The illegal wildlife trade poses a serious threat to wildlife and ecosystems. Therefore, we proposed a five-year project. This project aims to reduce illegal wildlife trade by focusing on awareness, stopping poaching and disrupting trade routes. Specifically, we created the Stop Illegal Wildlife Trade (SIWT) Model to measure the amount of illegal wildlife trade.
非法野生动物贸易对野生动物和生态系统构成严重威胁。因此，我们提出了一个为期五年的项目。该项目旨在通过提高意识、制止偷猎和扰乱贸易路线来减少非法野生动物贸易。具体来说，我们创建了停止非法野生动物贸易 （SIWT） 模型来衡量非法野生动物贸易的数量。

Firstly, we created a Customer Assessment (CA) model to evaluating potential clients. AHP and EWM and Topsis was used in this model. The World Wide Fund for Nature (WWF) had a composite score of $\mathbf{0}\mathbf{.}\mathbf{7}\mathbf{8}$$\mathbf{0}\mathbf{.}\mathbf{7}\mathbf{8}$0.78\mathbf{0 . 7 8}, representing it as the most suitable client. We explained the fit of the project with WWF through SRI theory and relevant references. The data shows a $\mathbf{5}\mathbf{4}\mathbf{\%}$$\mathbf{5}\mathbf{4}\mathbf{\%}$54%\mathbf{5 4 \%} decrease in the planet’s Life Vitality Index (LPI), which indicates a serious decline in the planet’s biodiversity. We projected that these outcomes would occur upon the successful implementation of the project. The Illegal wildlife trade decline and wildlife populations increase.
首先，我们创建了一个客户评估 （CA） 模型来评估潜在客户。该模型使用了 AHP 和 EWM 以及 Topsis。世界自然基金会 （WWF） 的综合评分为 $\mathbf{0}\mathbf{.}\mathbf{7}\mathbf{8}$$\mathbf{0}\mathbf{.}\mathbf{7}\mathbf{8}$0.78\mathbf{0 . 7 8} ，代表其为最合适的客户。我们通过 SRI 理论和相关参考资料解释了该项目与 WWF 的契合度。数据显示地球的生命活力指数 （LPI） $\mathbf{5}\mathbf{4}\mathbf{\%}$$\mathbf{5}\mathbf{4}\mathbf{\%}$54%\mathbf{5 4 \%} 下降，这表明地球的生物多样性严重下降。我们预计，这些成果将在项目成功实施后发生。非法野生动物贸易减少，野生动物数量增加。

Secondly, we developed the SIWT model, which consists of four sub-models:
其次，我们开发了 SIWT 模型，它由四个子模型组成：
Otential Buyer - Purchaser - Non-purchaser (OPN) model, Stop Poaching Wildlife (SWP) model, Forecasting Illegal Wildlife Trade (FIWT) model, Foreeast Widlife Populations (FWP) model .The OPN model is based on the SIRS model and simulates changes in the number of buyers. SWP model uses the modified Lotka-Volterra equation to simulates the amount of wildlife poached. The FIWT model uses difference equations to predict the total amount of illegally traded wildlife per month. FWP model was used to assess wildlife populations. For each mini-model, we propose specific strategies to facilitate project implementation.
潜在买方 - 买方 - 非买方 （OPN） 模型、停止偷猎野生动物 （SWP） 模型、预测非法野生动物贸易 （FIWT） 模型、Foreeast Widlife 种群 （FWP） 模型。OPN 模型基于 SIRS 模型，模拟买家数量的变化。SWP 模型使用修正的 Lotka-Volterra 方程来模拟野生动物偷猎的数量。FIWT 模型使用差分方程来预测每月非法交易的野生动物总量。FWP 模型用于评估野生动物种群。对于每个微型模型，我们都会提出促进项目实施的具体策略。

Thirdly, based on the strategies in the SIWT model, we found that the project would require significant funding and collaboration among organizations. We believe that more resources and authority are needed. This includes more funding inputs and implementation authority, among other things.
第三，根据 SIWT 模型中的策略，我们发现该项目需要大量资金和组织之间的合作。我们认为需要更多的资源和权威。这包括更多的资金投入和实施授权等。

