Highest averages method

The highest averages, divisor, or divide-and-round methods ^[1] are a family of apportionment rules, i.e. algorithms for fair division of seats in a legislature between several groups (like political parties or states). ^{[1] [2]} More generally, divisor methods are used to round shares of a total to a fraction with a fixed denominator (e.g. percentage points, which must add up to 100). ^[2]

The methods aim to treat voters equally by ensuring legislators represent an equal number of voters by ensuring every party has the same seats-to-votes ratio (or divisor). ^{[3] : 30} Such methods divide the number of votes by the number of votes needed to win a seat. The final apportionment. In doing so, the method approximately maintains proportional representation, meaning that a party with e.g. twice as many votes will win about twice as many seats. ^{[3] : 30}

The divisor methods are generally preferred by social choice theorists and mathematicians to the largest remainder methods, as they produce more-proportional results by most metrics and are less susceptible to apportionment paradoxes. ^{[3] [4] [5] [6]} In particular, divisor methods avoid the population paradox and spoiler effects, unlike the largest remainder methods. ^[5]

History

Divisor methods were first invented by Thomas Jefferson to comply with a constitutional requirement, that states have at most one representative per 30,000 people. His solution was to divide each state's population by 30,000 before rounding down. ^{[3] : 20}

Apportionment would become a major topic of debate in Congress, especially after the discovery of pathologies in many superficially-reasonable rounding rules. ^{[3] : 20} Similar debates would appear in Europe after the adoption of proportional representation, typically as a result of large parties attempting to introduce thresholds and other barriers to entry for small parties. ^[7] Such apportionments often have substantial consequences, as in the 1870 reapportionment, when Congress used an ad-hoc apportionment to favor Republican states. ^[8] Had each state's electoral vote total been exactly equal to its entitlement, or had Congress used Webster's method or a largest remainders method (as it had since 1840), the 1876 election would have gone to Tilden instead of Hayes. ^{[8] [9] [3] : 3, 37}

Definitions

The two names for these methods—highest averages and divisors—reflect two different ways of thinking about them, and their two independent inventions. However, both procedures are equivalent and give the same answer. ^[1]

Divisor methods are based on rounding rules, defined using a signpost sequence post(k), where k ≤ post(k) ≤ k+1. Each signpost marks the boundary between natural numbers, with numbers being rounded down if and only if they are less than the signpost. ^[2]

Divisor procedure

The divisor procedure apportions seats by searching for a divisor or electoral quota . This divisor can be thought of as the number of votes a party needs to earn one additional seat in the legislature, the ideal population of a congressional district, or the number of voters represented by each legislator. ^[1]

If each legislator represented an equal number of voters, the number of seats for each state could be found by dividing the population by the divisor. ^[1] However, seat allocations must be whole numbers, so to find the apportionment for a given state we must round (using the signpost sequence) after dividing. Thus, each party's apportionment is given by: ^[1]

${\displaystyle {\text{seats}}=\operatorname {round} \left({\frac {\text{votes}}{\text{divisor}}}\right)}$

Usually, the divisor is initially set to equal the Hare quota. However, this procedure may assign too many or too few seats. In this case the apportionments for each state will not add up to the total legislature size. A feasible divisor can be found by trial and error. ^[10]

Highest averages procedure

With the highest averages algorithm, every party begins with 0 seats. Then, at each iteration, we allocate a seat to the party with the highest vote average, i.e. the party with the most votes per seat . This method proceeds until all seats are allocated. ^[1]

However, it is unclear whether it is better to look at the vote average before assigning the seat, what the average will be after assigning the seat, or if we should compromise with a continuity correction. These approaches each give slightly different apportionments. ^[1] In general, we can define the averages using the signpost sequence:

${\displaystyle {\text{average}}:={\frac {\text{votes}}{\operatorname {post} ({\text{seats}})}}}$

With the highest averages procedure, every party begins with 0 seats. Then, at each iteration, we allocate a seat to the party with the highest vote average, i.e. the party with the most votes per seat. This method proceeds until all seats are allocated. ^[1]

