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“平學（31）：精讀英文論文《有效貿易管理中安全供應鍊管理的博弈方法》第四章 實驗結果（2）”
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Today, the editor brings you an article
"Ping Xue (31)：Intensive reading of English paper ‘Game theory approach for secured supply chain management in effective trade management’ Chapter4 Results and experiments (2) "
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一、内容摘要（Summary of content）
今天小編将從思維導圖、精讀内容、知識補充三個闆塊為大家帶來英文期刊論文《Game theory approach for secured supply chain management in effective trade management》的實驗結果的第二部分。
Today, I will bring you the second part of the results and experiments of the English journal paper ‘Game theory approach for secured supply chain management in effective trade management’ from the three sections of Thinking Maps, Intensive Reading Content, and Knowledge Supplement.
二、思維導圖（Mind mapping）
三、精讀内容（Intensive reading content）
1. 均方誤差率（Mean squared error rate）
當通過均方誤差率比較所提出的GBSSCA模型與其他模型的性能時，所提出的GBSSCA模型的表現優于其他模型。其較低的均方誤差率（MSE）表明其預測值與實際值之間的平均平方差較小。這表明與基于貝葉斯、EGT-攻擊和防禦以及基于QRD的EGT模型相比，所提出的GBSSCA模型提供了更準确的預測，并且與資料的拟合度更好，具體的對比情況如下圖所示：
When comparing the performance of the proposed GBSSCA model with other models through the mean square error rate, the proposed GBSSCA model outperforms the other models. Its lower mean square error rate (MSE) indicates a smaller mean squared difference between its predicted and actual values. This indicates that the proposed GBSSCA model provides more accurate predictions and better fit to the data compared to Bayesian, EGT-attack and defense and QRD-based EGT models as shown in the following figure:
2. 所提的GBSSCA方法的效率（Efficiency of proposed GBSSCA）
下圖展示了所提出的GBSSCA模型在收入提升、防禦效果和穩定性分析方面的有效性：
The following figure demonstrates the effectiveness of the proposed GBSSCA model in terms of revenue enhancement, defense effectiveness and stability analysis:
(1) 收入提升：圖中顯示了所提出的架構與基準或現有方法相比所取得的收入提升。标有“基準收入”和“所提收入”的線條分别表示從傳統供應鍊模型或博弈論方法以及所提出的GBSSCA獲得的收入值。如果“所提收入”線始終高于“基準收入”線，表明所提出的GBSSCA導緻更高的收入生成，表明其在改善供應鍊系統的财務結果方面的有效性。
(1) Revenue enhancement: The figure shows the revenue enhancement achieved by the proposed architecture compared to the benchmark or existing methods. The lines labeled “Benchmark Revenue” and “Proposed Revenue” indicate the value of revenue gained from the traditional supply chain model or game theory approach and the proposed GBSSCA, respectively. If the “proposed revenue” line is consistently higher than the “baseline revenue” line, it indicates that the proposed GBSSCA leads to higher revenue generation, demonstrating its effectiveness in improving the financial outcomes of the supply chain system.
(2) 防禦政策的有效性：圖中還展示了由強化學習模型（RLM）和QRD計算選擇的防禦政策的有效性。标有“攻擊收入減少”和“防禦收入增加”的線條分别表示通過在所提出的GBSSCA中采用的防禦政策實作的攻擊收入減少和防禦收入增加。如果這些線條始終顯示攻擊收入的下降趨勢和防禦收入的上升趨勢，表明所提出的GBSSCA有效地減少了攻擊造成的财務損失，并增強了供應鍊系統的防禦能力。
(2) Effectiveness of defense strategies: The figure also shows the effectiveness of the defense strategies selected by the Reinforcement Learning Model (RLM) and QRD calculations. The lines labeled “Attack Revenue Decrease” and “Defense Revenue Increase” indicate the decrease in attack revenue and increase in defense revenue, respectively, achieved by the defense strategy adopted in the proposed GBSSCA. If these lines consistently show a decreasing trend of attack revenue and an increasing trend of defense revenue, it indicates that the proposed GBSSCA effectively reduces the financial losses caused by attacks and enhances the defense capability of the supply chain system.
(3) 系統穩定性：圖中通過标有“穩定階段”和“不穩定階段”的線條展示了系統的穩定性。這些線條分别表示政策達到穩定狀态（納什均衡）的階段數和政策仍不穩定且經曆顯著變化的階段數。如果“穩定階段”線始終顯示較高的值，而“不穩定階段”線接近于零，表明所提出的GBSSCA在維持穩定和均衡狀态方面有效，最小化破壞性變化，并在整個供應鍊系統中促進一緻的性能。
(3) System stability: The stability of the system is illustrated by the lines labeled “stable phase” and “unstable phase”. These lines indicate the number of phases in which the strategy reaches a steady state (Nash equilibrium) and the number of phases in which the strategy remains unstable and undergoes significant changes. If the “Stable Stage” line consistently shows high values and the “Unstable Stage” line is close to zero, it indicates that the proposed GBSSCA is effective in maintaining the stable and equilibrium states, minimizing disruptive changes, and promoting consistent performance throughout the supply chain system. performance throughout the supply chain system.
四、知識補充（Knowledge supplementation）
文章當中提到了均方誤差，那麼什麼是均方誤差？它的計算公式是什麼？
The article mentions the mean square error, so what is it? What is the formula for calculating it?
均方誤差（Mean Squared Error, MSE）是一種常用的統計度量，用來評估預測值與實際觀測值之間差異的程度。在機器學習和統計模組化中，MSE 被用作回歸模型的損失函數，來量化模型預測的準确性。MSE 的計算公式如下：
Mean Squared Error (MSE) is a commonly used statistical measure to assess the extent to which predicted values differ from actual observations. In machine learning and statistical modeling, MSE is used as a loss function in regression models to quantify the accuracy of the model's predictions.The formula for MSE is as follows:
MSE 的值越小，表示模型的預測值與實際值之間的差距越小，模型的預測性能越好。
The smaller the value of MSE, the smaller the gap between the predicted and actual values of the model, and the better the predictive performance of the model.
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參考資料：Google翻譯，百度
參考文獻：Chu Wei, Shi Yanzhao, Jiang Xue, et al. Game theory approach for secured supply chain management in effective trade management [J]. Annals of Operations Research, 2024, 41(1): 1-19.
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