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大学生,数学建模,美国大学生数学建模竞赛,MCM/ICM,历年美赛特等奖O奖论文
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微信公众号:数学模型
For office use only
T1
T2
T3
T4
Team Control Number
73410
Problem Chosen
B
For office use only
F1
F2
F3
F4
2018
MCM/ICM
Summary Sheet
Language Population Projection and Location
Optimizaion Model Based on Inhomogeneous
Transition Matrix and Simulated Annealing
Algorithm
Summary
With the advent of increasingly accelerated globalization, the intricate ge-
ographic distributions of languages start to hamper international business op-
erations and cross-culture interactions. Comprehending the distribution dy-
namics has never been more crucial, yet projecting the distributions is difficult,
particularly due to the complicated composition of speakers, the influence of
various exogenous factors like the migration, government policies, economic
development, and eagerness to learn. Therefore, we establish a new model
to replace the projection model based purely on population, as it is not only
inaccurate but also invalid faced with the scarcity of supporting data.
Our model focuses on native speakers and non-native speakers of lan-
guages. We introduce the transition matrix to describe the transition between
native speakers and non-native speakers of different languages, because the
population growth of a language doesn’t solely come from natural births, but
also migration and learning. Additionally, we introduce two new groups:
learners and migrants to further analyze the transition. In the end, we intro-
duce a set of parameters to represent the exogenous influences, a new variable
to express time changes, and establish our inhomogeneous transition matrix.
It has been tested that our matrix only requires relatively little amount of
data input to function well. We employ the model to successfully project the
geographic distributions of languages in 2067, based on the data in 2017. In
the end, we adopt the simulated annealing algorithm to help our client, a large
multinational service corporation, select optimal location options for new of-
fices.
Keywords: Population of languages, Transition, Office locations
微信公众号:数学模型
Team # 73410 Page 1 of 27
Contents
1 Introduction 2
2 Preliminary Model 2
2.1 Notations and Symbol Description . . . . . . . . . . . . . . . . . . . . . . . 2
2.1.1 Symbol Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.1.2 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 General Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.3 Analysis of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.3.1 N=2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.4 Calculating and Simplifying the Model . . . . . . . . . . . . . . . . . . . . 6
2.5 The Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Modified Model 9
3.1 Notations and Symbool Description . . . . . . . . . . . . . . . . . . . . . . 9
3.1.1 Additional Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1.2 Additional Symbol Description . . . . . . . . . . . . . . . . . . . . . 10
3.2 Additional Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 Analysis of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.4 Calculating and Simplifying the Model . . . . . . . . . . . . . . . . . . . . 12
3.5 The Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.5.1 Graph of Population of Different Language Groups . . . . . . . . . 13
3.5.2 Graph of the Scale of Migration . . . . . . . . . . . . . . . . . . . . . 14
3.5.3 Distribution of Non-native English Speakers . . . . . . . . . . . . . 14
3.6 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.6.1 High Uniformity Scenario . . . . . . . . . . . . . . . . . . . . . . . . 15
3.6.2 Low Uniformity Scenario . . . . . . . . . . . . . . . . . . . . . . . . 16
4 Application of Our Model 17
4.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2 Symbol Descrption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.3 Calculating and Simplifying . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.3.1 The Year of 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.3.2 The Year of 2067 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5 Strengths and Weaknesses 20
6 Memo 21
Appendices 24
微信公众号:数学模型
Team # 73410 Page 2 of 27
Appendix A Tables 24
Appendix B Figures 26
1 Introduction
There are about 6,900 languages spoken on Earth nowadays. About half of the world’s
population take one of ten languages as their native language and much of the world
population also speaks a second language. However, because of a variety of influences,
the population of speakers of a language may increase or decrease over time. Our target
is to investigate trends of global languages and location options for new offices.
In part I, we compared our problem with the idea of Markov chain, and add transition
matrix to describe the population transition from native speakers of a language to second
language speakers of another language to native speakers of another language. Consid-
ering that transitions are inhomogeneous, we finally built up inhomogeneous transition
matrix and used this matrix to predict the populations of different languages and their
geographical distribution. In part II, based on prediction of our model in part I, we used
simulated annealing algorithm to find the best location options for new offices.
