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      Exercise design for interagency collaboration training: The case of maritime nuclear emergency management tabletop exercises

      1 , 1
      Journal of Contingencies and Crisis Management
      Wiley

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          Abstract

          In this paper, we explore a concept of exercise design within emergency management competence development. The paper presents recommendations for exercise design aspects that may be suitable for gaining collaboration skills and knowledge that responders need in unknown events of high complexity. This study explores the practice of tabletop exercises with complex scenarios that engage participants to discover organizational roles and responsibility division in collaborative action. Empirically, we base our analysis on maritime search and rescue nuclear preparedness exercises in the Arctic seas. We focus on elements that are essential for learners to increase their knowledge of operational complexity and collaborative performance, including understanding one's own and others' roles and responsibilities, formation of interagency teams, their boundaries and interaction between authorities and levels, mutual recognition of risks and learning about resource capacities and their limitations. The study draws conclusions on aspects that are critical for designing emergency management tabletop exercises, in particularly discussions in mixed groups for interpretative learning; own practice reflection for integrative learning; formulating general collaborative competence objectives, and complexity and realism of scenarios.

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          Most cited references55

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          Judgment under Uncertainty: Heuristics and Biases.

          This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.
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            Conditions for intuitive expertise: a failure to disagree.

            This article reports on an effort to explore the differences between two approaches to intuition and expertise that are often viewed as conflicting: heuristics and biases (HB) and naturalistic decision making (NDM). Starting from the obvious fact that professional intuition is sometimes marvelous and sometimes flawed, the authors attempt to map the boundary conditions that separate true intuitive skill from overconfident and biased impressions. They conclude that evaluating the likely quality of an intuitive judgment requires an assessment of the predictability of the environment in which the judgment is made and of the individual's opportunity to learn the regularities of that environment. Subjective experience is not a reliable indicator of judgment accuracy.
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              An Organizational Learning Framework: From Intuition to Institution

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Journal of Contingencies and Crisis Management
                Contingencies & Crisis Mgmt
                Wiley
                0966-0879
                1468-5973
                March 2024
                November 16 2023
                March 2024
                : 32
                : 1
                Affiliations
                [1 ] Centre for Crisis Management and Collaboration Nord University Business School Bodø Norway
                Article
                10.1111/1468-5973.12517
                595a21b6-cdf3-4a27-86d0-db4e006aee9d
                © 2024

                http://creativecommons.org/licenses/by-nc/4.0/

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