[EM] Sims with random candidate allocation, 1D and 2D

[Leon Smith]

Two questions...

What do all the different abbreviations mean?   Not being a voting
theory specialist,  sometimes I have a pretty good idea of what your
abbreviation means,  other times I don't.   Are they semi-standard
abbreviations that appear in literature somewhere?

Are you willing to share your simulation code,  as open source
software for people to review?

Best,
Leon

On Thu, Jun 10, 2010 at 5:07 PM, Kevin Venzke <stepjak at yahoo.fr> wrote:
> Hello,
>
> I've set up my simulation to be able to run repeatedly, and randomly toss
> the candidates out. I've changed distance to be taxicab. As the issues
> seem to be more independent than I first was thinking, I got rid of the
> voters having to be cast within a certain distance of the origin, which
> means they lie in a square (in 2D elections) rather than a circle.
>
> There are a few new methods here. I found that I mistakenly implemented
> sincere CdlA rather than truncated CdlA, so I renamed the old one
> "CdlASnc" and added the correct method as "CdlA".
>
> I added back Raynaud(wv), 2-slot MMPO, and sincere Majority Favorite//
> Antiplurality (MAP), as well as sincere Antiplurality.
>
> The program dumps all the results to a file that I've loaded into a
> database in order to look at "pairwise comparisons" of methods, and
> similarity of methods, and attempt to figure out what causes them to
> differ.
>
> I don't have a lot of trials (a few thousand, though each trial is made
> up of thousands of elections) so I wouldn't take these as the final
> word necessarily...
>
> The format is:
> Method, % elect best, % elect worst, % of times the method ranked in the
> top third of all methods, then middle third, and final third, average
> distance, average normalized distance.
>
> The reason I note how often each method was in the top/middle/bottom
> third is that I noticed some methods were all over the place in where
> they ranked, while other methods didn't move around much.
>
> Note that a method having superior average distance to another method
> doesn't necessarily have superior average normalized distance.
>
> The sort order is increasing average distance (which is the utility
> metric here).
>
> One-dimensional elections:
>
> Method  BestC   WorstC  Top     Middle  Bottom  Dist    DistN
> CdlASnc 91.8%   1.1%    87.3%   10.4%   2.2%    52.978  1.306
> MMstrict        91.8%   1.1%    97.2%   2.6%    0.1%    53.010  1.314
> Bucklin 90.4%   1.4%    79.0%   10.2%   10.8%   53.044  1.453
> DAC     89.6%   1.4%    61.8%   29.6%   8.7%    53.049  1.481
> MAP     91.8%   1.1%    94.1%   4.8%    1.2%    53.155  1.359
> RangeNS 83.3%   0.4%    46.4%   21.8%   31.8%   53.237  2.083
> ApprPoll        81.2%   1.3%    52.0%   19.7%   26.9%   53.442  3.060
> QR      84.1%   2.0%    33.2%   66.1%   0.7%    53.471  2.831
> DSC     83.0%   1.5%    53.1%   40.5%   6.4%    53.474  2.656
> C//A    81.0%   1.8%    13.3%   78.9%   7.8%    53.510  3.074
> MMWV    81.0%   1.8%    17.3%   72.7%   10.1%   53.510  3.075
> CdlA    82.5%   1.5%    25.1%   62.1%   12.8%   53.585  2.786
> ApprZIS 77.0%   0.9%    58.5%   13.2%   28.2%   53.593  3.841
> 2sMMPO  81.1%   1.3%    42.6%   28.6%   28.8%   53.602  3.004
> MMmarg  78.1%   3.0%    5.9%    62.4%   31.7%   53.762  3.983
> IRV     79.1%   3.6%    1.1%    67.9%   31.0%   53.851  4.216
> SPST    78.3%   2.5%    27.0%   44.1%   28.9%   53.996  4.245
> MMPO    76.7%   4.4%    4.4%    34.8%   60.8%   54.017  4.877
> IRV-tr  76.3%   4.1%    0.1%    42.7%   57.2%   54.110  4.924
> Raynaud 76.8%   4.4%    1.6%    34.4%   64.0%   54.139  4.841
> QR-tr   76.0%   4.5%    0.1%    39.2%   60.7%   54.200  5.221
> VFA     73.3%   4.0%    11.3%   21.0%   67.6%   54.377  5.630
> DSC-tr  71.8%   5.4%    13.1%   20.0%   66.9%   54.796  6.735
> FPP     70.4%   8.2%    7.9%    12.8%   79.3%   55.249  8.487
> Antip   44.8%   0.0%    8.8%    16.3%   74.9%   60.119  26.835
>
> Two-dimensional elections:
>
> Method  BestC   WorstC  Top     Middle  Bottom  Dist    DistN
> RangeNS 86.1%   1.1%    81.1%   7.4%    11.5%   113.559 2.470
> ApprPoll        83.6%   2.2%    72.6%   13.0%   14.2%   113.948 3.696
> Bucklin 83.9%   2.4%    76.7%   16.1%   7.1%    113.954 3.651
> DAC     83.9%   2.4%    71.6%   23.8%   4.7%    113.966 3.653
> ApprZIS 82.4%   1.7%    66.3%   16.0%   17.7%   114.009 3.641
> MMstrict        83.1%   2.4%    77.4%   18.6%   4.0%    114.089 3.961
> CdlASnc 82.3%   2.8%    58.6%   27.0%   14.4%   114.247 4.309
> MAP     81.4%   2.9%    51.9%   20.7%   27.4%   114.362 4.695
> CdlA    81.1%   3.3%    20.6%   61.0%   18.3%   114.370 4.686
> QR      81.1%   3.3%    26.1%   65.5%   8.4%    114.456 4.839
> DSC     79.9%   2.8%    43.4%   37.0%   19.5%   114.536 4.882
> C//A    80.5%   3.6%    17.4%   73.0%   9.6%    114.561 5.033
> IRV     79.8%   3.8%    12.8%   70.2%   17.1%   114.668 5.359
> MMWV    79.9%   4.0%    12.5%   59.2%   28.3%   114.701 5.343
> MMmarg  79.7%   4.2%    15.9%   59.7%   24.4%   114.746 5.477
> IRV-tr  78.5%   4.7%    5.9%    56.2%   37.9%   114.985 6.057
> QR-tr   78.3%   4.9%    6.1%    51.7%   42.2%   115.062 6.284
> Raynaud 78.5%   5.3%    4.4%    37.9%   57.8%   115.089 6.261
> SPST    77.0%   4.1%    19.9%   33.9%   46.3%   115.143 6.300
> VFA     75.9%   4.5%    17.8%   25.8%   56.3%   115.325 6.754
> MMPO    76.9%   7.3%    3.9%    22.5%   73.6%   115.827 7.857
> DSC-tr  74.3%   6.0%    15.4%   16.8%   67.8%   115.832 7.961
> FPP     73.6%   7.0%    10.3%   13.2%   76.5%   116.080 8.770
> 2sMMPO  77.0%   8.5%    25.6%   13.2%   61.2%   116.310 8.874
> Antip   62.5%   4.0%    16.7%   12.3%   71.1%   119.050 16.697
>
> I also tried out a hybrid, where the second dimension wasn't as large
> as the first, but it didn't seem to have unique results beyond being a
> compromise between the results of the 1D and 2D simulations.
>
> Kevin Venzke
>
>
>
>
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