The first point is extremely interesting. I’m just spitballing without having read the literature here, but here’s one quick thought that came to mind. I’m curious to hear what you think.
First, instruct participants to construct a very large number of 90% confidence intervals based on the two-point method.
Then, instruct participants to draw the shape of their 90% confidence interval.
Inform participants that you will take a random sample from these intervals, and tell them they’ll be rewarded based on both: (i) the calibration of their 90% confidence intervals, and (ii) the calibration of the x% confidence intervals implied by their original distribution — where x is unknown to the participants, and will be chosen by the experimenter after inspecting the distributions.
Allow participants to revise their intervals, if they so desire.
So, if participants offered the 90% confidence interval [0, 10^15] on some question, one could back out (say) a 50% or 5% confidence interval from the shape of their initial distribution. Experimenters could then ask participants whether they’re willing to commit to certain implied x% confidence intervals before proceeding.
There might be some clever hack to game this setup, and it’s also a bit too clunky+complicated. But I think there’s probably a version of this which is understandable, and for which attempts to game the system are tricky enough that I doubt strategic behavior would be incentivized in practice.
On the second point, I sort of agree. If people were still overprecise, another way of putting your point might be to say that we have evidence about the irrationality of people’s actions, relative to a given environment. But these experiments might not provide evidence suggesting that participants are irrational characters. I know Kenny Easwaran likes (or at least liked) this distinction in the context of Newomb’s Problem.
That said, I guess my overall thought is that any plausible account of the “rational character” would involve a disposition for agents to fine-tune their cognitive strategies under some circumstances. I can imagine being more convinced by your view if you offered an account of when switching cognitive strategies is desirable, so that we know the circumstances under which it would make sense to call people irrational, even if existing experiments don’t cut it.
I think the issue is that creating an incentive system where people are rewarded for being good at an artificial game that has very little connection to their real world cericumstances, isn’t going to tell us anything very interesting about how rational people are in the real world, under their real constraints.
I have a friend who for a while was very enthused about calibration training, and at one point he even got a group of us from the local meetup + phil hazeldon to do a group exercise using a program he wrote to score our calibration on numeric questions drawn from wikipedia. The thing is that while I learned from this to be way less confident about my guesses—which improves rationality, it is actually, for the reasons specified, useless to create 90% confidence intervals about making important real world decisions.
Should I try training for a new career? The true 90% confidence interval on any difficult to pursue idea that I am seriously considering almost certainly includes ‘you won’t succeed, and the time you spend will be a complete waste’ and ‘you’ll do really well, and it will seem like an awesome decision in retrospect’.
Elon Musk estimated a 10% chance of success for both Tesla and SpaceX. Those might have been good estimates.
Peter Thiel talks about how one of the reasons that the PayPal mafia is so successful is that they all learned that success is possible but really hard. If you pursue a very difficult idea and know you have a 10% chance of success you really have to give it all and know that if you slack you won’t succeed.
I like the strategy, though (from my experience) I do think it might be a big ask for at least online experimental subjects to track what’s going on. But there are also ways in which that’s a virtue—if you just tell them that there are no (good) ways to game the system, they’ll probably mostly trust you and not bother to try to figure it out. So something like that might indeed work! I don’t know exactly what calibration folks have tried in this domain, so will have to dig into it more. But it definitely seems like there should be SOME sensible way (along these lines, or otherwise) of incentivizing giving their true 90% intervals—and a theory like the one we sketched would predict that that should make a difference (or: if it doesn’t, it’s definitely a failure of at least local rationality).
On the second point, I think we’re agreed! I’d definitely like to work out more of a theory for when we should expect rational people to switch from guessing to other forms of estimates. We definitely don’t have that yet, so it’s a good challenge. I’ll take that as motivation for developing that more!
The first point is extremely interesting. I’m just spitballing without having read the literature here, but here’s one quick thought that came to mind. I’m curious to hear what you think.
First, instruct participants to construct a very large number of 90% confidence intervals based on the two-point method.
Then, instruct participants to draw the shape of their 90% confidence interval.
Inform participants that you will take a random sample from these intervals, and tell them they’ll be rewarded based on both: (i) the calibration of their 90% confidence intervals, and (ii) the calibration of the x% confidence intervals implied by their original distribution — where x is unknown to the participants, and will be chosen by the experimenter after inspecting the distributions.
Allow participants to revise their intervals, if they so desire.
So, if participants offered the 90% confidence interval [0, 10^15] on some question, one could back out (say) a 50% or 5% confidence interval from the shape of their initial distribution. Experimenters could then ask participants whether they’re willing to commit to certain implied x% confidence intervals before proceeding.
There might be some clever hack to game this setup, and it’s also a bit too clunky+complicated. But I think there’s probably a version of this which is understandable, and for which attempts to game the system are tricky enough that I doubt strategic behavior would be incentivized in practice.
On the second point, I sort of agree. If people were still overprecise, another way of putting your point might be to say that we have evidence about the irrationality of people’s actions, relative to a given environment. But these experiments might not provide evidence suggesting that participants are irrational characters. I know Kenny Easwaran likes (or at least liked) this distinction in the context of Newomb’s Problem.
That said, I guess my overall thought is that any plausible account of the “rational character” would involve a disposition for agents to fine-tune their cognitive strategies under some circumstances. I can imagine being more convinced by your view if you offered an account of when switching cognitive strategies is desirable, so that we know the circumstances under which it would make sense to call people irrational, even if existing experiments don’t cut it.
I think the issue is that creating an incentive system where people are rewarded for being good at an artificial game that has very little connection to their real world cericumstances, isn’t going to tell us anything very interesting about how rational people are in the real world, under their real constraints.
I have a friend who for a while was very enthused about calibration training, and at one point he even got a group of us from the local meetup + phil hazeldon to do a group exercise using a program he wrote to score our calibration on numeric questions drawn from wikipedia. The thing is that while I learned from this to be way less confident about my guesses—which improves rationality, it is actually, for the reasons specified, useless to create 90% confidence intervals about making important real world decisions.
Should I try training for a new career? The true 90% confidence interval on any difficult to pursue idea that I am seriously considering almost certainly includes ‘you won’t succeed, and the time you spend will be a complete waste’ and ‘you’ll do really well, and it will seem like an awesome decision in retrospect’.
Elon Musk estimated a 10% chance of success for both Tesla and SpaceX. Those might have been good estimates.
Peter Thiel talks about how one of the reasons that the PayPal mafia is so successful is that they all learned that success is possible but really hard. If you pursue a very difficult idea and know you have a 10% chance of success you really have to give it all and know that if you slack you won’t succeed.
Crossposting from Substack:
Super interesting!
I like the strategy, though (from my experience) I do think it might be a big ask for at least online experimental subjects to track what’s going on. But there are also ways in which that’s a virtue—if you just tell them that there are no (good) ways to game the system, they’ll probably mostly trust you and not bother to try to figure it out. So something like that might indeed work! I don’t know exactly what calibration folks have tried in this domain, so will have to dig into it more. But it definitely seems like there should be SOME sensible way (along these lines, or otherwise) of incentivizing giving their true 90% intervals—and a theory like the one we sketched would predict that that should make a difference (or: if it doesn’t, it’s definitely a failure of at least local rationality).
On the second point, I think we’re agreed! I’d definitely like to work out more of a theory for when we should expect rational people to switch from guessing to other forms of estimates. We definitely don’t have that yet, so it’s a good challenge. I’ll take that as motivation for developing that more!