Provisioning R2O to counteract groupthink in operations
Author Note: This post is particularly applicable to meteorology, but others implementing R2O in an environment that requires tactical decisions may find this discussion relatable.
Imprecise, if not outright incorrect, predictions of severe weather have been the bane of the meteorological enterprise since the emergence of the atmosphere as a chaotic but physical fluid. As science continues to sharpen the skill of weather forecasts, recent attention has focused on better communication principles and provisions of decision support. But who is communicating, who is listening, what information is sought, and how much uncertainty is allowable?
The challenge with predictions that miss the mark is that the degree of the miss is almost never clear beyond debate. When a tornado outbreak is forecast, but numerous destructive but non-tornadic thunderstorms form instead, is the forecast a “bust”? In hindsight, were there indicators that may have suggested a different threat, or was there simply no scientific skill to discriminate between tornado and non-tornadic thunderstorm regimes in the given situation?
Forecasting is tactical decision-making, and there are many meteorologists making predictions simultaneously in the days and hours ahead of prospective severe weather, not only the government and private entities themselves, but within them. After all, meteorologists endeavor to provide forecasts that save lives and protect property. But how do the many meteorologists arrive at a consistent narrative? Does the narrative describe the worst-case scenario?
Sidestepping the issue of whether the media hypes upcoming weather events, the prospect of the worst-case scenario captivates the public. In some ways, such a scenario is ideal because it ensures the greatest degree of preparedness. That does not imply that it is the most likely scenario, though, either in terms of scientific uncertainty or forecaster consensus.
Recently, there has been focus on resolving the weather event impact vs. certainty matrix in the realm of communicating weather information. But there is also the lesser discussed, and perhaps more contentious, matter of how forecasters arrive at decisions, and whether groupthink can lead an entity or enterprise astray. It is difficult to strike a balance between the desire to produce clear and consistent communication on potential hazards while honoring contrary insights to the forecast decision within the community.
At this point, you may be wondering what this has to do with research to operations (R2O). The intent of this discussion is not to resolve some of these questions, or the issue at hand, but instead to provide a perspective on how we have arrived at this place. While R2O can deliver many scientifically induced enhancements to operations, R2O can also provide a medium through which to evolve operational services completely.
The National Weather Service (NWS) has focused on providing gridded forecasts, with each forecast office responsible for completing a geographic subset of the United States grid immediately adjacent to other offices. Since its introduction, the NWS has sought to eliminate artificial geographic boundaries in forecasts based on different forecasts from different offices for adjacent points of the grid. R2O has provided a number of techniques to create a consensus from office to office. There is no doubt that some of these are beneficial in that they save tremendous effort without sacrificing, if not outright increasing, quality.
But at the same time, this has not afforded an outlet from the potential risks of groupthink. Consider if multiple forecasters were asked to prepare a forecast for the same geographic area and then they subsequently discussed the differences between their forecasts to arrive at a consensus. Such a process, if implemented adequately, would lessen or remove preconceived notions about the forecast and the prospective threat. Each forecaster would develop their notion of the hazard and its uncertainty independently.
Can we devise methods through R2O that alert when a forecast deviates from the allowable predictability or range of certainty? A robust R2O practice ensures that the nature of operations evolves from science and challenging the status quo of operations itself. R2O must not be blind to the potential psychological dynamics resulting from stressful decisions under heavy data, situational information, and external pressures.
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