I feel like I've long underappreciated the importance of introspectability in information & prediction systems.
Say you have a system that produces interesting probabilities pn for various statements. The value that an agent gets from them is not directly correlating to the accuracy of these probabilities, but rather to the expected utility gain they get after using information of these probabilities in corresponding Bayesian-approximating updates. Perhaps more directly, something related to the difference between one's prior and posterior after updated
Good point, though I think the "more nuanced cases" are very common cases.
The 2010 flash crash seems relevant; it seems like it was caused by chaotic feedback loops with algorithmic components, that as a whole, are very difficult to understand. While that example was particularly algorithmic-induced, other examples also could come from very complex combinations of trades between many players, and when one agent attempts to debug what happened, most of the traders won't even be available or willing to explain their parts.
The 2007-2008 crisis may have been s