Biproportional apportionment

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Biproportional apportionment is a proportional representation method to allocate seats in proportion to two separate characteristics. That is, for two different partitions each part receives the proportional number of seats within the total number of seats. For instance, this method could give proportional results by party and by region, or by party and by gender/ethnicity, or by any other pair of characteristics.

Contents

  1. Example: proportional by party and by region
    • Each party's share of seats is proportional to its total votes.
    • Each region's share of seats is proportional to its total votes
      • (or this could be based on its population-size or other criteria).
  2. Then, as nearly as possible given the totals for each region and each party:
    • Each region's seats are allocated among parties in proportion to that region's votes for those parties. (The region's seats go to locally popular parties.)
    • Each party's seats are allocated among regions in proportion to that party's votes in those regions. (The party's seats are in regions where it is most popular.)

Process

Suppose that the method is to be used to give proportional results by party and by region.

Each party nominates a candidate list for every region. The voters vote for the parties of their region (and/or for individual candidates, in an open list or local list system).

The results are calculated in two steps:

In the so called upper apportionment the seats for each party (over all regions) and the seats for each region (from all parties) are determined.
In the so called lower apportionment the seats are distributed to the regional party list respecting the results from the upper apportionment.

This can be seen as globally adjusting the voting power of each party's voters by the minimum amount necessary so that the region-by-region results become proportional by party.

Upper apportionment

In the upper apportionment the seats for each party are computed with a highest averages method (for example the Sainte-Laguë method). This determines how many of all seats each party deserves due to the total of all their votes (that is the sum of the votes for all regional lists of that party). Analogically, the same highest averages method is used to determine how many of all seats each region deserves.

Note, that the results from the upper apportionment are final results for the number of the seats of one party (and analogically for the number of the seats of one region) within the whole voting area, the lower apportionment will only determine in which particular regions the party seats are allocated. Thus, after the upper apportionment is done, the final strength of a party/region within the parliament is definite.

Lower apportionment

The lower apportionment has to distribute the seats to each regional party list in a way that respects both the apportionment of seats to the party and the apportionment of seats to the regions.

The result is obtained by an iterative process. Initially, for each region a regional divisor is chosen using the highest averages method for the votes allocated to each regional party list in this region. For each party a party divisor is initialized with 1.

Effectively, the objective of the iterative process is to modify the regional divisors and party divisors so that

The following two correction steps are executed until this objective is satisfied:

Using the Sainte-Laguë method, this iterative process is guaranteed to terminate with appropriate seat numbers for each regional party list.

Specific example

Suppose there are three parties A, B and C and three regions I, II and III and that there are 20 seats are to be distributed and that the Sainte-Laguë method is used. The votes for the regional party lists are as follows:

PartyRegionTotal
IIIIII
A12345815983
B9127144142040
C312255215782
total1347101414443805

Upper apportionment

For the upper apportionment, the overall seat number for the parties and the regions are determined.

Since there are 3805 voters and 20 seats, there are 190 (rounded) voters per seat. Thus the results for the distribution of the party seats is:

PartyABC
#votes9832040782
#votes/divisor5.210.74.1
#seats5114

Using the divisor 190, the results for the distribution of the region seats is:

RegionIIIIII
#votes134710141444
#votes/divisor7.15.37.6
#seats758

Lower apportionment

Initially, regional divisors have to be found to distribute the seats of each region to the regional party lists. In the tables, for each regional party list, there are two cells, the first shows the number of votes and the second the number of seats allocated.

Partyregion
IIIIII
A12314508155
B912471444142
C312225512151
total134771014514448
regional divisor205200180

Now, the party divisors are initialized with ones and the number of seats within each party is checked (that is, compared to the number computed in the upper apportionment):

Partyregiontotalparty

divisor

IIIIII
A1231450815598361
B9124714441422040101
C31222551215178241
total134771014514448380520
regional divisor205200180

Since not all parties have the correct number of seats, a correction step has to be executed: For parties A and B, the divisors are to be adjusted. The divisor for A has to be raised and the divisor for B has to be lowered:

Partyregiontotalparty

divisor

IIIIII
A1231450815498351.1
B9125714441422040110.95
C31222551215178241
total134781014514447380520
regional divisor205200180

Now, the divisors for regions I and III have to be modified. Since region I has one seat too much (8 instead of the 7 seats computed in the upper apportionment), its divisor has to be raised; in opposite, the divisor for region III has to be lowered.

