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### 2024年美赛特等奖O奖论文:F-2413565——终止非法野生动物贸易 #### 概述本篇特等奖论文聚焦于非法野生动物贸易问题，探讨了如何通过数学建模的方法来寻找合适的组织并鼓励它们参与打击非法野生动物贸易活动。该研究针对这一全球性挑战提出了一套系统性的解决方案。 #### 背景与问题非法野生动物贸易对全球生物多样性和生态系统平衡造成了严重威胁。为了有效应对这一问题，需要动员更多力量参与到阻止非法野生动物贸易中来。然而，如何识别并说服潜在合作伙伴加入成为一大难题。 #### 解决方案与方法论为了解决这一问题，作者们采用了一系列创新的方法： 1. **客户三角档案模型**：构建了一个基于权力、资源和利益三个维度的“客户三角档案”模型，将潜在合作伙伴分为三类：政府、企业和非政府组织（NGOs）。在此基础上，为每一类客户设置了多个评估指标，并利用熵权法（Entropy Weight Method, EWM）确定各项指标的权重。例如，在权力维度下，国家法律（权重0.8967）、行业影响力（权重0.2248）、媒体平台推广能力（权重0.4349）等；在资源维度下，GDP（权重0.8789）、技术进步程度（权重0.1283）、志愿者网络规模（权重0.6878）等；在利益维度下，犯罪率降低及公共安全改善（权重0.2540）、声誉优化（权重0.1368）、生物多样性保护（权重0.7460）等。 2. **层次分析法（Analytic Hierarchy Process, AHP）**：接着，运用层次分析法系统地评估了三类不同客户的综合得分，从底层到顶层逐步计算。通过对来自265个国家、157家企业以及30个组织的数据进行评估，最终选出了具有最高综合得分的合作伙伴。出人意料的是，著名的奢侈品集团LVMH获得了最高分0.4157，其客户三角档案的规范化值为（0.44, 0.94, 0.24），表明它不仅在资源方面表现突出，在权力和利益方面也有不错的表现。 3. **双赢模型**：研究者还建立了一个双赢模型，引入了一些新的收益指标，如罚款收入和交易替代材料的消除等。例如，以苹果公司为例，讨论了如何通过技术创新减少对非法野生动物产品的需求，并探索了可能的盈利模式。 #### 结论与展望本研究提出的方法为打击非法野生动物贸易提供了一种新的视角和可行方案。通过数学建模的方法，不仅可以有效地识别出潜在的合作伙伴，还能激励这些合作伙伴积极参与到这一行动中来。未来，这种数学建模的方法可以进一步应用于其他领域的社会问题解决上，为全球可持续发展做出贡献。 #### 关键词 - 美国大学生数学建模竞赛 (MCM/ICM) - 非法野生动物贸易 - 客户三角档案模型 - 熵权法 (EWM) - 层次分析法 (AHP) - 双赢模型这篇特等奖论文通过对非法野生动物贸易问题的研究，展示了数学建模在解决实际问题中的强大应用潜力。通过一系列严谨的数学工具和技术的应用，研究者不仅找到了有效的解决方案，也为未来类似问题的解决提供了宝贵的经验和参考。

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Problem Chosen

2024

MCM/ICM

Summary Sheet

Team Control Number

2413565

Technique Innovations Terminating Illegal Wildlife Trade

The illegal wildlife trade does serious harm to global biodiversity and the balance of ecosystems,

and we urgently need more people to get involved in stopping it. Finding suitable organizations and

convincing them to participate is a great challenge.

Firstly, we constructed the client triangle prole model based on three dimensions (power,

resources, and interests), encompassed three categories of related clients: governments, enterprises,

and non-governmental organizations (NGOs). We established this model to get the scores of each

client with many indicators summarized for each dimension and then used the Entropy Weight

Method (EWM) to get the corresponding weights. Some indicators and the corresponding weights

are listed for samples as follows: country law (0.8967), industry inuence (0.2248), or media platform

promotion (0.4349) in the power dimension; GDP (0.8789), technological advancements (0.1283),

or volunteer network (0.6878) in the resources dimension; crime rate reduction and public security

(0.2540), optimizing reputation (0.1368), or biodiversity conservation (0.7460) in the interests dimen-

sion.

