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Author (up) Simon French; Carmen Niculae pdf  isbn
  Title Believe in the model: Mishandle the emergency Type Conference Article
  Year 2004 Publication Proceedings of ISCRAM 2004 – 1st International Workshop on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2004  
  Volume Issue Pages 9-14  
  Keywords Artificial intelligence; Civil aviation; Civil defense; Decision making; Decision support systems; Disasters; Forecasting; Information systems; Risk management; Crisis management; Cynefin; Decision support system (dss); Emergency management; Model prediction; Uncertainty; Economic and social effects  
  Abstract During 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 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. 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. Too often our political masters promise the best 'science-based' decision making and too late realise that the social and economic impacts need addressing. In this paper, we explore how model predictions should be drawn into emergency management processes in more balanced ways than often has occurred in the past. © Proceedings ISCRAM 2004.  
  Address Manchester Business School, University of Manchester, Booth Street West, Manchester M15 6PB, United Kingdom  
  Corporate Author Thesis  
  Publisher Royal Flemish Academy of Belgium Place of Publication Brussels Editor B. Van de Walle, B. Carle  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 9076971080 Medium  
  Track Conference Keynote Expedition Conference 1st International ISCRAM Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number Serial 111  
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