Good decision-making is enhanced when both our ignorance of the future and the limits of today's science are communicated clearly.
A good decision is one where, with hindsight, we still feel happy about the process used to arrive at the decision. This does not necessarily mean having made “the right decision” in every circumstance: for example, if we are choosing whether to act given a forecast which is probabilistic. The “limits of today’s science” are the possibilities of quantifying these outcomes rigorously, where the future is not perfectly known.
Without this knowledge, it is harder to make good decisions. If we expect to be able to rely on an hour-by-hour weather forecast for next month, we may be disappointed when the outcomes are not as we expected. In hindsight, having evaluated the forecast, we realise that the procedure was not robust due to an unreliable input. With better communication about the limitations of the science, we could have made a better (more robust) decision.
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It may be the right decision to choose not to take an umbrella on days when there is a 10% chance of rain forecast by a reliable system; on 10% of those days, then, you will get wet. But you can still be content that the decision-making process is one which optimises your outcome overall. If you have a more conservative utility function and prefer never to get wet, then your threshold for choosing not to take the umbrella could be higher; the point is that you get what you asked for, even if that is getting wet on one in ten rainy days.
Comments on Principle 2
Dewi Le Bars - P02-0725
This is an important principle. However, in practice, in climate science, I see it as an ideal goal that is impossible to reach. How to communicate clearly something that is not clear? Climate projections for the year 2100 cannot be validated. On the other hand confidence in the field of climate science can be built from the understanding of the current earth system dynamics and from past climate projections for the years 2010s. These information do not give a clear limit of the science but an understanding of what can be projected more or less well. The difficulty is then on the users side to decide wether the climate information is robust enough for their decision or not.