Specific methods

While all divisor methods share the same general procedure, they differ in the choice of signpost sequence and therefore rounding rule. Note that for methods where the first signpost is zero, every party with at least one vote will receive a seat before any party receives a second seat; in practice, this typically means that every party must receive at least one seat, unless disqualified by some electoral threshold. ^[2]

Divisor formulas

Method	Signposts	Rounding of Seats	Approx. first values
Adams	k	Up	0.00 1.00 2.00 3.00
Dean	2÷(1⁄k + 1⁄k+1)	Harmonic	0.00 1.33 2.40 3.43
Huntington–Hill	${\displaystyle {\sqrt {k(k+1)}}}$	Geometric	0.00 1.41 2.45 3.46
Stationary (e.g. r = 1⁄3)	k + r	Weighted	0.33 1.33 2.33 3.33
Webster/Sainte-Laguë	k + 1⁄2	Arithmetic	0.50 1.50 2.50 3.50
Power mean (e.g. p = 2)	${\textstyle {\sqrt[{p}]{(k^{p}+(k+1)^{p})/2}}}$	Power mean	0.71 1.58 2.55 3.54
Jefferson/D'Hondt	k + 1	Down	1.00 2.00 3.00 4.00

Jefferson (D'Hondt) method

Main article: D'Hondt method

Thomas Jefferson was the first to propose a divisor method, in 1792; ^[1] it was later independently developed by Belgian political scientist Victor d'Hondt in 1878. It assigns the representative to the list that would be most underrepresented at the end of the round. ^[1] It remains the most-common method for proportional representation to this day. ^[1]

Jefferson's method uses the sequence ${\displaystyle \operatorname {post} (k)=k+1}$, i.e. (1, 2, 3, ...), ^[11] which means it will always round a party's apportionment down. ^[1]

Jefferson's apportionment never falls below the lower end of the ideal frame, and it minimizes the worst-case overrepresentation in the legislature. ^[1] However, it performs poorly when judged by most other metrics of proportionality. ^[12] The rule typically gives large parties an excessive number of seats, with their seat share often exceeding their entitlement rounded up. ^{[3] : 81}

This pathology led to widespread mockery of Jefferson's method when it was learned Jefferson's method could "round" New York's apportionment of 40.5 up to 42, with Senator Mahlon Dickerson saying the extra seat must come from the "ghosts of departed representatives". ^{[3] : 34}

Adams' method

Adams' method was conceived of by John Quincy Adams after noticing Jefferson's method allocated too few seats to smaller states. ^[13] It can be described as the inverse of Jefferson's method; it awards a seat to the party that has the most votes per seat before the new seat is added. The divisor function is post(k) = k, which is equivalent to always rounding up. ^[12]

Adams' apportionment never exceeds the upper end of the ideal frame, and minimizes the worst-case underrepresentation. ^[1] However, like Jefferson's method, Adams' method performs poorly according to most metrics of proportionality. ^[12] It also often violates the lower seat quota. ^[14]

Adams' method was suggested as part of the Cambridge compromise for apportionment of European parliament seats to member states, with the aim of satisfying degressive proportionality. ^[15]

Webster (Sainte-Laguë) method

Main article: Sainte-Laguë method

The Sainte-Laguë or Webster method, first described in 1832 by American statesman and senator Daniel Webster and later independently in 1910 by the French mathematician André Sainte-Lague, uses the fencepost sequence post(k) = k+.5 (i.e. 0.5, 1.5, 2.5); this corresponds to the standard rounding rule. Equivalently, the odd integers (1, 3, 5...) can be used to calculate the averages instead. ^{[1] [16]}

The Webster method produces more proportional apportionments than Jefferson's by almost every metric of misrepresentation. ^[17] As such, it is typically preferred to D'Hondt by political scientists and mathematicians, at least in situations where manipulation is difficult or unlikely (as in large parliaments). ^[18] It is also notable for minimizing seat bias even when dealing with parties that win very small numbers of seats. ^[19] The Webster method can theoretically violate the ideal frame, although this is extremely rare for even moderately-large parliaments; it has never been observed to violate quota in any United States congressional apportionment. ^[18]

