Phragmen's voting rules: Difference between revisions

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* '''max-Phragmén''': The non-sequential variant of seq-Phragmen: the objective is the same (minimize the maximal load), but it's treated as a global optimization problem.
* '''max-Phragmén''': The non-sequential variant of seq-Phragmen: the objective is the same (minimize the maximal load), but it's treated as a global optimization problem.
* '''var-Phragmén''': [[Ebert's method]]. This variant minimizes the variance of the load distribution.
* '''var-Phragmén''': [[Ebert's method]]. This variant minimizes the variance of the load distribution.
* [[Sequential Ebert]]: The sequential variant of Ebert's method.


In addition, these variants are also described on Electowiki:
Ebert's method is nonmonotone, so there's also a variant that restores [[monotonicity]]: [[PAMSAC]].
* [[Sequential Ebert]]: The sequential variant of Ebert's method.
* [[PAMSAC]]: An Ebert variant, developed by [[Toby Pereira]], that restores [[monotonicity]].


==Further Reading==
==Further Reading==

Revision as of 16:04, 19 March 2022

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Phragmén's method is one of the three common interpretations of non-partisan Proportional representation. It is named after the inventor Lars Edvard Phragmén. It was devised as a solution to a flaw he found in Thiele's method.

Phragmén describes his method on page 88 of his original work [1]. A translated and revised into modern terminology definition is as follows

  1. The ballots are Approval voting i.e. each ballot lists the set of candidates that voter "approves."
  2. Later on, we shall associate a "cost" with each ballot. (Phragmén used the Swedish word "belastning." Other people often prefer to translate this into the English word "load" rather than "cost.") All ballots initially have cost=0.
  3. Seats are elected sequentially. Perform steps 4-6 until all seats are filled:
  4. As soon as any candidate is elected, the N ballots that approved him have 1/N added to each of their costs. (Note: at any moment, the sum of all the ballot costs, equals the number of seats filled so far. This fact can help with checking one's calculations.)
  5. [This step is really peculiar, and perhaps things would be better if it were omitted.] Immediately after a candidate is elected, we then redistribute the costs among his approvers, to make their ballots each have equal costs.
  6. The candidate who wins the next seat is the one whose N supporters' ballots will have the least average cost. (So, for example, the first winner is simply the most-approved candidate, because if he is approved by N voters the average cost per approving-ballot is 1/N, which is minimal because N is maximal.)

In the case of a single candidate to be elected, Phragméns method reduces to Approval voting (because the candidate resulting in the minimal load on each voter is the one with most voters to share the load).

This method is the only sequential multi-member system proven to pass proportional justified representation.

Variations and implementations

As described above, Phragmén's is a method that attempts to ensure that the winners' support are as widely distributed as possible. This can be done in different ways, and thus there exist different methods that follow the same broad principle. Brill et al.[2] identify the following variants:

  • seq-Phragmén: Phragmén's original method, described above. It attempts to minimize the maximal load by sequentially electing candidates.
  • max-Phragmén: The non-sequential variant of seq-Phragmen: the objective is the same (minimize the maximal load), but it's treated as a global optimization problem.
  • var-Phragmén: Ebert's method. This variant minimizes the variance of the load distribution.

In addition, these variants are also described on Electowiki:

Further Reading

References

  1. Phragmén, Edvard (1899). "Till frågan om en proportionell valmetod" (PDF). Statsvetenskaplig Tidskrift. 2 (2): 297–305.
  2. Brill, Markus; Freeman, Rupert; Janson, Svante; Lackner, Martin. "Phragmén's Voting Methods and Justified Representation". Association for the Advancement of Artificial Intelligence. Retrieved 2020-02-04.