Risk Management and Analytics in Wildfire Response

Purpose of Review The objectives of this paper are to briefly review basic risk management and analytics concepts, describe their nexus in relation to wildfire response, demonstrate real-world application of analytics to support response decisions and organizational learning, and outline an analytics strategy for the future. Recent Findings Analytics can improve decision-making and organizational performance across a variety of areas from sports to business to real-time emergency response. A lack of robust descriptive analytics on wildfire incident response effectiveness is a bottleneck for developing operationally relevant and empirically credible predictive and prescriptive analytics to inform and guide strategic response decisions. Capitalizing on technology such as automated resource tracking and machine learning algorithms can help bridge gaps between monitoring, learning, and data-driven decision-making. Summary By investing in better collection, documentation, archiving, and analysis of operational data on response effectiveness, fire management organizations can promote systematic learning and provide a better evidence base to support response decisions. We describe an analytics management framework that can provide structure to help deploy analytics within organizations, and provide real-world examples of advanced fire analytics applied in the USA. To fully capitalize on the potential of analytics, organizations may need to catalyze cultural shifts that cultivate stronger appreciation for data-driven decision processes, and develop informed skeptics that effectively balance both judgment and analysis in decision-making.

Using live disaster exercises to study large multiteam systems in extreme environments: Methodological and measurement fit

Multiteam systems (MTSs) are comprised of two or more teams working toward shared superordinate goals but with unique subgoals. In large MTSs operating in extreme environments, coordination difficulties have repeatedly been found, which compromise response effectiveness. Research is needed that examines MTSs in situ within extreme environments to develop temporal theories of inter-team processes and understanding of how coordination may be improved within these challenging contexts. Live disaster exercises replicate the complexities of extreme environments, providing a valuable avenue for observing inter-team processes in situ. This article seeks to contribute to MTS research by highlighting (i) a mixed-method framework for collecting data during live disaster exercises that uses both inductive and deductive approaches to promote methodological and measurement fit; (ii) ways in which data can be collected and combined to meet the appropriate standards of their methodological class; and (iii) a case example of a National exercise.