Distributed Multi-Voting
Distributed Multi-Voting (DMV) is a Single-Winner and Multi-Winner voting system.
This voting method consists of evaluating all possible elections (subset of candidates) to find out which candidate loses the most and then eliminate him; by repeating the procedure several times, 1 or more winners (candidates left) are obtained.
Procedure
Each voter has 100 points to distribute among the candidates according to his preferences. All candidates in the vote have 0 points by default.
- For each single vote, get the normalized votes on all subsets conteining at least 2 candidates. Add up the points for each candidate of the normalized votes, obtaining the converted original vote.
- After obtaining all the converted original votes, the candidate with the lowest sum, of the converted votes, loses.
- Eliminate the loser from all the original votes. Repeat the whole process from the beginning, leaving as many winner as you like.
% of victory: got the winners, eliminate the losers from all the original votes and normalize. The % of victory are obtained from the sum of the points for each candidate.
Normalization
Given a vote like this: A[60],B[30],C[10],D[0] to normalize it to the subset of candidates A,B,C you have to:
- set the candidate (s) with the lowest score between A,B,C to 0.
- apply the following formula on the other candidates:
S = sum of the points of the candidates. v0 = value of candidate X, before normalization v1 = value of candidate X, after normalization. Failed to parse (unknown function "\begin{equation}"): {\displaystyle \begin{equation} v1=\frac{v0}{S} \cdot 100 \end{equation}}
In normalization for the % of victory, use the same formula without setting the candidate with the lowest score to 0.
If the candidates of the subset, in a certain vote, all have the same score different from 0 then, before normalization, don’t set the lowest score to 0.