There is a strategy that is almost mentioned here, but not pursued, that I think is near-optimal—explaining your reasoning as a norm. This is the norm I have experienced in the epistemic community around forecasting. (I am involved in both Good Judgment, where I was an original participant, and have resumed work, and on Metaculus’s AI instance. Both are very similar in that regard.)
If such explanation is a norm, or even a possibility, the social credit for updated predictions will normally be apportioned based on the reasoning as much as the accuracy. And while individual brier scores are useful, forecasters who provide mediocre calibration but excellent public reasoning and evidence which others use are more valuable for an aggregate forecast than excellent forecasters who explain little or nothing.
If Bob wants social credit for his estimate in this type of community, he needs to publicly explain his model—at least in general. (This includes using intuition as an input—there are superforecasters who I update towards based purely on claims that the probability seems too low / high.) Similarly, if Bob wants credit for updating, he needs to explain his updated reasoning—including why he isn’t updating based on evidence that prompted Alice’s estimate, which would usually have been specified, or updated based on Alice’s stated model and her estimate itself. If Bob said 75% initially, but now internally updates to think 50%, it will often be easier to justify a sudden change based on an influential datapoint, rather than a smaller one using an excuse.
Right. I kinda implied it was part of the solution but didn’t say it explicitly enough, and may edit.
The problem for implementation, of course, is that explaining your reasoning is toxic in worlds with the models we describe. It’s the opposite of not taking positions, staying hidden and destroying records. It opens you up to being blamed for any aspect of your reasoning. That’s pretty terrible. It’s doubly terrible if you’re in any sort of double-think equilibrium (see SSC here). Because now, you can’t explain your reasoning.
Political contexts are poisonous, of course, in this and so many other ways, so politics should be kept as small as possible. In most contexts, however, including political ones, the solution is to give no credit for those that don’t explain, or even to assign negative credit for punditry that isn’t demonstrably more accurate than the corwd—which leads to a wonderful incentive to shut up unless you can say something more than “I think X will happen.”
And in collaborative contexts, people are happy to give credit for mostly correct thinking that assist their own, rather than attack for mistakes. We should stay in those contexts and build them out where possible—positive sum thinking is good, and destroying, or at least ignoring, negative sum contexts is often good as well.
There is a strategy that is almost mentioned here, but not pursued, that I think is near-optimal—explaining your reasoning as a norm. This is the norm I have experienced in the epistemic community around forecasting. (I am involved in both Good Judgment, where I was an original participant, and have resumed work, and on Metaculus’s AI instance. Both are very similar in that regard.)
If such explanation is a norm, or even a possibility, the social credit for updated predictions will normally be apportioned based on the reasoning as much as the accuracy. And while individual brier scores are useful, forecasters who provide mediocre calibration but excellent public reasoning and evidence which others use are more valuable for an aggregate forecast than excellent forecasters who explain little or nothing.
If Bob wants social credit for his estimate in this type of community, he needs to publicly explain his model—at least in general. (This includes using intuition as an input—there are superforecasters who I update towards based purely on claims that the probability seems too low / high.) Similarly, if Bob wants credit for updating, he needs to explain his updated reasoning—including why he isn’t updating based on evidence that prompted Alice’s estimate, which would usually have been specified, or updated based on Alice’s stated model and her estimate itself. If Bob said 75% initially, but now internally updates to think 50%, it will often be easier to justify a sudden change based on an influential datapoint, rather than a smaller one using an excuse.
Right. I kinda implied it was part of the solution but didn’t say it explicitly enough, and may edit.
The problem for implementation, of course, is that explaining your reasoning is toxic in worlds with the models we describe. It’s the opposite of not taking positions, staying hidden and destroying records. It opens you up to being blamed for any aspect of your reasoning. That’s pretty terrible. It’s doubly terrible if you’re in any sort of double-think equilibrium (see SSC here). Because now, you can’t explain your reasoning.
Political contexts are poisonous, of course, in this and so many other ways, so politics should be kept as small as possible. In most contexts, however, including political ones, the solution is to give no credit for those that don’t explain, or even to assign negative credit for punditry that isn’t demonstrably more accurate than the corwd—which leads to a wonderful incentive to shut up unless you can say something more than “I think X will happen.”
And in collaborative contexts, people are happy to give credit for mostly correct thinking that assist their own, rather than attack for mistakes. We should stay in those contexts and build them out where possible—positive sum thinking is good, and destroying, or at least ignoring, negative sum contexts is often good as well.