Learning From Fails in Crisis Management: Case of Stress Impact

Sammy Teffali (), Nada Matta () and Eric Chatelet ()
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Sammy Teffali: Tech-CICO - TECHnologies pour la Coopération, l’Interaction et les COnnaissances dans les collectifs - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche Scientifique - CNRS - Centre National de la Recherche Scientifique
Nada Matta: Tech-CICO - TECHnologies pour la Coopération, l’Interaction et les COnnaissances dans les collectifs - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche Scientifique - CNRS - Centre National de la Recherche Scientifique
Eric Chatelet: LM2S - Laboratoire Modélisation et Sûreté des Systèmes - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche Scientifique

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Abstract: A crisis is a complex situation, which actors have some difficulties to manage it. They are under stress to deal with problems that they cannot predict consequences. The human conditions (familial and life) and, the influence of the environment (politic, economic, media) pushes the actors to lose control of the crisis situation. The question we face in this paper is: "is it possible to predict fails action under the impact of the stress in this type of situation and to correct it?" Our main hypothesis to answer is representing fails actions using the experience feedback and the knowledge management. To model the crisis management as systemic system emphasizing regulation loops, and the collaboration activity by showing the dimension of the communication, coordination, and cooperation. This modeling is illustrated on a terrorist attack situation in Algeria. To predict actions consequence of the stress and their corrective, Fuzzy set principle is adopted, based on experience feedback and situations modeling.

Date: 2018-07-28
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Published in 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Jul 2018, Huangshan, China. pp.806-814, ⟨10.1109/FSKD.2018.8687304⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02330636

DOI: 10.1109/FSKD.2018.8687304

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