Gonzalez, J. J., & Eden, C. (2023). Devising Mitigation Strategies With Stakeholders Against Systemic Risks in a Pandemic. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1000–1013). Omaha, USA: University of Nebraska at Omaha.
Abstract: Understanding and managing systemic risk has huge importance for disaster risk reduction in our globally connected world. The COVID-19 pandemic is a prominent case for the global impact of systemic risk. Did so the added urgency of the pandemic systemic risk trigger such paradigm shift? The use of qualitative modelling of systemic risk has progressed the field, particularly when policy makers need support urgently and want to utilize a range of interdisciplinary expertise. We have extended to disaster risk reduction a method for causal mapping for problem solving and strategy development targeting complex project management. Our approach delivers useful, useable, and used mitigation to systemic risk in a pandemic using participatory modelling with practitioners, domain experts and power-brokers.
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Abildsnes, E., Paulsen, S., & Gonzalez, J. J. (2023). Improving resilience against a pandemic: A novel technology for strategy development with practitioners and decision-makers. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 964–974). Omaha, USA: University of Nebraska at Omaha.
Abstract: The project Systemic Pandemic Risk Management (SPRM), funded by the Research Council of Norway, has developed methods to assess and manage pandemic systemic risks. The project consortium includes an enterprise leading the project, public partners and research institutions in Norway, Sweden, and Italy. Kristiansand municipality, a partner in the SPRM project, adopted the project methods to assess and manage systemic risks. Based on a scenario about the potential spread patterns of the COVID-19 Omicron variant developed by the Norwegian Institute of Public Health, staff from Kristiansand employed the SPRM project’s approach to facilitate systemic risk assessment and management workshops. Practitioners and decision-makers from the main hospital in the Agder county and several municipalities proposed risks, their causal consequences and identified practical and impactful mitigation strategies. The strategies were implemented at the county level. The approach can improve handling of systemic risk scenarios beyond pandemics.
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Gabriel, A., & Torres, F. S. (2023). Navigating Towards Safe and Secure Offshore Wind Farms: An Indicator Based Approach in the German North and Baltic Sea. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 609–619). Omaha, USA: University of Nebraska at Omaha.
Abstract: Offshore wind farms (OWFs) have become an increasingly relevant form of renewable energy in recent years, with the German North Sea being one of the most active regions in the world. However, the safety and security of OWF have become increasingly important due to the potential threats and risks associated with their growing share in the security of energy supply. This paper aims to present a comprehensive and systematic indicator-based approach to assess the safety and security status of OWFs in the German North Sea. The approach is based on the results of a survey of people working in the offshore industry and draws on the work published by Gabriel et al. (2022). The results of the study suggest that the indicator-based approach is a useful tool for end users to assess the security status of offshore wind farms and can be used for further research and development.
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Pettersson, M. N., Axelsson, J., Svenson, P., & Johansson, A. (2023). Towards a Risk Analysis Method for Systems of Systems: A Case Study on Wildfire Rescue Operations. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 530–545). Omaha, USA: University of Nebraska at Omaha.
Abstract: Crisis management (CM) is facing new challenges due to the increasing complexity of contemporary society. To mitigate a crisis, it is often necessary for a collection of independent systems, people, and organizations to cooperate. These collaborating entities constitute an interconnected socio-technical system of systems (SoS). An important question is how a CM SoS should be constructed to minimize the risk of failure and accurately handle a crisis. SoS pose new challenges in analysing risk during interactions. This paper investigates whether the risk analysis method STAMP (System-Theoretic Accident Model and Processes) is suitable for SoS, using a forest fire rescue operation case study. Results show characteristics of various risk sources and identify some SoS characteristics, such as dynamic structure and latent risks, that are not sufficiently handled in STAMP. The study further contributes to the body of knowledge by presenting potential directions for research on SoS risk assessment methods.
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Cruz, J. A. dela, Hendrickx, I., & Larson, M. (2023). Towards XAI for Information Extraction on Online Media Data for Disaster Risk Management. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 478–486). Omaha, USA: University of Nebraska at Omaha.
Abstract: Disaster risk management practitioners have the responsibility to make decisions at every phase of the disaster risk management cycle: mitigation, preparedness, response and recovery. The decisions they make affect human life. In this paper, we consider the current state of the use of AI in information extraction (IE) for disaster risk management (DRM), which makes it possible to leverage disaster information in social media. We consolidate the challenges and concerns of using AI for DRM into three main areas: limitations of DRM data, limitations of AI modeling and DRM domain-specific concerns, i.e., bias, privacy and security, transparency and accountability, and hype and inflated expectations. Then, we present a systematic discussion of how explainable AI (XAI) can address the challenges and concerns of using AI for IE in DRM.
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