Journal of Homeland Security and Emergency Management
Editor-in-Chief: Renda-Tanali, Irmak
Managing Editor: McGee, Sibel
4 Issues per year
IMPACT FACTOR 2017: 0.712
CiteScore 2017: 0.92
SCImago Journal Rank (SJR) 2017: 0.242
Source Normalized Impact per Paper (SNIP) 2017: 0.615
Believe in the Model: Mishandle the Emergency
You can hardly tell where the computer models finish and the dinosaurs begin.Laura Dern, on the film Jurassic ParkDuring the past quarter century there have been many developments in scientific models and computer codes to help predict the ongoing consequences in the aftermath of many types of emergency: e.g. storms and flooding, chemical and nuclear accident, epidemics such as SARS and terrorist attack. Some of these models relate to the immediate events and can help in managing the emergency; others predict longer term impacts and thus can help shape the strategy for the return to normality. But there are many pitfalls in the way of using these models effectively. Firstly, non-scientists and, sadly, many scientists believe in the models predictions too much. The inherent uncertainties in the models are underestimated; sometimes almost unacknowledged. This means that initial strategies may later need to be revised in ways that unsettle the public, losing their trust in the emergency management process. Secondly, the output from these models form an extremely valuable input to the decision making process; but only one such input. There is a need to draw on much tacit knowledge which by definition cannot reside in a decision support system. Most emergencies are events that have huge social and economic impacts alongside the health and environmental consequences. While we can model the latter passably well, we are not so good at modelling economic impacts and very poor at modelling social impacts. Our knowledge of them is tacit and they lie in the complex space of Snowdens Cynefin categorisation of decision contexts. Thus we draw upon recent thinking in both decision support and knowledge management systems to suggest that we need a more socio-technical approach to developing crisis response system; and, in particular, we explore how model predictions should be drawn into emergency management processes in more balanced ways than often has occurred in the past.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.