1. Excursions in Modern Mathematics Sixth Edition
Peter Tannenbaum

2. Chapter 4 The Mathematics of Apportionment
Making the Rounds

3. The Mathematics of Apportionment Outline/learning Objectives
To state the basic apportionment problem.
To implement the methods of Hamilton, Jefferson, Adams and Webster to solve apportionment problems.
To state the quota rule and determine when it is satisfied.
To identify paradoxes when they occur.
To understand the significance of Balanski and Young’s impossibility theorem.

4. The Mathematics of Apportionment
4.1 Apportionment Problems

5. The Mathematics of Apportionment
Apportion- two critical elements
in the definition of the word
We are dividing and assigning things.
We are doing this on a proportional basis and in a planned, organized fashion.

6. The Mathematics of Apportionment
Table 4-3 Republic of Parador (Population by State)
State A B C D E F
Total
Population
1,646,000
6,936,000
154,000
2,091,000
685,000
988,000
12,500,000
The first step is to find a good unit of measurement. The most
natural unit of measurement is the ratio of population to seats.
We call this ratio the standard divisor SD = P/M
SD = 12,500,000/250 = 50,000

7. The Mathematics of Apportionment
Table 4-4 Republic of Parador: Standard Quotas for Each State (SD = 50,000)
State A B C D E F
Total
Population
1,646,000
6,936,000
154,000
2,091,000
685,000
988,000
12,500,000
Standard quota
32.92
138.72
3.08
41.82
13.70
19.76
250
For example, take state A. To find a state’s standard quota,
we divide the state’s population by the standard divisor:
Quota = population/SD = 1,646,000/50,000 = 32.92

8. The Mathematics of Apportionment
The “states.” This is the term we will use to describe the players involved in the apportionment.
The “seats.” This term describes the set of M identical, indivisible objects that are being divided among the N states.
The “populations.” This is a set of N positive numbers which are used as the basis for the apportionment of the seats to the states.

9. The Mathematics of Apportionment
Upper quotas. The quota rounded down and is denoted by L.
Lower quotas. The quota rounded up and denoted by U.
In the unlikely event that the quota is a whole number, the lower and upper quotas are the same.

10. The Mathematics of Apportionment
4.2 Hamilton’s Method and the Quota Rule

11. The Mathematics of Apportionment
Hamilton’s Method
Step 1. Calculate each state’s standard quota.
State
Population
Step1
Quota A
1,646,000
32.92 B
6,936,000
138.72 C
154,000
3.08 D
2,091,000
41.82 E
685,000
13.70 F
988,000
19.76
Total
12,500,000
250.00

12. The Mathematics of Apportionment
State
Population
Step1
Quota
Step 2
Lower Quota A
1,646,000
32.92 32 B
6,936,000
138.72
138 C
154,000
3.08 3 D
2,091,000
41.82 41 E
685,000
13.70 13 F
988,000
19.76 19
Total
12,500,000
250.00
246
Hamilton’s Method
Step 2. Give to each state its lower quota.

13. The Mathematics of Apportionment
Step 3. Give the surplus seats to the state with the largest fractional parts until there are no more surplus seats.
State
Population
Step1
Quota
Step 2
Lower Quota
Fractional
parts
Step 3
Surplus
Hamilton
apportionment A
1,646,000
32.92 32
0.92
First 33 B
6,936,000
138.72
138
0.72
Last
139 C
154,000
3.08 3
0.08 D
2,091,000
41.82 41
0.82
Second 42 E
685,000
13.70 13
0.70 F
988,000
19.76 19
0.76
Third 20
Total
12,500,000
250.00
246
4.00 4
250

14. The Mathematics of Apportionment
The Quota Rule
No state should be apportioned a number of seats smaller than its lower quota or larger than its upper quota. (When a state is apportioned a number smaller than its lower quota, we call it a lower-quota violation; when a state is apportioned a number larger than its upper quota, we call it an upper-quota violation.)

15. The Mathematics of Apportionment
4.3 The Alabama and Other Paradoxes

16. The Mathematics of Apportionment
The most serious (in fact, the fatal) flaw of Hamilton's method is commonly know as the Alabama paradox. In essence, the paradox occurs when an increase in the total number of seats being apportioned, in and of itself, forces a state to lose one of its seats.