Secondly, we systematically evaluated the comprehensive scores of three dierent client categories

from bottom to top by utilizing the Analytic Hierarchy Process (AHP) respectively. Then we

calculated all the dierent potential client scores from 265 countries, 157 enterprises, and 30

organizations, and chose the client with the highest score. The famous luxury company, LVMH

group, with the normalized client triangle prole (0.44, 0.94, 0.24) achieves the most potential score

of 0.4157, and it is surprisingly an enterprise, neither a government nor a NGO.

Thirdly, we established a win-win model, innovatively introducing new revenue indicators such

as penalty revenues and eradication of transactions (replaceable materials). As in the case of

Apple Inc., discontinuing the use of leather products and adopting new materials has yielded more

gains. We assessed the benets that the project brings to our client through ane mapping analysis

and estimated the additional prot value ($128.06 million) in a year for LVMH. We believe this

project is ideally suitable for LVMH with its reputation for animal protection signicantly enhanced.

Additionally, we’ve devised a complementary vector model to evaluate the extra capabilities

needed by clients during project execution. The resulting complementary vector of our client LVMH is

(0.56, 0.06, 0.76), then we used the maximum matching model to identify the Seychelles govern-

ment with prole data (0.35, 0.03, 0.46) from our previously evaluated potential clients which have

the closest (99.9%) complementary vector based on cosine similarity. Our client LVMH may need

some resources such as an exclusive economic zone of nearly 1.4 million square kilometers. LVMH

can also benet from the Seychelles government’s support for wildlife protection policies, thereby

enhancing its brand image.

Finally, we established an impact model with the collected data on illegal wildlife trade from

2015 to 2022. We used multiple linear regression model based on the least squares method

to compute measurable impact from factors such as medical trade, experimental trade, food

trade, fur trade, and decoration trade, yielding coecients for the inuencing vectors of (2.651,

0.812, 0.164, -0.147, -1.811). We can decrease the future ve-year illegal wildlife trade value to $20

billion, assuming positive factors decrease by 10% annually and negative factors increase by 5%.

The model resulted in a nal predicted value of 17.54 billion, indicating a 33.81% decrease in trade

volume. We utilized a weighted probability model based on the ve-point estimation to assess

the likelihood of project success, resulting in a probability of 87.2%.

Furthermore, we put the wildlife trade within the larger ecological system and adapted the Leslie-

Gower model as a fundamental framework to elucidate the dynamics of prey-predator interactions,

the experiment results show the environmental protection factor will boost wildlife survival rates.

Moreover, when we slightly adjusted the initial trade values (±5%), the future illegal wildlife trade

value will change within 0.047%, thus it shows that our models have strong robustness.

Key Words: EWM ; AHP; Complementary Vector; Maximum Matching; Multiple Linear Regression

Contents

1 Introduction 3

1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1.2 Problem Restatement and Analysis . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 Data Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 The Client Triangle Prole Model 6

2.1 Weight Calculation using EWM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Typical Clients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3 Our choice of clients by AHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3 Win-Win Model 12

3.1 The Project Developed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 Estimated Prot of Adopting Our Project based on Ane Mapping . . . . . . . . . . 12

3.3 Other Clients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4 The Complement Vector Model 14

4.1 Maximum Matching of The Complement . . . . . . . . . . . . . . . . . . . . . . . . . 14

5 Impact Model 16

5.1 Basic Judgments based on scatter plots . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5.2 Construction of curve Fitting Model based on least square Method . . . . . . . . . . 17

5.3 Multivariate Linear Regression Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

5.4 Model Solving and Analysis of Impact Results . . . . . . . . . . . . . . . . . . . . . . 19

5.5 Probability Weighted Assessment Model . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.6 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

6 Wildlife Trade within Larger Ecological Model 22

6.1 The Leslie-Gower Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

6.2 Population Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

7 Strengths and Weaknesses 23

7.1 Strengths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

7.2 Weaknesses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

8 Conclusion and Future Works 23

8.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

8.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Team # 2413565 Page 3 of 25

1 Introduction

1.1 Overview

1.1.1 Background

Wildlife tracking, ranking as the world’s fourth-largest illegal trade, encompasses a diverse

range of trades, including fur trade, food trade, decorative item trade, Medical trade, experimen-

tal trade, and more. Like intricate root systems, it penetrates various dark corners of the world.

