The formula is an approximation which is accurate for small values of s. Which is the domain we care about, since you don’t get huge fitness gains from a single random mutation.
What transformation would make the formula correct? Like does it actually output odds? Or is it one of those convienient linearizations that melts down if you go to far?
I haven’t found the full text of the paper it was derived in, but the discussion I did find says that it’s a matter of approximating assumptions that were necessary to make the analysis tractable in the first place (to someone without a computer, since it was 1927), not a summary of a more complex closed-form solution. So yes, convenient linearizations. The more general case has probably been been analyzed since then, but I wouldn’t know where to look.
TYPE ERROR. Consider
fitness=.75
.Why has no one pointed this out? Am I missing something?
The formula is an approximation which is accurate for small values of s. Which is the domain we care about, since you don’t get huge fitness gains from a single random mutation.
What transformation would make the formula correct? Like does it actually output odds? Or is it one of those convienient linearizations that melts down if you go to far?
Is there a formula for the approximate error?
I haven’t found the full text of the paper it was derived in, but the discussion I did find says that it’s a matter of approximating assumptions that were necessary to make the analysis tractable in the first place (to someone without a computer, since it was 1927), not a summary of a more complex closed-form solution. So yes, convenient linearizations. The more general case has probably been been analyzed since then, but I wouldn’t know where to look.