17. The Mathematics of Apportionment
With M = 200 seats and SD = 100, the apportionment under Hamilton’s method
State
Population
Step 1
Step 2
Step 3
Apportionment
Bama
940
9.4 9 1 10
Tecos
9,030
90.3 90
Ilnos
10,030
100.3
100
Total
20,000
200.0
199
200

18. The Mathematics of Apportionment
With M = 201 seats and SD = 99.5, the apportionment under Hamilton’s method
State
Population
Step 1
Step 2
Step 3
Apportionment
Bama
940
9.45 9
Tecos
9,030
90.75 90 1 91
Ilnos
10,030
100.80
100
101
Total
20,000
201.00
199 2
201

19. The Mathematics of Apportionment
The Hamilton’s method can fall victim to two other paradoxes called
the population paradox- when state A loses a seat to state B even though the population of A grew at a higher rate than the population of B.
the new-states paradox- that the addition of a new state with its fair share of seats can, in and of itself, affect the apportionments of other states.

20. The Mathematics of Apportionment
4.4 Jefferson’s Method

21. The Mathematics of Apportionment
Jefferson’s Method
Step 1. Find a “suitable” divisor D. [ A suitable or modified divisor is a divisor that produces and apportionment of exactly M seats when the quotas (populations divided by D) are rounded down.

22. The Mathematics of Apportionment
Jefferson’s Method
Step 2. Each state is apportioned its lower quota.
State
Population
Standard Quota
(SD = 50,000)
Lower Quota
Modified Quota
(D = 49,500)
Hamilton
apportionment A
1,646,000
32.92 32
33.25 33 B
6,936,000
138.72
138
140.12
140 C
154,000
3.08 3
3.11 D
2,091,000
41.82 41
42.24 42 E
685,000
13.70 13
13.84 F
988,000
19.76 19
19.96
Total
12,500,000
250.00
246
250

23. The Mathematics of Apportionment
Bad News- Jefferson’s method can produce upper-quota violations!
To make matters worse, the upper-quota violations tend to consistently favor the larger states.

24. The Mathematics of Apportionment
4.5 Adam’s Method

25. The Mathematics of Apportionment
Adam’s Method
Step 1. Find a “suitable” divisor D. [ A suitable or modified divisor is a divisor that produces and apportionment of exactly M seats when the quotas (populations divided by D) are rounded up.
State
Population
Quota
(D = 50,500) A
1,646,000
32.59 B
6,936,000
137.35 C
154,000
3.05 D
2,091,000
41.41 E
685,000
13.56 F
988,000
19.56
Total
12,500,000

26. The Mathematics of Apportionment
Adam’s Method
Step 2. Each state is apportioned its upper quota.
State
Population
Quota
(D = 50,500)
Upper Quota
(D = 50,700)
Adam’s
apportionment A
1,646,000
32.59 33
32.47 B
6,936,000
137.35
138
136.80
137 C
154,000
3.05 4
3.04 D
2,091,000
41.41 42
41.24 E
685,000
13.56 14
13.51 F
988,000
19.56 20
19.49
Total
12,500,000
251
250

27. The Mathematics of Apportionment
Bad News- Adam’s method can produce lower-quota violations!
We can reasonably conclude that Adam’s method is no better (or worse) than Jefferson’s method– just different.

28. The Mathematics of Apportionment
4.6 Webster’s Method

29. The Mathematics of Apportionment
Webster’s Method
Step 1. Find a “suitable” divisor D. [ Here a suitable divisor means a divisor that produces an apportionment of exactly M seats when the quotas (populations divided by D) are rounded the conventional way.

30. The Mathematics of Apportionment
Step 2. Find the apportionment of each state by rounding its quota the conventional way.
State
Population
Standard Quota
(D = 50,000)
Nearest
Integer
Quota
(D = 50,100)
Webster’s
apportionment A
1,646,000
32.92 33
32.85 B
6,936,000
138.72
139
138.44
138 C
154,000
3.08 3
3.07 D
2,091,000
41.82 42
41.74 E
685,000
13.70 14
13.67 F
988,000
19.76 20
19.72
Total
12,500,000
250.00
251
250

31. The Mathematics of Apportionment Conclusion
Covered different methods to solve apportionment problems
named after Alexander Hamilton, Thomas Jefferson, John Quincy Adams, and Daniel Webster.
Examples of divisor methods
based on the notion divisors and quotas can be modified to work under different rounding methods

32. The Mathematics of Apportionment Conclusion (continued)
Balinski and Young’s impossibility theorem
An apportionment method that does not violate the quota rule and does not produce any paradoxes is a mathematical impossibility.