Laibson Interview

Prof. Goodman's interview with Prof. Laibson (linked here) was both a very interesting and odd experience, considering I am taking both of their classes simultaneously. It is especially remarkable to see the concepts two distinct fields/classes interlinking and material I've learned about in very different context come to hint at some larger intellectual umbrella. One point that I found interesting from the interview itself was an idea that I think was encapsulated both by Prof. Laibson's discussion of the fatter bell-curve of climate change and Prof. Goodman's suggestion that economics is a field with a lot of "air resistance" (in that the forces that cause error in economics area lot more prevalent) - that is, a larger question of how to make sufficient progress and models in environments with lots of error, and especially communicating it to the public. It's not an issue I've recognized so distinctly in economics in the past, and it makes me wonder why economics is treated as highly as it is by the public when it has the same amount of error as weather or climate, fields that seem to be constantly lambasted for errors.

One topic that I would have explored further within the interview is the parallelism made between Aristotelean simplification and rational choice theory. Although I do largely agree with the overall idea, I think that some of the distinctions between them serve important roles in understanding the development of how we think about predictive models. As much as I recognize rational choice theory's flaws, I will still take its defense somewhat and push back on the idea that it has a track record as bad as Aristotle's persistent misinformation on everything except for basic biology - it still does a surprisingly good job at explaining [if simplistically] how aggregate decisions rise from individuals, why cooperation can be born out of highly competitive systems, and exploring what making a decision is on a basic level. It's not perfect ground, but I think it's still compatible with behavioral theories in fields like behavioral game theory in ways that are productive, whereas something like Aristotelean physics is completely outmoded by modern approaches. This begs the question, can we develop more nuanced classifications for "wrong" models that pick up on distinctions like these while still drawing out the fundamental and valuable points made by the parallel on a basic level?