Warren Smith wrote:
You have to be clearer. E.g.
QUOTE
If N = total number of voters, then (unless there's a bug somewhere) we get:
PA_Linear_Range_STV 0.29813 0.00031
If N = the number of voters voting according to the ballot being
reweighted, then the result is:
PA_Linear_Range_STV 0.20928 0.01991
END QUOTE.
I have absolutely no idea what any of that meant. It is just
undefined gobbledegook.
Everything you wrote is meaningless to me. It would help to have
your program print out summary complete with keys, definitions, etc
so that (1) somebody can understand what you are talking about and (2)
you do not have to keep telling us over and over again what you (now)
mean whenever you rerun revised program, the printout will just tell
us.
See my previous posts and
http://munsterhjelm.no/km/elections/multiwinner_tradeoffs/ . Very simply
put, the former number is a measure of proportionality, and the latter
is a measure of Bayesian regret. They are scaled so that 0 is best and 1
is worst. To find out the details (proportionality measure, utility
calc), see the page or previous posts to the list.
As for what N means, I quoted Jameson's formula. Since he says "N = num
voters", it was ambiguous as to whether the number of voters was the
number who voted the particular way, or the number of voters in total.
In other words, if I have an election of the type
10: A (0.9) > B (0.5) > C (0.1)
5: B (0.7) > C (0.3) > A (0.2)
and I'm processing the first ballot set (the 10: ...), then if N =
number of voters who voted the particular way, it would be equal to 10.
Otherwise, it would be equal to 15.
"Linear Range STV" is just the name of the method, and PA is a prefix
for the stats output (as opposed to, for instance, TPF for social
optimum Pareto front).
If you think it'll help, I could probably add a header so you get:
NAME PROP. REGRET
PA_Linear_Range_STV 0.20928 0.01991
Even so, you'd have to look at the page and/or previous posts to know
what those fields *mean*.
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