Journal article
Modeling an incident management team as a joint cognitive system
Journal of loss prevention in the process industries, Vol.56, pp.231-241
11/01/2018
DOI: 10.1016/j.jlp.2018.07.021
Abstract
Resilience is considered an essential capability of an Incident Management Team (IMT) in planning for and responding to disasters and catastrophes. While IMTs have been studied as a decision-making unit, few attempts were made to view them from a Joint Cognitive System (JCS) perspective that highlights the interplay among humans and technical agents and demands imposed by the incident. To that end, this paper presents a JCS model of the IMT grounded in findings from the existing literature and naturalistic observations of simulated IMT's incident action planning, which functions in a cyclic manner across multiple scales. Using this model, three measures for resilience of the IMT, recovery time, resource status, and interactions, are discussed. To effectively represent the resilient performance incorporating these measures, a novel adoption of the Interactive Episode Analysis method is utilized. By providing a few examples of the analysis method, this study provides proof-of-concept for objective assessment of the resilience characteristics of the IMT. The proposed JCS-based IMT model can be used for descriptive modeling of similar systems to investigate resilient performance.
•An incident management team can be modeled as a joint cognitive system.•Resilient performance of an IMT depends on recovery time, resource status and efficient interactions among system elements.•Decomposition of recovery time would help IMTs to understand and adjust the allocation of limited resources.•Resource status awareness contributes to IMT’s proactive maintenance of operations near the resource boundaries.•Interactive episode analysis can illustrate the IMT’s adaptive performance under non-routine incident scenarios.
Details
- Title: Subtitle
- Modeling an incident management team as a joint cognitive system
- Creators
- Changwon Son - Industrial and Systems Engineering Department, 3131 TAMU, Texas A&M University, College Station, TX, 77843-3131, USAFarzan Sasangohar - Industrial and Systems Engineering Department, 3131 TAMU, Texas A&M University, College Station, TX, 77843-3131, USAS. Camille Peres - Industrial and Systems Engineering Department, 3131 TAMU, Texas A&M University, College Station, TX, 77843-3131, USATimothy J. Neville - Environmental and Occupational Health Department, Texas A&M University, College Station, TX, 77843-1266, USAJukrin Moon - Industrial and Systems Engineering Department, 3131 TAMU, Texas A&M University, College Station, TX, 77843-3131, USAM. Sam Mannan - Industrial and Systems Engineering Department, 3131 TAMU, Texas A&M University, College Station, TX, 77843-3131, USA
- Resource Type
- Journal article
- Publication Details
- Journal of loss prevention in the process industries, Vol.56, pp.231-241
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.jlp.2018.07.021
- ISSN
- 0950-4230
- eISSN
- 1873-3352
- Number of pages
- 11
- Language
- English
- Date published
- 11/01/2018
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984806512802771
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