# Summability criterion

The summability criterion is a criterion about the counting process of voting systems. Unlike most other voting system criteria, it does not relate to the end result, only to the process.

Each vote should map onto a summable array, where the summation operation is associative and commutative, and the winner should be determined from the array sum for all votes cast. An election method is kth-order summable if there exists a constant c such that in any election with n candidates, the required size of the array is at most cnk. If there is no value of k for which the method is kth-order summable, the method is non-summable.

Strictly speaking, a method is kth-order summable if an election involving $V$ voters and $c$ candidates can be stored in a data structure (a summary) that requires $O(\log(V) \cdot c^k)$ bits in total, where there exists a summation operator that takes any two such summaries and produces a third for the combined election, and the election method itself can use these summaries instead of ballot sets to produce the same results. This definition closes the obvious loophole of using a few very large numbers to store more data than would otherwise be permitted.

## Multi-winner generalizations and results

Forest Simmons has constructed a color-proportional method that's summable in $O(\log(V) \cdot c)$ for any number of seats. The same approach can be generalized to make a Droop-proportional method that's fixed-parameter summable in $O(\log(V) \cdot c^s)$ where $s$ is the number of seats, by keeping a separate count for each solid coalition of size $s$ or less.

It's unknown whether it's possible to construct a Droop-proportional method that's summable in $O(\log(V) \cdot c^k \cdot s^n)$ for constant $k$ and $n$ .

## Summable methods

Methods and their summability levels.
k=1 k=2 k=3 non-summable

## Examples

In plurality voting, each vote is equivalent to a one-dimensional array with a 1 in the element for the selected candidate, and a 0 for each of the other candidates. The sum of the arrays for all the votes cast is simply a list of vote counts for each candidate. Approval voting is the same as plurality voting except that more than one candidate can get a 1 in the array for each vote. Each of the selected or "approved" candidates gets a 1, and the others get a 0.

In Schulze, each vote is equivalent to a two-dimensional array referred to as a pairwise matrix. If candidate A is ranked above candidate B, then the element in the A row and B column gets a 1, while the element in the B row and A column gets a 0. The pairwise matrices for all the votes are summed, and the winner is determined from the resulting pairwise matrix sum.

IRV does not comply with the summability criterion. In the IRV system, a count can be maintained of identical votes, but votes do not correspond to a summable array. The total possible number of unique votes grows factorially with the number of candidates.

Since IRV does not comply with the summability criterion, it is silly to try to apply that criterion in that case.

## Importance of summability

The summability criterion addresses implementation logistics. Election methods with lower summability levels are substantially easier to implement with integrity than methods with higher summability levels or methods that are non-summable.

Suppose, for example, that the number of candidates is ten. Under first-order summable methods like plurality or Approval voting, the votes at any level (precinct, ward, county, etc.) can be compressed into a list of ten numbers. For Schulze, a 10×10 matrix is needed. In an IRV system, however, each precinct would need to send a list of ten numbers, the number of first-place votes for each candidate. The central system would then return to each precinct a candidate to eliminate. Each precinct would then return the first-place votes for each of the nine remaining candidates, and receive another candidate to eliminate. This would be repeated at most 9 times. This is more than the others.

IRV therefore requires more data transfer and storage than the other methods. The biggest challenge in using computers for public elections will always be security and integrity. If N-1 times more data needs to be transferred and stored, verification becomes more difficult and the potential for fraudulent tampering becomes slightly greater.

To illustrate this point, consider the verification of a vote tally for a national office. In a plurality election, each precinct verifies its vote count. This can be an open process where The counts for each precinct in a county can then be added to determine the county totals, and anyone with a calculator or computer can verify that the totals are correct. The same process is then repeated at the state level and the national level. If the votes are verified at the lowest (precinct) level, the numbers are available to anyone for independent verification, and election officials could never get away with "fudging" the numbers. Of course, if verified images of all the ballots are available to the public, then the whole counting process is available to anyone for independent verification, for any voting system.

### Recounts

In first-order summable election systems, adding new ballots to the count (say, ballots that were found after the initial count, or late absentee ballots, or ballots that were initially ruled invalid) is as simple as "summing" the original result with the newly-found ballots. Under non-summable systems, though, finding new ballots means all ballots might possibly need to be recounted. This is not a big problem for computer recounts, but manual recounts can be extremely time-consuming and expensive.

Some parts of this article are derived with permission from text at http://electionmethods.org