In addition, we emulated the SIWT model with elephants and rhinos as the main trading animals and countries such as India as the main players. We used the existing elephant and rhino populations, the number of poached wildlife, and the total amount of illegal trade at the end of January 2023 as initial values for the SIWT model. We adjusted the $\mathbf{1}\mathbf{3}$$\mathbf{1}\mathbf{3}$13\mathbf{1 3} parameters in SIWT model by fitting them to historical data in the end, we found that after the implementation of the project, the number of illegally traded ivory and rhino horns decreased by $\mathbf{5}\mathbf{2}\mathbf{.}\mathbf{4}\mathrm{\%}$$\mathbf{5}\mathbf{2}\mathbf{.}\mathbf{4}\mathrm{\%}$52.4%\mathbf{5 2 . 4} \% and their populations increased by $\mathbf{9}\mathbf{.}\mathbf{3}\mathrm{\%}$$\mathbf{9}\mathbf{.}\mathbf{3}\mathrm{\%}$9.3%\mathbf{9 . 3} \%. The project achieved the desired results. However, We need to accommodate the factor of instability. Then we emulated the SIWT model with high negative impacts. We found that the number of illegally tradable elephants and rhinoceros decreased by $\mathbf{4}\mathbf{9}\mathbf{.}\mathbf{6}\mathbf{\%}$$\mathbf{4}\mathbf{9}\mathbf{.}\mathbf{6}\mathbf{\%}$49.6%\mathbf{4 9 . 6 \%} and the population increased by $\mathbf{6}\mathbf{.}\mathbf{9}\mathbf{\%}$$\mathbf{6}\mathbf{.}\mathbf{9}\mathbf{\%}$6.9%\mathbf{6 . 9 \%}. In order to achieve the desired goals, we need to address factors of uncertainty.
此外，我们模拟了 SIWT 模型，以大象和犀牛为主要贸易动物，印度等国家为主要参与者。我们使用现有的大象和犀牛种群、偷猎野生动物的数量以及 2023 年 1 月底的非法贸易总额作为 SIWT 模型的初始值。我们通过对 SIWT 模型中的 $\mathbf{1}\mathbf{3}$$\mathbf{1}\mathbf{3}$13\mathbf{1 3} 参数进行拟合来调整参数，最终发现，在项目实施后，非法交易的象牙和犀牛角数量减少了 $\mathbf{5}\mathbf{2}\mathbf{.}\mathbf{4}\mathrm{\%}$$\mathbf{5}\mathbf{2}\mathbf{.}\mathbf{4}\mathrm{\%}$52.4%\mathbf{5 2 . 4} \% ，其数量增加了。 $\mathbf{9}\mathbf{.}\mathbf{3}\mathrm{\%}$$\mathbf{9}\mathbf{.}\mathbf{3}\mathrm{\%}$9.3%\mathbf{9 . 3} \% 该项目达到了预期的结果。然而，我们需要适应不稳定因素。然后，我们模拟了具有高负面影响的 SIWT 模型。我们发现，非法交易的大象和犀牛数量减少了 $\mathbf{4}\mathbf{9}\mathbf{.}\mathbf{6}\mathbf{\%}$$\mathbf{4}\mathbf{9}\mathbf{.}\mathbf{6}\mathbf{\%}$49.6%\mathbf{4 9 . 6 \%} ，数量增加了 $\mathbf{6}\mathbf{.}\mathbf{9}\mathbf{\%}$$\mathbf{6}\mathbf{.}\mathbf{9}\mathbf{\%}$6.9%\mathbf{6 . 9 \%} 。为了实现预期目标，我们需要解决不确定性因素。

Finally, we conducted sensitivity analyses on the OPN and SWP models and found that they are stable and robust. At the end, we wrote a memo to WWF communicating the key points of the project in conjunction with the results of the project analysis.
最后，我们对 OPN 和 SWP 模型进行了敏感性分析，发现它们稳定且稳健。最后，我们向 WWF 写了一份备忘录，将项目的关键点与项目分析结果相结合。

Keywords: Illegal wildlife trade, AHP-EWM, SIWT model, Lotka-Volterra equation
关键词：非法野生动物贸易 ， AHP-EWM， SIWT模型 ， Lotka-Volterra方程

