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Although sociologists have studied for decades the process whereby citizens make emergency decisions in response to disaster warnings, very little attention has been given to understanding the role of ethnicity in this process. Data derived from fatality and injury counts following disasters, though sparse, documents that minority group citizens tend to suffer disproportionately high negative consequences in connection with the impact of disasters. On the basis of this information, it is inferred that differences exist between the emergency decision‐making processes of minority and majority citizens. This paper lays the theoretical groundwork for beginning to revise existing conceptual models of warning response behavior to more adequately address the variation associated with minority group status. This goal is accomplished through pursuing three general tasks: (1) examining the process through which personal emergency decisions are made, (2) delineating social factors which impinge upon the outcomes of this decision‐making process, and (3) integrating into an existing model of warning response behavior, additional variables which are related to minority status.

In July 1988, Occidental Petroleum (Caledonia) Ltd.'s North Sea oil platform, Piper Alpha, exploded with the loss of 167 lives. Although rare, such industrial disasters demonstrate that the task facing managers at high-hazard sites in an emergency is complex and characterized by time pressure, uncertainty, and danger. In this paper we examine the decision making required in a crisis by the offshore installation manager (OIM) and his or her emergency response team on an offshore oil installation. The paper summarizes the findings of a study in which we examined the selection and training of OIMs for crisis management and interviewed OIMs who had dealt with a real offshore emergency. The characteristics of the decision making that the on-scene commander requires in an offshore crisis are discussed in terms of recent developments in theories of naturalistic decision making, with particular reference to recognition-primed decision making.

AbstractWhen facing public emergencies, human societies need to make decisions rapidly in order to mitigate the problems. However, this process can be difficult due to complexity of the emergency scenarios and lack of systematic methods for analyzing them. In the work reported here, we develop a framework based upon dynamic Bayesian networks in order to simulate emergency scenarios and support corresponding decisions. In this framework, we highlight the importance of emergency propagation, which is a critical factor often ignored by decisionmakers. We illustrate that failure of considering emergency propagation can lead to suboptimal mitigation strategies. By incorporating this critical factor, our framework enables decisionmakers to identify optimal response strategies minimizing emergency impacts. Scenarios developed from two public emergencies: the 2011 Fukushima nuclear power plant accidents and the Covid‐19 pandemic, are utilized to illustrate the framework in this paper. Capabilities of the framework in supporting decision making in both events illustrate its generality and adaptability when dealing with complex real‐world situations. Our analysis results reveal many similarities between these two seemingly distinct events. This indicates that seemingly unrelated emergencies can share many common features beyond their idiosyncratic characteristics. Valuable mitigation insights can be obtained by analyzing a broad range of past emergencies systematically.

Decision-Making in Emergency Management examines decisions the authors have made over their careers based on their combined training, experience and instinct. Through a broad range of case studies, readers discover how experience impacts decision-making in conjunction with research and tools available. While the use of science, data and industry standards are always the best option when it comes to handling emergency situations, not all emergency situations fit one known solution. This book comprehensively explores the question "Is 'instinct' a viable factor when faced with a challenging situation and how close does it match up with the best science available?"

PurposeThe purpose of this paper is to contribute information and recommendations that could better equip emergency managers to prepare for and respond to emergencies and disasters, with a focus on improving their decision‐making capabilities during response.Design/methodology/approachA questionnaire‐based survey approach was used in this research and 48 different local government organisations participated. These results were examined in conjunction with contemporary emergency management decision‐making literature. A combination of closed and open ended questions was used, enabling qualitative and quantitative analysis.FindingsResults suggest that while there is information available about decision making, not all emergency managers are aware of the existence of this information or understand its relevance to emergency management. It is likely that those who did have a comprehensive understanding of decision making had gained this knowledge through non‐emergency management‐related courses. In total, 71 percent of participants said they would be interested in receiving more support regarding training and practice for decision making in Emergency Operations Centres.Originality/valueA wide body of research has investigated decision‐making styles. However, this paper shows that in the local government emergency management sector there is little awareness of the understanding of the different decision‐making approaches. In addition, for those organisations surveyed, there is a great desire for further training and practice in decision making. It is thus vital that this need is addressed, to further improve the future response of these organisations to emergencies.

The researchers compared the effectiveness of two decision models for modeling decision making in Emergency Operations Centers (EOCs): Klein's Recognition Primed Decision (RPD) model and Gladwin's Ethnographic Decision Tree Model (EDTM). The focus was on decisions that affect the psychological and social well-being of responders and community members. Communities of EOC personnel participated in a simulated emergency event, followed by an interview and/or focus group. Analysis of the decision-making processes during the simulation revealed that most operational decisions were made intuitively, with expertise, and best modeled by RPD. When the decisions involved issues for which EOC personnel had less experience (e.g., psychosocial issues), the decision-making approach shifted from a fast intuitive style to a more deliberative style. In some cases, EOC staff requested additional information before making a decision. With no formalized feedback loops, decisions were delayed or not made at all, leaving community residents and EOC personnel without psychosocial services for unnecessary lengths of time. The researchers found the RPD model to be most useful in its potential for identifying areas where future training (i.e., simulated exercises) and education (i.e., knowledge transfer) could be offered to EOC personnel to improve the provision of psychosocial services.