I meant that doing a straightforward VOI the way you would have learned to by eg implementing CFAR's advice, would not automatically include the value of simpler models and self-calibration and other general-utility effects that are negligible in the current decision, but significant when added up outside the current context.

Hm. I'm not sure I agree with this, but that's partly because I'm not sure exactly what you're saying. A charitable read is that there's reason to expect overconfidence when considering each situation individually and more correct confidence when considering all situations together because of a psychological quirk. An uncharitable read is that you can have a recurring situation where Policy A chooses an option with negative EV every time, and Policy B chooses an option with positive EV every time, but Policy A has a total higher EV than Policy B. (This can only happen with weird dependencies between the variables and naive EV calculations.)

I do agree that the way to urgify information-seeking and confusion-reducing actions and goals is to have a self-image of someone who gets things right, and to value precision as well as just calibration, and that this is probably more effective in shifting behavior and making implicit VoI calculations come out correctly.

Also, good example of the value of redundant information. I agree that a well done VOI should catch that, but I assert that this has to be learned.

I certainly learned it the hard way!