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1．Introduction … 3  1.简介 ...3
1．1 Problem Background … 3
1.1 问题背景 ...3
1．2 Restatement of the Problem … 3
1.2 重述问题 ...3
1．3 Our Work … ．． 3
1.3 我们的工作 ... .. 3
2．Assumptions and Justifications … 4
2.假设和理由......4
3．Notations … 4  3.符号 ...4
4．Client Assessment（CA）Model … 5
4.客户评估（CA）模型5
4．1 Construction of the Client System … 5
4.1 客户端系统建设5
4．2 Selection of evaluation indicators … 5
4.2 评价指标的选择5
4．3 Game Theory Combinatorial Empowerment … 6
4.3 博弈论组合授权 ...6
4．4 Empowerment TOPSIS … 7
4.4 赋权 TOPSIS ...7
4．5 Evaluation Results … 8
4.5 评估结果 ...8
5．Why is WWF the Ideal Client？ … 8
5.为什么 WWF 是理想的客户？8
5．1 Theoretical Foundation … 8
5.1 理论基础8
5．2 Literature Support … 9
5.2 文献支持 ...9
5．3 Data Analysis … 9
5.3 数据分析 ...9
6．Stop Illegal Wildlife Trade（SIWT）Mode … 10
6.制止非法野生动植物贸易 （SIWT） 模式10
6．1 Otential Buyer－Purchaser－Non Purchaser（OPN）Model … 11
6.1 潜在买方-买方-非买方（OPN）模型...11
6．2 Stop Poaching Wildlife（SPW）Moder． … 12
6.2 停止偷猎野生动物 （SPW）Moder. ...12
6．3 Forecasting Illegal Wridlife Trade（FIWT）Model … 14
6.3 预测非法 Wridlife Trade（FIWT） 模型14
6．4 Forecast Wildlife Populations（FWP）Model … 15
6.4 野生动物种群预报 （FWP） 模型 ...15
7．What additional resources and rights they need？ … 16
7.他们需要哪些额外的资源和权利？16
7．1 Additional Funding … 16
7.1 额外资金 ...16
7．2 Additional Powers … 17
7.2 其他权力 ...17
8．SIWT Model Application and Situation Expectations … 17
8.SIWT 模型应用及情况预期17
8．1 Fitting and Simulation of OPN and SPW Model Parameters … 18
8.1 OPN 和 SPW 模型参数的拟合和仿真 ...18
8．2 Fitting and Simulation of FIWT and FWP Model Parameters … 19
8.2 FIWT 和 FWP 模型参数的拟合和仿真 ...19
8．3 Likelihood that the Project will Meet its Objectives … 20
8.3 项目实现其目标的可能性 ...20
8．4 Uncertainty … 21  8.4 不确定性21
9．Sensitivity Analysis … 21
9.灵敏度分析...21
10．Model Strengths and Weaknesses … 22
10.模型的优势和劣势 ...22
10．1 Strengths … 22  10.1 优势 ...22
10．2 Weaknesses … 22  10.2 弱点 ...22
11．Conclusion … 22  11.结论 ...22
12．References … 23  12.参考资料 ...23

The illegal wildlife trade not only causes enormous damage to the environment, but also poses a serious threat to global biodiversity. The trade is estimated to involve as much as $\mathrm{\$}26.5$$\mathrm{\$}26.5$$26.5\$ 26.5 billion annually, making it the fourth largest illegal trade globally. However, global efforts to address the problem still face significant challenges.
非法野生动植物贸易不仅对环境造成巨大破坏，而且对全球生物多样性构成严重威胁。据估计，该贸易每年涉及多达 $\mathrm{\$}26.5$$\mathrm{\$}26.5$$26.5\$ 26.5 10 亿美元，使其成为全球第四大非法贸易。然而，解决这一问题的全球努力仍然面临重大挑战。

We are therefore proposing a data-driven, five-year program aimed at significantly reducing the illegal wildlife trade through accurate client and program selection.
因此，我们提出了一个数据驱动的五年计划，旨在通过准确的客户和计划选择来显著减少非法野生动物贸易。

Figure 1Wildlife scene (left) and illegal trade in wildlife (right)
图 1野生动物场景（左）和野生动物非法贸易（右）

Considering the background, we need to solve the following problems:
考虑到背景，我们需要解决以下问题：

In order to avoid complicated descriptions, intuitively reflect our work process, the flow chart is shown in Figure 2.
为了避免复杂的描述，直观地反映我们的工作流程，流程图如图 2 所示。

We built two models, the first model used the EWM-AHP approach to calculate weights and the tosopsis composite scores for indicators such as powers to determine the ideal client WWF.
我们构建了两个模型，第一个模型使用 EWM-AHP 方法计算权重和 tosopsis 综合分数，用于确定理想客户 WWF 的功效等指标。

The second model, SIWT, consists of four sub-models that are used to construct a fiveyear project and propose policies in four areas such as reducing poaching behaviour. Based on the SIWT model, firstly, we reviewed the literature and based on data-driven analysis of the critical situation of wildlife and project support as a way to convince WWF to undertake this project. Secondly, we calculate and determine the additional power and resources needed by WWF based on assumptions and reality. Finally, we measure the risk that instability poses to the project by simulating and analysing the four sub-models. We will achieve our goal of reducing illegal wildlife trade through dynamic enhancements.
第二个模型 SIWT 由四个子模型组成，用于构建一个五年项目，并在四个领域提出政策，例如减少偷猎行为。首先，基于 SIWT 模型，我们回顾了文献，并基于对野生动物危急情况和项目支持的数据驱动分析，以此来说服 WWF 开展该项目。其次，我们根据假设和现实计算并确定 WWF 所需的额外电力和资源。最后，我们通过模拟和分析四个子模型来衡量不稳定因素对项目构成的风险。我们将通过动态增强来实现减少非法野生动物贸易的目标。