Abbas Ganji, Negin Alimohammadi, & Scott Miles. (2019). Challenges in Community Resilience Planning and Opportunities with Simulation Modeling. 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: The importance of community resilience has become increasingly recognized in emergency management and
post-disaster community well-being. To this end, three seismic resilience planning initiatives have been
conducted in the U.S. in the last decade to envision the current state of community resilience. Experts who
participated in these initiatives confronted challenges that must be addressed for future planning initiatives.
We interviewed eighteen participants to learn about the community resilience planning process, its
characteristics, and challenges. Conducting qualitative content analysis, we identify six main challenges to
community resilience planning: complex network systems; interdependencies among built environment systems;
inter-organizational collaboration; connections between the built environment and social systems;
communications between built environment and social institutions? experts; and communication among
decision-makers, social stakeholders, and community members. To overcome the identified challenges, we
discuss the capability of human-centered simulation modeling as a combination of simulation modeling and
human-centered design to facilitate community resilience planning.
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Rafael A. Gonzalez. (2009). Crisis response simulation combining discrete-event and agent-based modeling. In S. J. J. Landgren (Ed.), ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives. Gothenburg: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper presents a crisis response simulation model architecture combining a discrete-event simulation (DES) environment for a crisis scenario with an agent-based model of the response organization. In multi-agent systems (MAS) as a computational organization, agents are modeled and implemented separately from the environmental model. We follow this perspective and submit an architecture in which the environment is modeled as a discreteevent simulation, and the crisis response agents are modeled as a multi-agent system. The simultaneous integration and separation of both models allows for independent modifications of the response organization and the scenario, resulting in a testbed that allows testing different organizations to respond to the same scenario or different emergencies for the same organization. It also provides a high-level architecture suggesting the way in which DES and MAS can be combined into a single simulation in a simple way.
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Anja Van Der Hulst, Rudy Boonekamp, & Marc Van Den Homberg. (2014). Field-testing a comprehensive approach simulation model. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 575–584). University Park, PA: The Pennsylvania State University.
Abstract: This paper describes the field tests of a simulation based game aiming at raising awareness and creating a deeper understanding of the dynamics of the comprehensive approach (CA). The setting of this game is that of a failed state where an UN intervention takes place after massive conflict that requires a CA to stabilize the situation. That is, the civil and military actors need to collaborate effectively, taking into account their respective strengths, mandates and roles. Underlying the game is the Go4it CA simulation Model (GCAM2.0). GCAM2.0 was extensively field-tested in eight sessions with about 16 persons each, aiming at assessment of the perceived realism and learning effects. It was found to provide a sufficiently authentic experience to obtain awareness of the CA in novices. With regard to improving the deeper understanding of the dynamics and complexity of the CA, in a cooperation-oriented setting only deeper learning can be reached.
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