Prompt: Comment on your interest in choosing this video, and let us (and PredictionX forum readers) know what they might find most surprising in it. Explain why.
Firstly, I chose Gina McCarthy for several reasons. I believe that climate change is one of the most (if not the most) important predictions and uncertain phenomena that we must understand. Our understanding of the issue, its implications, and how to deal with it is critical to our species' own survival. Secondly, I thought her credentials (working in the White House under Obama and Biden, former EPA administrator, etc.) were extremely impressive. And lastly, she worked in Connecticut at the start of her career -- my home state!
I found McCarthy's discussion of who looks where for information to be one of the most surprising notions brought up in the interview. Before the interview, I simply thought through intuition that scientists would always be the profession to explain climate change and disseminate its implications. However, McCarthy notes that peoples' most reliable sources of information tend to actually be role models they look up to (e.g. clergymen) more than anything else. She rightly points out that when people hear information about climate change, their first instinct is never to ask what the model is compared to who the information is coming from. It was also interesting to hear that the experts realize this, which is why McCarthy notes that they've reached out to church groups to work with them as they, too, become distributors of the desired information.
The second, underlying layer to this surprising finding is that predictions and modeling have real-world, pragmatic challenges that may be equally important to the construction of the model itself. From my knowledge, we have understood how humans have contributed to climate change and why we cannot continue down this path -- but opposition arises when this information isn't distributed properly. Weather models, on the other hand, are a good example of model outcomes that we distribute well (through local weather channels and apps) that lack opposition. So, I think we ought to consider this second half to prediction modeling as well.