While watching the interview with Dan Gilbert, I was most surprised to hear him note that in the context of climate change, most people tend to recognize some sort of "confidence interval" (and this is why there is a general lack of urgency). I was initially surprised to hear this, though it ultimately makes sense: the general non-scientist public obviously recognize what uncertainty is, but don't generally use the language of confidence interval. This made me question if non-scientist people (myself included) know more about prediction, uncertainty, or models than we think, and they just do not understand it in the context of hard statistics. With this in mind, and given that Dan Gilbert argues people will be less motivated by statistics about model accuracy than by other campaigns (like behavioral based), I wonder how we can "meet people where they are" in educating about predictions, uncertainty, and models.
Similarly, I was surprised to hear him use the word "simulation" to describe the personal predictions we make in our minds after considering past information. This has me thinking about potential ways to bridge how the public naturally understands models and uncertainty with the more academic (and more accurate, but harder to convey) explanations.