In small districts with no threshold, parties can manipulate Webster by splitting into many lists, each of which wins a full seat with less than a Hare quota's worth of votes. This is often addressed by modifying the first divisor to be slightly larger (often a value of 0.7 or 1), which creates an implicit threshold. ^[20]

Huntington–Hill method

Main article: Huntington–Hill method

In the Huntington–Hill method, the signpost sequence is post(k) = √k (k+1), the geometric mean of the neighboring numbers. Conceptually, this method rounds to the integer that has the smallest relative (percent) difference. For example, the difference between 2.47 and 3 is about 19%, while the difference from 2 is about 21%, so 2.47 is rounded up. This method is used for allotting seats in the US House of Representatives among the states. ^[1]

The Huntington-Hill method tends to produce very similar results to the Webster method, except that it guarantees every state or party at least one seat (see Highest averages method § Zero-seat apportionments). When first used to assign seats in the House, the two methods produced identical results; in their second use, they differed only in assigning a single seat to Michigan or Arkansas. ^{[3] : 58}

Comparison of properties

Zero-seat apportionments

Huntington-Hill, Dean, and Adams' method all have a value of 0 for the first fencepost, giving an average of ∞. Thus, without a threshold, all parties that have received at least one vote will also receive at least one seat. ^[1] This property can be desirable (as when apportioning seats to states) or undesirable (as when apportioning seats to party lists in an election), in which case the first divisor may be adjusted to create a natural threshold. ^[21]

Bias

There are many metrics of seat bias. While the Webster method is sometimes described as "uniquely" unbiased, ^[18] this uniqueness property relies on a technical definition of bias, which is defined as the average difference between a state's number of seats and its seat entitlement. In other words, a method is called unbiased if the number of seats a state receives is, on average across many elections, equal to its seat entitlement. ^[18]

By this definition, the Webster method is the least-biased apportionment method, ^[19] while Huntington-Hill exhibits a mild bias towards smaller parties. ^[18] However, other researchers have noted that slightly different definitions of bias, generally based on percent errors, find the opposite result (The Huntington-Hill method is unbiased, while the Webster method is slightly biased towards large parties). ^{[19] [22]}

In practice, the difference between these definitions is small when handling parties or states with more than one seat. ^[19] Thus, both the Huntington-Hill and Webster methods can be considered unbiased or low-bias methods (unlike the Jefferson or Adams methods). ^{[19] [22]} A 1929 report to Congress by the National Academy of Sciences recommended the Huntington-Hill method, ^[23] while the Supreme Court has ruled the choice to be a matter of opinion. ^[22]

Comparison and examples

Example: Jefferson

The following example shows how Jefferson's method can differ substantially from less-biased methods such as Webster. In this election, the largest party wins 46% of the vote, but takes 52.5% of the seats, enough to win a majority outright against a coalition of all other parties (which together reach 54% of the vote). Moreover, it does this in violation of quota: the largest party is entitled only to 9.7 seats, but it wins 11 regardless. The largest congressional district is nearly twice the size of the smallest district. The Webster method shows none of these properties, with a maximum error of 22.6%.

