[EM] General PR question (from Andy Jennings in 2011)
kathy.dopp at gmail.com
Thu Oct 9 06:35:08 PDT 2014
On Wed, Oct 8, 2014 at 2:50 PM, Toby Pereira <tdp201b at yahoo.co.uk> wrote:
>>> It doesn't work by ignoring or eliminating
>>> smaller factions;
> you suggested in one of your posts that it might
> be desirable.
Well, upon reflection, I think it is undesirable to remove small
factions from the calculations.
> I was just saying that my system deals with it "naturally" -
> i.e. without manually taking out factions.
How? I don't see how? What is your formula again? I believe squaring
the deviations has potential to give smaller factions proportionately
more weight, not less.
>>> And I'm still unsure how to translate your method into approval voting
>>> overlapping factions.
>>It works exactly the same way with overlapping candidate support in
>>different factions. (i.e. v_i and s_i have exactly the same meanings,
> But what I mean is that if a large faction (with say 50% of all voters) is
> divided into two (say 25% each) because of a single controversial candidate
> who appears on half of that faction's ballots but not the other half, then
> if that faction receives half the candidates (and the one controversial
> candidate is not elected), then it will be measured as unproportional
> because each faction will have each contributed to 50% of the candidates but
> will only be 25% of the electorate each.
I don't see the problem. Could you possibly provide an example where
you believe this is situation would be a problem?
>>What is the logic of using squared rather than absolute deviation? and
>>are you also selecting the slate of candidates minimizing your formula?
> Squared deviation gave better and more consistent results when I tried it. I
> always come armed with election scenarios where I have an intended result,
> and I see if the method being tested gives the intended result. My method
> with squared deviation gave every result I wanted it to. Absolute deviation
I tend to not like squaring the deviations since it gives more weight
to some voter groups than others. I also do not like the way the OLS
method squares the deviations when estimating parameters for a model
because it gives much more weight to outliers. (in addition to my
other criticisms of OLS as being limited, by design, to detecting
correlation to certain types of functional models, when often data is
created by multiple distinct processes).
> And yes, in my method the winning set would be the one with the lowest sum
> of the squared deviations. Well, not necessarily, because if candidates
> could be elected sequentially, which could give a different result.
How do you apply your method sequentially? Many sequential methods
I've seen are fundamentally unfair (IRV, for example that treats
voters' votes unequally) and can tend to produce undesirable results.
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