories and the impacted population in the complex digital world.
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Gerd Van Den Eede, Willem J. Muhren, Raphaël Smals, & Bartel A. Van De Walle. (2006). IS capability for incident management and the DERMIS design premises. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 251–261). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: In this paper we present a dynamic model of the performance of an organization's Incident Management process as determined by the capability of its supporting emergency response information system. Our work is based on the Capability Trap model by Repenning & Sterman (2001) and draws from the many insights on emergency response information systems design as described in the DERMIS (Dynamic Emergency Response Management Information System) framework established by Turoff et al. 2004. Whereas the latter describes the premises that underlie an Information System (IS) that is capable of ensuring a reliable and flexible emergency response, the present paper contributes to the research field by looking at the interrelations of the aforementioned premises. We take a System Dynamics approach and gain insights in the key determinants of IS Capability by highlighting the mutual interdependences grouped around the concepts of adaptability, control, implicit knowledge and explicit knowledge.
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