Jefferson								Webster
Party	Yellow	White	Red	Green	Purple	Total		Party	Yellow	White	Red	Green	Purple	Total
Votes	46,000	25,100	12,210	8,350	8,340	100,000		Votes	46,000	25,100	12,210	8,350	8,340	100,000
Seats	11	6	2	1	1	21		Seats	9	5	3	2	2	21
Ideal	9.660	5.271	2.564	1.754	1.751	21		Ideal	9.660	5.271	2.564	1.754	1.751	21
Votes/Seat	4182	4183	6105	8350	8340	4762		Votes/Seat	5111	5020	4070	4175	4170	4762
% Error	13.0%	13.0%	-24.8%	-56.2%	-56.0%	(100.%)		(% Range)	-7.1%	-5.3%	15.7%	13.2%	13.3%	(22.6%)
Seats	Averages					Signposts		Seats	Averages					Signposts
1	46,000	25,100	12,210	8,350	8,340	1.00		1	92,001	50,201	24,420	16,700	16,680	0.50
2	23,000	12,550	6,105	4,175	4,170	2.00		2	30,667	16,734	8,140	5,567	5,560	1.50
3	15,333	8,367	4,070	2,783	2,780	3.00		3	18,400	10,040	4,884	3,340	3,336	2.50
4	11,500	6,275	3,053	2,088	2,085	4.00		4	13,143	7,172	3,489	2,386	2,383	3.50
5	9,200	5,020	2,442	1,670	1,668	5.00		5	10,222	5,578	2,713	1,856	1,853	4.50
6	7,667	4,183	2,035	1,392	1,390	6.00		6	8,364	4,564	2,220	1,518	1,516	5.50
7	6,571	3,586	1,744	1,193	1,191	7.00		7	7,077	3,862	1,878	1,285	1,283	6.50
8	5,750	3,138	1,526	1,044	1,043	8.00		8	6,133	3,347	1,628	1,113	1,112	7.50
9	5,111	2,789	1,357	928	927	9.00		9	5,412	2,953	1,436	982	981	8.50
10	4,600	2,510	1,221	835	834	10.00		10	4,842	2,642	1,285	879	878	9.50
11	4,182	2,282	1,110	759	758	11.00		11	4,381	2,391	1,163	795	794	10.50

Example: Adams

The following example shows a case where Adams' method fails to give a majority to a party winning 55% of the vote, again in violation of their quota entitlement.

Adams' Method								Webster Method
Party	Yellow	White	Red	Green	Purple	Total		Party	Yellow	White	Red	Green	Purple	Total
Votes	55,000	17,290	16,600	5,560	5,550	100,000		Votes	55,000	17,290	16,600	5,560	5,550	100,000
Seats	10	4	3	2	2	21		Seats	11	4	4	1	1	21
Ideal	11.550	3.631	3.486	1.168	1.166	21		Ideal	11.550	3.631	3.486	1.168	1.166	21
Votes/Seat	5500	4323	5533	2780	2775	4762		Votes/Seat	4583	4323	5533	5560	5550	4762
% Error	-14.4%	9.7%	-15.0%	53.8%	54.0%	(99.4%)		(% Range)	3.8%	9.7%	-15.0%	-15.5%	-15.3%	(28.6%)
Seats	Averages					Signposts		Seats	Averages					Signposts
1	∞	∞	∞	∞	∞	0.00		1	110,001	34,580	33,200	11,120	11,100	0.50
2	55,001	17,290	16,600	5,560	5,550	1.00		2	36,667	11,527	11,067	3,707	3,700	1.50
3	27,500	8,645	8,300	2,780	2,775	2.00		3	22,000	6,916	6,640	2,224	2,220	2.50
4	18,334	5,763	5,533	1,853	1,850	3.00		4	15,714	4,940	4,743	1,589	1,586	3.50
5	13,750	4,323	4,150	1,390	1,388	4.00		5	12,222	3,842	3,689	1,236	1,233	4.50
6	11,000	3,458	3,320	1,112	1,110	5.00		6	10,000	3,144	3,018	1,011	1,009	5.50
7	9,167	2,882	2,767	927	925	6.00		7	8,462	2,660	2,554	855	854	6.50
8	7,857	2,470	2,371	794	793	7.00		8	7,333	2,305	2,213	741	740	7.50
9	6,875	2,161	2,075	695	694	8.00		9	6,471	2,034	1,953	654	653	8.50
10	6,111	1,921	1,844	618	617	9.00		10	5,790	1,820	1,747	585	584	9.50
11	5,500	1,729	1,660	556	555	10.00		11	5,238	1,647	1,581	530	529	10.50
Seats	10	4	3	2	2			Seats	11	4	4	1	1

Example: All systems

The following shows a worked-out example for all voting systems. Notice how Huntington-Hill and Adams' methods give every party one seat before assigning any more, unlike Webster or Jefferson.

