[extropy-chat] Inside Vs. Outside Forecasts

[Eliezer S. Yudkowsky]

Robin Hanson wrote:
> At 11:06 PM 10/11/2005, Eliezer S. Yudkowsky wrote:
>>
>> It seems to me that there are *no* past situations similar enough to 
>> permit an outside view.  For an outside view to work, you need data 
>> that verges on i.i.d. - independent and identically distributed.  
>> *Analogies* to past situations are not outside views, and they tend to 
>> be chosen after the analogizer has already decided what the results 
>> ought to be. There *is* no outside view of when AI will arrive.   
>> Anyone who tries to pin a quantitative prediction on the date is 
>> just... plain... screwed.
> 
> The article says nothing about needing data verging on i.i.d., and for 
> good reason.  There are lots of ways to make useful comparisons with 
> other cases without needing such a strong constraint.  Yes of course 
> there are ways to be biased with outside views, just as if one works 
> hard enough one can bias any analysis.  But that doesn't mean such 
> analysis isn't useful, or better than alternative ways to analyze.
> 
> Consider the class of situations in which someone predicted an event 
> that they said was so different from existing events that nothing else 
> was similar to it.  We could collect data on this and look at what 
> fraction of the time the predicted event actually happened, and how far 
> into the future it did happen if it did.  Even if we had no other info 
> about the event, that would give a useful estimate of the chance of it 
> happening and when.  Of course we could also look at other 
> characteristics of such predictions and do a multiple regression to take 
> all of the characteristics into account together.

This is exactly what I think you can't do.  If the reference class isn't 
immediately obvious, you can't make one up and call that an 'outside 
view'.  Developing a heuristics-and-biases curriculum for high school is 
not something that shares a few arguable similarities with other 
attempts to develop high-school curricula.  The reference class was 
obvious - that's why the outside view worked.

I'm not sure there's any point in trying to list out what makes a 
reference class "obvious"; that will just entice people to take 
nonobvious reference classes and argue that they're obvious.  Or 
alternatively, entice people to argue that trying to develop a 
heuristics-and-biases curriculum *doesn't* belong to the obvious 
reference class for yada-yada reason.  As you say, if one works hard 
enough, one can bias any analysis - but that's no excuse for issuing 
engraved invitations.  This is one of those cases where, if you don't 
already have a good commonsense understanding of what constitutes 
shooting yourself in the foot, making up complicated lists of criteria 
just introduces independent opportunities for motivated cognition on 
each list item.

If you've already formulated your question, much less already have an 
answer in mind, and *then* you go around formulating "a class of 
situations" and collecting instances, your intended result will bias 
what you think is a relevant instance, and, in fact, what you believe to 
be a relevant reference class.  "The class of situations in which 
someone predicted an event that they said was so different from existing 
events that nothing else was similar to it" is, already, an obviously 
biased reference class where you had a pretty good idea in mind of what 
sort of data you might collect at the time you chose the reference 
class.  Why not the class of predictions about technology?  Why not the 
class of predictions made by people whose name ends in 'N'?  And there's 
no pre-existing source in which you can look up all the relevant 
instances - you'll have to decide what's a relevant instance and what's 
not, and it seems likely that your decision about 'relevant instances' 
will use an archetype-based retrieval schema that recalls 'successful 
predictions of novel events' or 'failed prediction of novel events', 
depending on your thesis.

That's not an outside view.  Outside views are when you can pick up a 
statistical paper or turn to a domain expert and find out what happened 
the last dozen times someone tried pretty much the same thing.  Outside 
views are fast and cheap and introduce few opportunities to make errors. 
  Selecting analogies to support your argument is a whole different 
ballgame, even if you dress it up in statistical lingerie.

-- 
Eliezer S. Yudkowsky                          http://singinst.org/
Research Fellow, Singularity Institute for Artificial Intelligence