2 Preliminary Model
2.1 Notations and Symbol Description
2.1.1 Symbol Description
Symbol Descrption
N The number of languages in consideration
Y
(n)
i
(i = 1, 2, ..., N; n = 0, 1, 2...) The number of native speakers of language i in the
year of n
y
(n)
i
(i = 1, 2, ..., N; n = 0, 1, 2...) The number of non-native speakers of language i
in the year of n
Y
(n)
the state vector of the model in the year of n
A The transition matrix
α
ii
The annual birth rate of native speakers of language i
α
i(N+i)
The annual proportion of non-native speakers of
language i giving birth to native speakers of this
language
β
ii
The annual death rate of native speakers of language i
β
(N+i)(N+i)
The annual death rate of non-native speakers of
language i
γ
(N+i)j
The annual proportion (learning rate) of native speakers
of language j successfully starting to master language i
Γ
i
The total learning rate of language i
微信公众号:数学模型
Team # 73410 Page 3 of 27
2.1.2 Notations
Native speakers of A are individuals whose first language is A.
Non-native speakers of A are individuals whose first language is not A, but who master
A as a foreign language (Implying that the individual possesses advanced skills of
language A and is fluent in both speaking and writing).
2.2 General Assumptions
1. Speakers of any particular language can be categorized into two groups: native
speakers and non-native speakers.
2. The number of native speakers only increases out of natural birth, and all new-
born babies remain native speakers at the year of their birth (Leaving out rare cases
of prodigies who can instantly learn to speak foreign languages); The number of
native speakers is only reduced by death (Leaving out rare cases of postnatal first
language disability).
3. Native speakers of a certain language cannot be converted to non-native speakers
of this language, but can become non-native speakers of other languages.
4. The number of non-native speakers only increases out of postnatal learning, and
only decreases due to death (Leaving out rare cases of forgetting learnt foreign lan-
guages).
5. Non-native speakers of a certain language cannot be converted to native speakers
of this language, but can become non-native speakers of other languages.
6. Once an individual has mastered a new language, he becomes a non-native speaker
of this language, while remaining all previous identities.
7. No radical and unpredicted events will occur, causing utter shifting in the popula-
tion structure.
2.3 Analysis of the Problem
It is apparent that native speakers and non-native speakers of the same language are
more closely related, which mainly reflects in:
1. Parents usually raise their children to have the same first languages, or at least
languages they master. Therefore, we can assume that new native speakers can only
be given birth to by native speakers or non-native speakers of the same language
(Leaving out refugees, asylum seekers etc.).
2. It’s unlikely that native speakers of a certain language can abandon their first lan-
guage. Therefore, we can assume that no native speakers of a certain language
can be converted to non-native speakers of this language (Leaving out rare cases of
postnatal first language disability).
微信公众号:数学模型
Team # 73410 Page 4 of 27
Furthermore, A number of people who cannot master a certain language will be con-
verted to non-native speakers of this language through learning every year. There will
also be natural deaths causing the number of each group of people to drop.
2.3.1 N=2 Model
Take the simplest model with only two languages (N = 2) as an example, we are able to
plot the transition between different groups of people (See Figure 1).
Figure 1: Transition Between Different Groups with Only Two Languages
It can be concluded that this model much resembles the model of homogeneous Markow
chain (After converted to the new group, the element will remain in the previous groups).
The transition follows the following rules:
Y
(n+1)
=
Y
(n+1)
1
Y
(n+1)
2
y
(n+1)
1
y
(n+1)
2
=
1 + α
11
− β
11
0 α
13
0
0 1 + α
22
− β
22
0 α
24
0 γ
32
1 − β
33
0
γ
41
0 0 1 − β
44
Y
(n)
1
Y
(n)
2
y
(n)
1
y
(n)
2
= AY
(n)
(1)
In expression (1), parameters are set as follows:
1. α
11
, α
22
represents the annual birth rate of native speakers of language 1 and 2 re-
spectively.
2. β
11
, β
22
represents the annual death rate of native speakers of language 1 and 2
respectively.
3. α
13
, α
24
represents the ratio of non-native speakers of language 1 and 2 giving birth
to native speakers of corresponding language respectively (Assuming all births are
i
l
bi
th
)
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