[extropy-chat] Inside Vs. Outside Forecasts
Robin Hanson
rhanson at gmu.edu
Wed Oct 12 03:36:52 UTC 2005
At 11:06 PM 10/11/2005, Eliezer S. Yudkowsky wrote:
>Robin Hanson wrote:
>>It still seems to me that this group tends to base its forecasts a
>>bit too much on constructing scenarios, and a bit too little on
>>statistics about similar past situations.
>
>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.
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
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