[EM] Various measures of bias and approaches to eliminating it
Michael Ossipoff
email9648742 at gmail.com
Tue Jul 17 16:23:27 PDT 2012
When I say "interval" without qualifying it, I'm referring to the
interval between two intgers, for the value of q, the quotient of
dividing states' populations by some common divisor.
Divisor methods, expected s/q:
Divisor methods can eliminate bias by makiing equal, for all of the
intervals, the expected s/q for a state somewhere in a particular
interval.
That can be done for different assumptions about the
probability-density distribution for states, over the range of q..
As I've said before, Bias-Free (BF) is unbiased if that distribution
is assumed to be uniform.
Weighted Bias-Free (WBF) is unbiased if that distribution is
accurately approximated by an approxmating function that is used with
WBF. The definition of WBF doesn't specify any particular
approximating function.
As a first impression, it might seem as if WBF is objectively more
unbiased than BF, because the distribution is known to be non-uniform.
But that depends on how one judges bias. One can judge bias by the
expected s/q of a party somewhere in an interval, equally likely to be
anywhere in that interval, without actually believing or assuming that
a state is really equally likely to be anywhere in that interval. I
suggest that that is a legitimate way of judging bias.
If you reside in a state whose population is such as to usually put it
in a certain interval, and you want fairness to states in that
interval, maybe you're interested in the overall average for s/q in
that interval, averaged over all q values in the interval. So it isn't
incorrect to calculate bias as if the distribution were uniform in the
interval. After all, it's not as if your state is really randomly
varying in population throughout the interval..
So BF is completely ulnbiased in a meaningful sense.
If you really felt that your state were randomly varying in
population, according to some estimated probability density function,
throughout the interval, then WBF would be more justified, for
bias-elimination by a finer measure.
But BF's meaningful justsififcation for genuine unbiasedness is good
news, because BF is a lot simpler than WBF.
Correlation between q and s/q in a particular allocation:
I've already proposed a method that, by trial-and-error, minimizes the
Pearson correlation between the states q and s/q.
That's bias-by-state. A problem with that is that that bias reverses
its directdion with each interval. For instance, consider
Jefferson/d'Hondt:
Jefferson/d'Hondt is well-known to be strongly large-biased. But
that's only true _overall_, globally. Within each interval,
Jefferson/d'Hondt is strongly small-biased.
Consider two states in the 1 to 2 interval. One of them has q of 1.001
The other has q of 1.999
Whilch one has higher s/q? They both have one seat. The smaller state
has q close to 1. The larger one has
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