André Sabino, Rui Nóbrega, Armanda Rodrigues, & Nuno Correia. (2008). Life-saver: Flood emergency simulator. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 724–733). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: This paper proposes an agent-based simulation system for Dam Break Emergency Plan validation. The proposed system shows that integrating GIS data with an agent-based approach provides a successful simulation platform for the emergency plan validation process. Possible strategies to emergency plan modeling and representation are discussed, proposing a close relation with the actual workflow followed by the entities responsible for the plan's specification. The simulation model is mainly concerned with the location-based and location-motivated actions of the involved agents, describing the likely effects of a specific emergency situation response. The simulator architecture is further described, based on the correspondence between the representation of the plan, and the simulation model. This includes the involving characteristics of the simulation, the simulation engine, the description of the resulting data (for the later evaluation of the emergency plan) and a visualization and interaction component, enabling the dynamic introduction of changes in the scenario progression.
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Alexei Sharpanskykh. (2012). An agent-based approach for safety analysis of safety-critical organizations. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Modern safety-critical organizations are characterized by complex, nonlinear dynamics involving many interrelated actors and processes. Safety issues that emerge from these complex dynamics increasingly remain hidden, until an incident or even a serious accident occurs. Traditional safety analysis methods developed long ago for much simpler organizations cannot help identifying, explaining and predicting many safety-related properties of modern organizations. To address this issue, in the paper a formal approach is proposed to establish relations between local dynamics of actors of a complex safety-critical organization and global safetyrelated properties that emerge from these dynamics. In contrast to the traditional approaches, the organizational dynamics are specified by taking the agent perspective with an organizational layer. The application of the approach is illustrated by a simulation case study, in which spread of safety-critical information in an air navigation service provider is investigated. © 2012 ISCRAM.
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Tomoichi Takahashi. (2007). Agent-based disaster simulation evaluation and its probability model interpretation. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 369–376). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Agent-based simulations enable the simulation of social phenomenon by representing human behaviors using agents. Human actions such as evacuating to safe havens or extinguishing fires in disaster areas are important during earthquakes. The inclusion of human actions in calculating the damage at disaster sites provides useful data to local governments for planning purposes. In order to practically apply these simulation results, these results should be tested using actual data. Further, these results should be analyzed and explained in a manner that people who are not agent programmers can also understand easily. First, the possibility of applying agent-based approaches to social tasks is shown by comparing the simulation results with those obtained from other methods. Next, we propose a method to present agent behaviors using a probability model and discuss the results of applying this method to the RoboCup Rescue simulation data. These will delve into future research topics for developing agent based social simulations to practical ones.
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