	Jefferson method							Webster method							Huntington–Hill method							Adams method
party	Yellow	White	Red	Green	Blue	Pink		Yellow	White	Red	Green	Blue	Pink		Yellow	White	Red	Green	Blue	Pink		Yellow	White	Red	Green	Blue	Pink
votes	47,000	16,000	15,900	12,000	6,000	3,100		47,000	16,000	15,900	12,000	6,000	3,100		47,000	16,000	15,900	12,000	6,000	3,100		47,000	16,000	15,900	12,000	6,000	3,100
seats	5	2	2	1	0	0		4	2	2	1	1	0		4	2	1	1	1	1		3	2	2	1	1	1
votes/seat	9,400	8,000	7,950	12,000	∞	∞		11,750	8,000	7,950	12,000	6,000	∞		11,750	8,000	15,900	12,000	6,000	3,100		15,667	8,000	7,950	12,000	6,000	3,100
seat	seat allocation							seat allocation							seat allocation							seat allocation
1	47,000							47,000							∞							∞
2	23,500								16,000							∞							∞
3		16,000								15,900							∞							∞
4			15,900					15,667										∞							∞
5	15,667										12,000								∞							∞
6				12,000				9,400												∞							∞
7	11,750							6,714							33,234							47,000
8	9,400											6,000			19,187							23,500
9		8,000							5,333						13,567								16,000
10			7,950							5,300						11,314								15,900

Stationary calculator

The following table allows the user to calculate the apportionment for any stationary signpost function. In other words, it rounds an apportionment if the value is above the selected bar.

Party	Yellow	White	Red	Green	Blue	Pink	Total
Votes	4600	1600	1550	1200	600	450
Vote share	%	%	%	%	%	%	100%
Seats
Entitlement							Round x >	0.5
votes⁄seat							Divisor:	600

Properties

Monotonicity

Divisor methods are generally preferred by mathematicians to largest remainder methods ^[24] because they are less susceptible to apportionment paradoxes. ^[5] In particular, divisor methods satisfy population monotonicity, i.e. voting for a party can never cause it to lose seats. ^[5] Such population paradoxes occur by increasing the electoral quota, which can cause different states' remainders to respond erratically. ^{[3] : Tbl.A7.2} Divisor methods also satisfy resource or house monotonicity, which says that increasing the number of seats in a legislature should not cause a state to lose a seat. ^{[5] [3] : Cor.4.3.1}

Min-Max inequality

Every divisor method can be defined using the min-max inequality. Letting brackets denote array indexing, an allocation is valid if-and-only-if: ^{[1] : 78–81}

max ⁠votes[party]/ post(seats[party])⁠ ≤ min ⁠votes[party]/ post(seats[party]+1)⁠

In other words, it is impossible to lower the highest vote average by reassigning a seat from one party to another. Every number in this range is a possible divisor. If the inequality is strict, the solution is unique; otherwise, there is an exactly tied vote in the final apportionment stage. ^{[1] : 83}

Method families

The divisor methods described above can be generalized into families.

Generalized average

In general, it is possible to construct an apportionment method from any generalized average function, by defining the signpost function as post(k) = avg(k, k+1). ^[1]

Stationary family

A divisor method is called stationary ^{[25] : 68} if for some real number ${\displaystyle r\in [0,1]}$, its signposts are of the form ${\displaystyle d(k)=k+r}$. The Adams, Webster, and d'Hondt methods are stationary, while Dean and Huntington-Hill are not. A stationary method corresponds to rounding numbers up if they exceed the weighted arithmetic mean of k and k+1. ^[1] Smaller values of r are friendlier to smaller parties. ^[19]

Danish elections allocate leveling seats at the province level using-member constituencies. It divides the number of votes received by a party in a multi-member constituency by 0.33, 1.33, 2.33, 3.33 etc. The fencepost sequence is given by post(k) = k+1⁄3; this aims to allocate seats closer to equally, rather than exactly proportionally. ^[26]

