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Simon French, & Carmen Niculae. (2004). Believe in the model: Mishandle the emergency. In B. C. B. Van de Walle (Ed.), Proceedings of ISCRAM 2004 – 1st International Workshop on Information Systems for Crisis Response and Management (pp. 9–14). Brussels: Royal Flemish Academy of Belgium. 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. Â© Proceedings ISCRAM 2004. 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 Track: Conference Keynote http://idl.iscram.org/show.php?record=111
Simon French, Emma Carter, & Carmen Niculae. (2006). When experts or models disagree. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 547–553). Newark, NJ: Royal Flemish Academy of Belgium. Abstract: In managing crises, decision makers are confronted with a plethora of uncertainties. Many arise because the world is uncertain, particularly in the context of a crisis. But some arise because analyses based upon different, but seemingly equivalent models lead to different forecasts. Other times expert advisors differ in their explanations and predictions of the evolving situation. We argue that when handled correctly such conflict can alert the decision makers to the inherent complexity and uncertainty of the situation and improve their management of the crisis. Keywords: Information systems; Expert opinion; Highly reliable organisations (HRO); Risk communication; Shared mental model; The expert problem; Decision making Track: Emergency Response Reachback: Cases, Concepts, Processes, and Tools http://idl.iscram.org/show.php?record=513

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. Â© Proceedings ISCRAM 2004.

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

Track: Conference Keynote

http://idl.iscram.org/show.php?record=111

Abstract: In managing crises, decision makers are confronted with a plethora of uncertainties. Many arise because the world is uncertain, particularly in the context of a crisis. But some arise because analyses based upon different, but seemingly equivalent models lead to different forecasts. Other times expert advisors differ in their explanations and predictions of the evolving situation. We argue that when handled correctly such conflict can alert the decision makers to the inherent complexity and uncertainty of the situation and improve their management of the crisis.