[extropy-chat] Inside Vs. Outside Forecasts

Robin Hanson rhanson at gmu.edu
Wed Oct 12 23:38:52 UTC 2005

At 05:07 PM 10/12/2005, Eliezer S. Yudkowsky wrote:
>>But don't let the best be the enemy of the good.  The inside view 
>>is so often bad that even a crude outside view is typically a big 
>>improvement.  That is the idea of the surprising usefulness of 
>>simple linear models.  Yes, the world isn't linear, but simple 
>>linear models often beat the hell out of "sophisticated" human 
>>inside-view intuition.
>Don't let the worse be the argument for the bad.  I wasn't saying 
>that you should use inside views instead.  I was proposing that you 
>were just screwed.  Robyn Dawes makes a similar point in his chapter 
>on the robustness of linear models: when the best linear model 
>predicts only 4% of the variance (and, of course, sophisticated 
>human intuition doesn't predict anything at all), it may be that the 
>phenomenon in question is just not very predictable.  What makes 
>people think they *can* predict student performance ten years out on 
>the basis of a five-minute interview?  What makes people think they 
>can predict the arrival time of AI?
>Yes, people seek excuses to reject outside views (typically bearing 
>bad news) in favor of inside views (typically optimistic).  But that 
>the outside views work is tied to the existence of an obvious 
>reference class in the experimental tests, making the outside view 
>fast, cheap, and simple.  ... the outside view will also tend to 
>work worse as as the problem becomes harder - as the reference class 
>becomes less obvious, the analogies become more distant and complex, 
>and people start to argue about the reference class and introduce 
>motivated cognition into the arguments.  With enough opportunities 
>for motivated cognition, why should selected arguments from 
>analogies work any better than inside views, even dressed up in 
>statistics?  What evidence is there that this sort of thing will 
>work? Linear models are great stuff but neither linear models nor 
>human intuitive judgment will predict next week's lottery 
>numbers.  What makes you think you can do better than chance?  Why 
>are you not simply screwed?

This nice thing about using formal statistical analysis is that if 
you do it right it should tell you when you are screwed.  If your 
posterior isn't much different from you prior, why then the data 
didn't tell you much.  So there's not much harm in trying the 
statistics.  We can't pretend we don't have beliefs on hard problems 
- so we have to try what we can to get the best estimates we can.

Robin Hanson  rhanson at gmu.edu  http://hanson.gmu.edu
Associate Professor of Economics, George Mason University
MSN 1D3, Carow Hall, Fairfax VA 22030-4444
703-993-2326  FAX: 703-993-2323 

More information about the extropy-chat mailing list