Power mean family

The power mean family of divisor methods includes the Adams, Huntington-Hill, Webster, Dean, and Jefferson methods (either directly or as limits). For a given constant p, the power mean method has signpost function post(k) = ^_p√k^p + (k+1)^p. The Huntington-Hill method corresponds to the limit as p tends to 0, while Adams and Jefferson represent the limits as p tends to negative or positive infinity. ^[1]

The family also includes the less-common Dean's method for p=-1, which corresponds to the harmonic mean. Dean's method is equivalent to rounding to the nearest average—every state has its seat count rounded in a way that minimizes the difference between the average district size and the ideal district size. For example: ^{[3] : 29}

The 1830 representative population of Massachusetts was 610,408: if it received 12 seats its average constituency size would be 50,867; if it received 13 it would be 46,954. So, if the divisor were 47,700 as Polk proposed, Massachusetts should receive 13 seats because 46,954 is closer to 47,700 than is 50,867.

Rounding to the vote average with the smallest relative error once again yields the Huntington-Hill method because |log(x⁄y)| = |log(y⁄x)|, i.e. relative differences are reversible. This fact was central to Edward V. Huntington's use of relative (instead of absolute) errors in measuring misrepresentation, and to his advocacy for Hill's rule: ^[27] Huntington argued the choice of apportionment method should not depend on how the equation for equal representation is rearranged, and only the relative error (minimized by Hill's rule) satisfies this property. ^{[3] : 53}

Stolarsky mean family

Similarly, the Stolarsky mean can be used to define a family of divisor methods that minimizes the generalized entropy index of misrepresentation. ^[28] This family includes the logarithmic mean, the geometric mean, the identric mean and the arithmetic mean. The Stolarsky means can be justified as minimizing these misrepresentation metrics, which are of major importance in the study of information theory. ^[29]

Modifications

Thresholds

Main article: Electoral threshold

Many countries have electoral thresholds for representation, where parties must win a specified fraction of the vote in order to be represented; parties with fewer votes than the threshold requires for representation are eliminated. ^[20] Other countries modify the first divisor to introduce a natural threshold; when using the Webster method, the first divisor is often set to 0.7 or 1.0 (the latter being called the full-seat modification). ^[20]

Majority-preservation clause

A majority-preservation clause guarantees any party winning a majority of the vote will receive at least half the seats in a legislature. ^[20] Without such a clause, it is possible for a party with slightly more than half the vote to receive just barely less than half the seats (if using a method other than D'Hondt). ^[20] This is typically accomplished by adding seats to the legislature until an apportionment that preserves the majority for a parliament is found. ^[20]

Quota-capped divisor method

Main article: Rank-index method § Quota-capped divisor method

A quota-capped divisor method is an apportionment method where we begin by assigning every state its lower quota of seats. Then, we add seats one-by-one to the state with the highest votes-per-seat average, so long as adding an additional seat does not result in the state exceeding its upper quota. ^[30] However, quota-capped divisor methods violate the participation criterion (also called population monotonicity)—it is possible for a party to lose a seat as a result of winning more votes. ^{[3] : Tbl.A7.2}