Estimated to involve as much as $26.5 billion annually, illegal wildlife trade has profound and far-

reaching impacts on biodiversity, the environment, and social stability, gradually drawing heightened

attention from the international community. Governments, enterprises, and organizations are taking

measures, wielding three swords, as depicted in Figure 1, to cut o the sources and pathways of illegal

wildlife trade from dierent angles.

Government

Organizations

Enterprises

Strengthen regulations with stricter penalties

Conduct public education campaigns

Establish reward systems

Conduct independent investigations

Initiate advocacy campaigns

Providing training and education for employees

Enhancing supply chain management

Adhering to all relevant regulations and laws

Food Trade

Medical Trade

Fur Trade

Experimental Trade

Decoration Trade

Trading meat from wildly

Selling protected animal components

Hunting wildlife to obtain fur product

Selling products containing animal fur

Trading organs of wildlife for traditional medical

Selling drugs containing protected animal or plant

Obtaining endangered wildlife for making decoration

Trading ivory used in crafting artifacts

Breeding animals for scientific experiments Illegally

Trade genetic material or laboratory supplies

Figure 1: Illegal wildlife trade behavior

In the current context, the urgency of this issue becomes more apparent, prompting the interna-

tional community to emphasize the imperative need for measures to combat wildlife crime through

a series of international agreements.

However, despite these agreements, substantive and systematic measures for eective inter-

vention still require further proposal and implementation. Confronting this global challenge, the

international community needs to collaborate synergistically, actively exploring and formulating in-

novative policy frameworks that not only robustly curb illegal wildlife tracking but also contribute

to maintaining the ecological balance of the planet and safeguarding precious natural resources.

When developing such policy frameworks, it is crucial for countries to work together to ensure

they are comprehensive, feasible, and innovative, capable of addressing this urgent issue comprehen-

sively and globally, guaranteeing the sustained protection of wildlife on a worldwide scale.

Team # 2413565 Page 5 of 25

1.3 Assumptions

▶ Assumption 1:

The mentioned factors including power, resources, and interests in this article

are all related to combating illegal wildlife trade.

◦ Justication: We focus on key powers and resources that can decrease the illegal wildlife trade:

government enforcement, international organization leadership, and sustainable development

eorts by businesses. The required resources include funding, technology, and expertise, with

a concentrated interest in supporting wildlife conservation and sustainable development goals.

▶ Assumption 2: The power, resources, and interest factors for government, enterprises, and

non-governmental organizations have dierent meanings.

◦ Justication: Each party has independent and distinct roles in the eorts to combat illegal

wildlife trade. This delineation is benecial for considering them as dierent types of clients,

enabling the more precise formulation of targeted projects and strategies.

▶ Assumption 3: New technologies and materials allow traditional industries that use animals

as raw materials to be upgraded.

◦ Justication: There are many famous examples, such as Apple Inc. discontinuing the use of

leather products and adopting new materials, which eventually led to more gains.

▶ Assumption 4: Our clients are rational and can be persuaded by potential gains or reputation.

◦ Justication: Our model assumes that enterprises have a certain level of operational feasibility

in their willingness to accept our project for decreasing the wildlife trade.

▶ Assumption 5: The amount of illegal trade is roughly proportional to the amount of related

industries and is relatively stable.

◦ Justication: The illegal wildlife trade can be viewed as a input cost of the related industry,

and its share is relatively stable and roughly proportional to the total industry dollar amount.

1.4 Data Pre-processing

Sucient data is the fundamental step in designing a project. To ensure the authenticity and

reliability of our project design to the greatest extent possible, we have extensively collected relevant

data from around the world, encompassing multiple countries, businesses, and organizations. We

particularly focused on countries where illegal wildlife trade is prevalent, such as in Africa, and

identied relevant businesses and organizations with a need for wildlife conservation projects.

Our data comes from a diverse range of sources, including various ocial websites and statisti-

cal studies, such as ocial reports on illegal wildlife trade, World Bank

[9]

data, and ocial national

databases of each country. Such diverse data sources ensure the quality and credibility of the foun-

dational data for our project, enabling us to more accurately assess and formulate eective strategies

to combat illegal wildlife trade.

What seems dicult is to assure the fully complete data in the procession of collecting. However,

the availability of the data is a crucial issue, therefore it is necessary for us to process the missing

data properly to enhance the accuracy and validity of our model. The methods of this procession

are shown as follows:

• Mean Complement: If the data before and after the missing value exists, the mean value of

the two will be used to substitute it.

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