Those are some of the lines I was thinking along. But it’s not clear if the peak of the distribution is close to accuracy, human bias and poor understanding being what they are..
I agree that even without manipulation, human reasoning is wildly inaccurate. But perhaps we can use a model where human reasoning asymptotically converges to something accurate unless subjected to some sort of “destructive manipulation” which is unlikely to happen by chance.
The following is one (simplistic) model which might be a useful starting point.
Consider a human and a robot playing a stochastic game like in CIRL. Suppose that each of them is an oracle machine plugged into a reflective oracle, like in the recent paper of Jan, Jessica and Benya. Let the robot have the following prior over the program implemented by the human. The human implements a random program (i.e. a random string of bits for some prefix-free universal Oracle machine) conditional on this program being asymptotically optimal in mean for the class of all robot policies that avoid producing some set of “manipulative action sequences.” Here, “manipulative sequences” can be any set S of action sequences s.t.∑x∈Sn−|x|<ϵ where |x| is the length of the action sequence x, n is the number of possible actions and ϵ is a parameter on which the prior depends.
Those are some of the lines I was thinking along. But it’s not clear if the peak of the distribution is close to accuracy, human bias and poor understanding being what they are..
I agree that even without manipulation, human reasoning is wildly inaccurate. But perhaps we can use a model where human reasoning asymptotically converges to something accurate unless subjected to some sort of “destructive manipulation” which is unlikely to happen by chance.
Interesting. How could we formalise that?
The following is one (simplistic) model which might be a useful starting point.
Consider a human and a robot playing a stochastic game like in CIRL. Suppose that each of them is an oracle machine plugged into a reflective oracle, like in the recent paper of Jan, Jessica and Benya. Let the robot have the following prior over the program implemented by the human. The human implements a random program (i.e. a random string of bits for some prefix-free universal Oracle machine) conditional on this program being asymptotically optimal in mean for the class of all robot policies that avoid producing some set of “manipulative action sequences.” Here, “manipulative sequences” can be any set S of action sequences s.t.∑x∈Sn−|x|<ϵ where |x| is the length of the action sequence x, n is the number of possible actions and ϵ is a parameter on which the prior depends.