References

1. Pukelsheim, Friedrich (2017). "Divisor Methods of Apportionment: Divide and Round". Proportional Representation: Apportionment Methods and Their Applications. Cham: Springer International Publishing. pp. 71–93. doi:10.1007/978-3-319-64707-4_4. ISBN   978-3-319-64707-4 . Retrieved 2021-09-01.
2. Pukelsheim, Friedrich (2017). "From Reals to Integers: Rounding Functions, Rounding Rules". Proportional Representation: Apportionment Methods and Their Applications. Springer International Publishing. pp. 59–70. doi:10.1007/978-3-319-64707-4_3. ISBN   978-3-319-64707-4 . Retrieved 2021-09-01.
3. Balinski, Michel L.; Young, H. Peyton (1982). Fair Representation: Meeting the Ideal of One Man, One Vote . New Haven: Yale University Press. ISBN   0-300-02724-9.
4. Ricca, Federica; Scozzari, Andrea; Serafini, Paola (2017). "A Guided Tour of the Mathematics of Seat Allocation and Political Districting". In Endriss, Ulle (ed.). Trends in Computational Social Choice. Lulu.com. pp. 49–68. ISBN   978-1-326-91209-3. Archived from the original on 2024-10-08. Retrieved 2024-10-08.
5. Pukelsheim, Friedrich (2017). "Securing System Consistency: Coherence and Paradoxes". Proportional Representation: Apportionment Methods and Their Applications. Cham: Springer International Publishing. pp. 159–183. doi:10.1007/978-3-319-64707-4_9. ISBN   978-3-319-64707-4 . Retrieved 2024-05-10.
6. Dančišin, Vladimír (2017-01-01). "No-show paradox in Slovak party-list proportional system". Human Affairs. 27 (1): 15–21. doi:10.1515/humaff-2017-0002. ISSN   1337-401X.
7. Pukelsheim, Friedrich (2017). "Exposing Methods: The 2014 European Parliament Elections". Proportional Representation: Apportionment Methods and Their Applications. Cham: Springer International Publishing. pp. 1–40. doi:10.1007/978-3-319-64707-4_1. ISBN   978-3-319-64707-4 . Retrieved 2024-07-03.
8. Argersinger, Peter H., ed. (2012), ""Injustices and Inequalities": The Politics of Apportionment, 1870–1888", Representation and Inequality in Late Nineteenth-Century America: The Politics of Apportionment, Cambridge: Cambridge University Press, pp. 8–41, doi:10.1017/cbo9781139149402.002, ISBN   978-1-139-14940-2, archived from the original on 2018-06-07, retrieved 2024-08-04, Apportionment not only determined the power of different states in Congress but, because it allocated electors as well, directly affected the election of the president. Indeed, the peculiar apportionment of 1872, adopted in violation of the prevailing law mandating the method of allocating seats, was directly responsible for the 1876 election of Rutherford B. Hayes with a popular vote minority. Had the previous method been followed, even the Electoral Commission would have been unable to place Hayes in the White House.
9. Caulfield, Michael J. (2012). "What If? How Apportionment Methods Choose Our Presidents". The Mathematics Teacher. 106 (3): 178–183. doi:10.5951/mathteacher.106.3.0178. ISSN   0025-5769. JSTOR   10.5951/mathteacher.106.3.0178.
10. Pukelsheim, Friedrich (2017). "Targeting the House Size: Discrepancy Distribution". Proportional Representation: Apportionment Methods and Their Applications. Cham: Springer International Publishing. pp. 107–125. doi:10.1007/978-3-319-64707-4_6. ISBN   978-3-319-64707-4 . Retrieved 2024-05-10.
11. Gallagher, Michael (1991). "Proportionality, disproportionality and electoral systems" (PDF). Electoral Studies. 10 (1): 33–51. doi:10.1016/0261-3794(91)90004-C. Archived from the original (PDF) on 2016-03-04.
12. Gallagher, Michael (1992). "Comparing Proportional Representation Electoral Systems: Quotas, Thresholds, Paradoxes and Majorities" (PDF). British Journal of Political Science. 22 (4): 469–496. doi:10.1017/S0007123400006499. ISSN   0007-1234. S2CID   153414497.
13. "Apportioning Representatives in the United States Congress - Adams' Method of Apportionment | Mathematical Association of America". www.maa.org. Archived from the original on 9 June 2024.
