"This desiderata is often difficult to reconcile with clear scoring, since complexity in forecasts generally requires complexity in scoring."
Can you elaborate on this? In some sense, log-scoring is simple and can be applied to very complex distributions; Are you saying that the this would still be "complex scoring" because the complex forecast needs to be evaluated, or is your point about something different?
Partial resolution could also help with getting some partial signal on long term forecasts.
In particular, if we know that a forecasting target is growing monotonously over time (like "date at which X happens" or "cumulative number of X before a specified date"), we can split P(outcome=T) into P(outcome>lower bound)*P(outcome=T|outcome>lower bound). If we use log scoring, we then get log(P(outcome>lower bound)) as an upper bound on the score.
If forecasts came in the form of more detailed models, it should be possible to use a similar approach to calculate bounds based on conditioning on more complicated events as well.
I don't know what performance measure is used to select superforecasters, but updating frequently seems to usually improve your accuracy score on GJopen as well (see "Activity Loading" inthis thread on the EA forum. )
"Beginners in college-level math would learn about functions, the basics of linear systems, and the difference between quantitative and qualitative data, all at the same time."
This seems to be the standard approach for undergraduate-level mathematics at university, at least in Europe.
Makes sense, I was thinking about rewards as function of the next state rather than the current one.
I can stil imagine that things will still work if we replace the difference in Q-values by the difference in the values of the autoencoded next state. If that was true, this would a) affect my interpretation of the results and b) potentially make it easier to answer your open questions by providing a simplified version of the problem.
Edit: I guess the "Chaos unfolds over time" property of the safelife environment makes it unlikely that this would work?
I'm curious whether AUP or the autencoder/random projection does more work here. Did you test how well AUP and AUP_proj with a discount factor of 0 for the AUP Q-functions do?
"So if you wouldn’t sacrifice >0.01AUC for the sake of what a human thinks is the “reasonable” explanation to a problem, in the above thought experiment, then why sacrifice unknown amounts of lost accuracy for the sake of explainability?" You could think of explainability as some form of regularization to reduce overfitting (to the test set).
"Overall, access to the AI strongly improved the subjects' accuracy from below 50% to around 70%, which was further boosted to a value slightly below the AI's accuracy of 75% when users also saw explanations. "But this seems to be a function of the AI system's actual performance, the human's expectations of said performance, as well as the human's baseline performance. So I'd expect it to vary a lot between tasks and with different systems.
"My own guess is that humans are capable of surviving far more severe climate shifts than those projected in nuclear winter scenarios. Humans are more robust than most any other mammal to drastic changes in temperature, as evidenced by our global range, even in pre-historic times"
I think it is worth noting that the speed of climate shifts might play an important role, as a lot of human adaptability seems to rely on gradual cultural evolution. While modern information technology has greatly sped up the potential for cultural evolution, I am unsure if these speedups are robust to a full-scale nuclear war.
I interpreted this as a relative reduction of the probability (P_new=0.84*P_old) rather than an absolute decrease of the probability by 0.16. However, this indicates that the claim might be ambiguous which is problematic in another way.