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Decision inertia: deciding between least worst outcomes in emergency responses to disasters

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
  • Laurence Alison
  • Nicola Power
  • Claudia van den Heuvel
  • Michael Humann
  • Marek Palasinski
  • Jonathan Crego
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<mark>Journal publication date</mark>06/2015
<mark>Journal</mark>Journal of Occupational and Organizational Psychology
Issue number2
Volume88
Number of pages27
Pages (from-to)295-321
Publication StatusPublished
Early online date17/02/15
<mark>Original language</mark>English

Abstract

This study demonstrates how naturalistic decision-making (NDM) can be usefully applied to study ‘decision inertia’ – Namely the cognitive process associated with failures to execute action when a decision-maker struggles to choose between equally perceived aversive outcomes. Data assessed the response and recovery from a sudden impact disaster during a 2-day immersive simulated emergency response. Fourteen agencies (including police, fire, ambulance, and military) and 194 participants were involved in the exercise. By assessing the frequency, type, audience, and content of communications, and by reference to five subject matter experts’ slow time analyses of critical turning points during the incident, three barriers were identified as reducing multiagency information sharing and the macrocognitive understanding of the incident. When the decision problem was non-time-bounded, involved multiple agencies, and identification of superordinate goals was lacking, the communication between agencies decreased and agencies focused on within-agency information sharing. These barriers distracted teams from timely and efficient discussions on decisions and action execution with seeking redundant information, which resulted in decision inertia. Our study illustrates how naturalistic environments are conducive to examining relatively understudied concepts of decision inertia, failures to act, and shared situational macrocognition in situations involving large distributed teams.