14. Ichimori, Tetsuo (2010). "New apportionment methods and their quota property". JSIAM Letters. 2: 33–36. doi: 10.14495/jsiaml.2.33 . ISSN   1883-0617.
15. The allocation between the EU Member States of the seats in the European Parliament (PDF) (Report). European Parliament. 2011. Archived (PDF) from the original on 2024-05-12. Retrieved 2024-01-26.
16. Webster, André. "La représentation proportionnelle et la méthode des moindres carrés." Archived 2024-05-15 at the Wayback Machine Annales scientifiques de l'école Normale Supérieure. Vol. 27. 1910.
17. Pennisi, Aline (March 1998). "Disproportionality indexes and robustness of proportional allocation methods". Electoral Studies. 17 (1): 3–19. doi:10.1016/S0261-3794(97)00052-8. Archived from the original on 2024-04-24. Retrieved 2024-05-10.
18. Balinski, M. L.; Young, H. P. (January 1980). "The Sainte-Laguë method of apportionment". Proceedings of the National Academy of Sciences. 77 (1): 1–4. Bibcode:1980PNAS...77....1B. doi: 10.1073/pnas.77.1.1 . ISSN   0027-8424. PMC   348194 . PMID   16592744.
19. Pukelsheim, Friedrich (2017). "Favoring Some at the Expense of Others: Seat Biases". Proportional Representation: Apportionment Methods and Their Applications. Cham: Springer International Publishing. pp. 127–147. doi:10.1007/978-3-319-64707-4_7. ISBN   978-3-319-64707-4 . Retrieved 2024-05-10.
20. Pukelsheim, Friedrich (2017). "Tracing Peculiarities: Vote Thresholds and Majority Clauses". Proportional Representation: Apportionment Methods and Their Applications. Cham: Springer International Publishing. pp. 207–223. doi:10.1007/978-3-319-64707-4_11. ISBN   978-3-319-64707-4 . Retrieved 2024-05-10.
21. Pukelsheim, Friedrich (2017). "Truncating Seat Ranges: Minimum-Maximum Restrictions". Proportional Representation: Apportionment Methods and Their Applications. Cham: Springer International Publishing. pp. 225–245. doi:10.1007/978-3-319-64707-4_12. ISBN   978-3-319-64707-4 . Retrieved 2024-05-10.
22. Ernst, Lawrence R. (1994). "Apportionment Methods for the House of Representatives and the Court Challenges". Management Science. 40 (10): 1207–1227. doi:10.1287/mnsc.40.10.1207. ISSN   0025-1909. JSTOR   2661618. Archived from the original on 2024-05-10. Retrieved 2024-02-10.
23. Huntington, Edward V. (1929). "The Report of the National Academy of Sciences on Reapportionment". Science. 69 (1792): 471–473. Bibcode:1929Sci....69..471H. doi:10.1126/science.69.1792.471. ISSN   0036-8075. JSTOR   1653304. PMID   17750282.
24. Pukelsheim, Friedrich (2017). "Quota Methods of Apportionment: Divide and Rank". Proportional Representation: Apportionment Methods and Their Applications. Cham: Springer International Publishing. pp. 95–105. doi:10.1007/978-3-319-64707-4_5. ISBN   978-3-319-64707-4 . Retrieved 2024-05-10.
25. Pukelsheim, Friedrich (2017). "From Reals to Integers: Rounding Functions and Rounding Rules". Proportional Representation: Apportionment Methods and Their Applications. Cham: Springer International Publishing. pp. 59–70. doi:10.1007/978-3-319-64707-4_3. ISBN   978-3-319-64707-4 . Retrieved 2021-09-01.
26. "The Parliamentary Electoral System in Denmark". Archived from the original on 2016-08-28.
27. Lauwers, Luc; Van Puyenbroeck, Tom (2008). "Minimally Disproportional Representation: Generalized Entropy and Stolarsky Mean-Divisor Methods of Apportionment". SSRN Electronic Journal. doi:10.2139/ssrn.1304628. ISSN   1556-5068. S2CID   124797897.
28. Wada, Junichiro (2012-05-01). "A divisor apportionment method based on the Kolm–Atkinson social welfare function and generalized entropy". Mathematical Social Sciences. 63 (3): 243–247. doi:10.1016/j.mathsocsci.2012.02.002. ISSN   0165-4896.
29. Agnew, Robert A. (April 2008). "Optimal Congressional Apportionment". The American Mathematical Monthly. 115 (4): 297–303. doi:10.1080/00029890.2008.11920530. ISSN   0002-9890. S2CID   14596741.
30. Balinski, M. L.; Young, H. P. (1975-08-01). "The Quota Method of Apportionment". The American Mathematical Monthly. 82 (7): 701–730. doi:10.1080/00029890.1975.11993911. ISSN   0002-9890.