5.3 An Examination of Decision-Making by Emergency Managers During Severe Weather Events: A Test of Fuzzy Trace Theory

Tuesday, 9 January 2018: 2:00 PM
Ballroom E (ACC) (Austin, Texas)
Shadya J. Sanders, Howard Univ., Washington, DC

A big push is being made to expand probabilistic forecasting beyond probabilities of precipitation, and extreme event products from NOAA affiliates. Research by Morss (2008) has shown that when users are given a general instruction on how to use probabilistic information they can effectively make decisions and understand the information they have been given. Moving further with probabilistic forecasting, psychological and economic research has noted improved decision-making by combining explicit probabilistic information (e.g. 75%) in combination with gist messaging (e.g. "There is a high chance for lightning to occur at your location.") from an expert in the field (Reyna 2008), using fuzzy-trace theory. Fuzzy trace theory is a dual process decision-making model, but unlike other methods, fuzzy-trace argues that emotional reactions and verbatim representations during decision-making run in parallel, not sequentially. Expanding research to understand the decision-making process of emergency managers could give incredible insight on how to make dramatic and highly impactful improvements to short range ensemble modeling. Testing components of fuzzy-trace theory in this new application, could give insights to how different experiences affect the decision-making of emergency managers. It is generally understood that decision makers are utilizing a variety of information sources, including their own experience and expertise. Regardless of the different experience levels, and information sources used by emergency managers, preliminary data collection implies emergency managers must mentally reduce copious amounts of information into a binary decision. The main goal of this research is to investigate the role of verbatim (probabilistic, numerical forecasting) and gist messaging (contextualized and qualitative) to see what impacts decision makers when given uncertainty information. This research could be used as a guide for forecasters and developers before new forecast technologies are created. A mixed-methods approach is used in this interdisciplinary study gain a deeper understanding of a “high-impact” user. This research can help the communication chain to more effectively disseminate uncertainty and verification data, specifically in the case of: high precipitation events.
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