#### Discover more from Overcoming Bias

Last week I claimed that the saying "extraordinary claims require extraordinary evidence" is appropriate anytime people too easily make more extreme claims than their evidence can justify. Eliezer, however, whom I respect, thought the saying appropriate anytime people make claims with a very low prior probability. So I have worked out a concrete math model to explore our dispute. I suggest that if you are math averse you stop reading this post now.

Consider the example of power law disasters, where the chance of an event with severity greater than *x* goes as a negative power *x*^-*v*. If we talk in terms of the magnitude *y* = log(*x*) of the disaster, then our prior on y is distributed exponentially as exp(-*v*y*).

Imagine that a claimer gets a signal *s *about our next disaster magnitude *y*, a signal normally distributed with mean *y* and standard deviation *d*. His maximum posterior estimate (MPE) of *y* should rationally be *z = s – d*v*, and his posterior for *y* should be normally distributed with mean* z* and standard deviation *d*. The claimer will claim to us either that his signal was *s’*, or equivalently that his MPE was *z’*. We must then decide what to believe about the distribution of *y* (or *x*) from this claim.

Let us assume that the payoff *U* of this claimer is the sum of two parts: attention *U1* and truth *U2*. First, he gets an attention payoff *U1* that goes as *a*z’*, rewarding him more for claiming that a bigger disaster looms. Second, he gets a truth payoff* U2* that is a logarithmic proper scoring rule. That is, he must declare a probability distribution* p*(*y*) and will be paid as log(*p*(*y*)), using the chance he assigned to the actual disaster magnitude y that occurs. (So *U *= *a*z*(*s’*) + Int_*y* *p*(*y*|*s*) log(*p*(*y*|*s’*)) d*y*.)

Let us also assume that the claimer is not an exact rational agent; the chance he makes any claim *z’ *(or *s’*) is exponential in his total payoff, as in exp(*r*U*), where *r* is his rationality. Finally, assume that we know parameters like *a,d,r,v,* and that the claimer’s signal *s* is well away from the lower boundary of possible *y*, so we can treat integrals over normal distributions as if they went to infinity.

Putting this all together and turning the crank, we find that a claimer with zero attention payoff will claim a *z’* that is normally distributed with a mean of the infinitely rational *z* (or equivalently a *s’* that is normally distributed around a mean of the actual *s*). The standard deviation *c* of this distribution depends on his rationality *r *and the strength of his payoffs *U*. For a claimer with a positive attention payoff, his *z’* (or *s’*) is also normally distributed with the same standard deviation *c*, but the mean of this distribution is biased toward larger disasters, with a bias *b* that is proportional to *a*.

When we hear the claimer’s claim* z’ *(or *s’*), we make an inference about *y*. Our rational posterior for *y* should have a mean *z = z’-b-c*v* and a standard deviation *c*, which depends on both *d* and *r*. That is, we are skeptical about the claim for two reasons. First, we correct for the bias* b* due to the claimer distorting his claims to gain more attention. Second, we correct for the fact that larger disasters are a priori less likely, a correction that rises as the claimer’s rationality falls.

So this model contains effects similar to those Eliezer and I were discussing. My claim was that in practice the attention payoff effect is larger than the irrational claimer effect, but I have not shown that yet. Note, however, that the correction, and the relative size of the two effects, does *not* depend on how a priori unlikely is the claim *z’*.

## A Model of Extraordinary Claims

Also I should have written U1 = a*z' instead of a*y.

Ah yes, right, of course.

Well, I grant that it is a reasonable sounding model. A pretty good one for a few days work, absolutely. Perhaps you should submit it to the Mathematical Contest in Modeling as a problem for them? Reading it reminded me of my years competing there. :)

I'm not convinced that it's necessarily much more accurate than some other plausible models, but it does have the advantage of much nicer looking mathematics than many alternatives, and that counts for a lot.

Anyone want to co-author something a write up of this? :)

Well, I am in Fairfax County and a probabilist... ;)

You are both right; my error; I meant max posterior instead of max likelihood. Also I should have written U1 = a*z' instead of a*y. I've corrected these in the text above.

So, Eliezer, U = a*z(s') + Int_y p(y|s) log(p(y|s')) dy.

John, I took Eliezer's argument to be that there was a low probability effect that wasn't due to an attention payoff at all.

Yes, other models might give different results; this was the first one I tried. Yes, an obvious easy generalization is a general quadratic U1, which can express the diminishing returns Eliezer suggests.

Anyone want to co-author something a write up of this? :)