Neat. The minimal example would be if each risk had 50% chance of happening: then the observable correlation coefficient would be −0.5 (not −1, since there is 1⁄3 chance to get neither risk). If the chance of no disaster happening is N/(N+2), then the correlation will be −1/(N+1).
It is interesting to note that many insurance copula methods are used to make size-dependent correlations, but these are nearly always of the type of stronger positive correlations in the tail. This suggests—unsurprisingly—that insurance does not encounter much anthropic risk.
Neat. The minimal example would be if each risk had 50% chance of happening: then the observable correlation coefficient would be −0.5 (not −1, since there is 1⁄3 chance to get neither risk). If the chance of no disaster happening is N/(N+2), then the correlation will be −1/(N+1).
It is interesting to note that many insurance copula methods are used to make size-dependent correlations, but these are nearly always of the type of stronger positive correlations in the tail. This suggests—unsurprisingly—that insurance does not encounter much anthropic risk.
When I read this, my first reaction was “I have to show this comment to Anders” ^_^
It is pretty cute. I did a few Matlab runs with power-law distributed hazards, and the effect holds up well: http://aleph.se/andart2/uncategorized/anthropic-negatives/