[EM] Ideas for reducing computational burden in very large elections

[Brian Olson]

This might come down to how we define the “one vote” part of one-person-one-vote.
Should the sum of the vote be 1 or should the magnitude of the vote vector be 1?
Which ‘distortion’ best preserves voter intent?
I’m not 100% sure which form of normalization is better.
It might just be mathematical intuition that makes me prefer ‘magnitude’. I feel like I had a better reason for this a while ago and forgot the reason I chose vector magnitude L2 normalization.
If I haven’t already done it and forgotten where the results are, I should run my random election simulator on IRNR normalized by both modes and see which one produces better outcomes.

> On Dec 17, 2015, at 4:37 PM, Forest Simmons <fsimmons at pcc.edu> wrote:
> 
> What if you used the L_1 norm instead of the L_2 norm to normalize the ballot vectors?  In other words, divide by the sum of the absolute values of the ratings instead of the root sum squares.