Partyregiontotalparty

divisor

IIIIII
A1231450815498351.1
B9125714441432040120.95
C31212551215178231
total134771014514448380520
regional divisor210200170

Again, the divisors for the parties have to be adjusted:

Partyregiontotalparty

divisor

IIIIII
A1231450815498351.1
B9124714441432040110.97
C31222551215178240.98
total134771014514448380520
regional divisor210200170

Now, the numbers of seats for the three parties and the three regions match the numbers computed in the upper apportionment. Thus, the iterative process is completed.

The final seat numbers are:

#seatsregiontotal
PartyIIIIII
A1045
B44311
C2114
total75820

Usage

A method of biproportional appointment that was proposed in 2003 by German mathematician Friedrich Pukelsheim [1] is now used for cantonal and municipal elections in some cantons of Switzerland, i.e. Zurich (since 2006), Aargau and Schaffhausen (since 2008), Nidwalden, Zug (since 2013), Schwyz (since 2015) and Valais (since 2017).

Biproportional appointment is also used in national elections for the Bulgarian National Assembly and the Parliament of Finland.[ citation needed ]

Fair majority voting

Fair majority voting is a biproportional apportionment method with single-member regions called "districts", so each district has exactly one representative. It was proposed in 2008 by Michel Balinski (who also invented the single-winner voting system called majority judgment) as a way to eliminate the power of gerrymandering, especially in the United States. [2]

Related Research Articles

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<span class="mw-page-title-main">Party-list proportional representation</span> Family of voting systems

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The D'Hondt method, also called the Jefferson method or the greatest divisors method, is a method for allocating seats in parliaments among federal states, or in party-list proportional representation systems. It belongs to the class of highest-averages methods.

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<span class="mw-page-title-main">Electoral system</span> Method by which voters make a choice between options

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Mathematics of apportionment describes mathematical principles and algorithms for fair allocation of identical items among parties with different entitlements. Such principles are used to apportion seats in parliaments among federal states or political parties. See apportionment (politics) for the more concrete principles and issues related to apportionment, and apportionment by country for practical methods used around the world.

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State-population monotonicity is a property of apportionment methods, which are methods of allocating seats in a parliament among federal states. The property says that, if the population of a state increases faster than that of other states, then it should not lose a seat. An apportionment method that fails to satisfy this property is said to have a population paradox.

Seat bias is a property describing methods of apportionment. These are methods used to allocate seats in a parliament among federal states or among political parties. A method is biased if it systematically favors small parties over large parties, or vice versa. There are various ways to compute the bias of apportionment methods.

Coherence, also called uniformity or consistency, is a criterion for evaluating rules for fair division. Coherence requires that the outcome of a fairness rule is fair not only for the overall problem, but also for each sub-problem. Every part of a fair division should be fair.

Vote-ratio monotonicity (VRM) is a property of apportionment methods, which are methods of allocating seats in a parliament among political parties. The property says that, if the ratio between the number of votes won by party A to the number of votes won by party B increases, then it should NOT happen that party A loses a seat while party B gains a seat.

Balance or balancedness is a property of apportionment methods, which are methods of allocating identical items between among agens, such as dividing seats in a parliament among political parties or federal states. The property says that, if two agents have exactly the same entitlements, then the number of items they receive should differ by at most one. So if two parties win the same number of votes, or two states have the same populations, then the number of seats they receive should differ by at most one.

References

  1. Gaffke, Norbert; Pukelsheim, Friedrich (2008-09-01). "Divisor methods for proportional representation systems: An optimization approach to vector and matrix apportionment problems". Mathematical Social Sciences. 56 (2): 166–184. doi:10.1016/j.mathsocsci.2008.01.004. ISSN   0165-4896.
  2. Balinski, Michel (2008-02-01). "Fair Majority Voting (or How to Eliminate Gerrymandering)". The American Mathematical Monthly. 115 (2): 97–113. doi:10.1080/00029890.2008.11920503. ISSN   0002-9890. S2CID   1139441.