本篇論文提出一個有系統的風險管理過程，其中包含風險分析與風險回應兩個部份。在風險分析的部份，利用累積願景理論（Cumulative Prospect Theory）描述個人的風險偏好，進而分析出風險事件帶給此人之風險程度（risk level），並依其影響大小，對應之風險區域即可確認。在風險回應的部份，每一風險區域有其對應之回應措施，包含接受（acceptance）、減輕（mitigation）、轉移（transfer）、避免（avoidance），利用提出的評估模式，考慮回應成本與回應風險後所產生的影響，以評估回應措施的適當與否，最後，選擇最適當的回應方法並執行之。
此外，由於現實生活中存在著許多的不確定資訊，因此，考慮模糊的風險事件影響(outcomes)，我們結合模糊理論（Fuzzy Sets Theory）的概念至風險管理中，以產生更精準的結果。
運用此風險管理過程至A-C官司問題中，相對於傳統決策樹之期望值方法（EMV），我們提供決策者更完整且切身的資訊以做決策參考。

In this study, we use Cumulative Prospect Theory to propose a risk management process including a risk analysis stage and a risk response stage. According to an individual’s preferential structure, individual’s risk level for the confronted risk can be identified from risk analysis. And based on a response evaluated model, the appropriate response strategy is assessed at the risk response stage. Based on the analyzed individual’s preference toward risk events, and the acquired level of severity for a risk event, the individual can be classified into dead, rational, sensitivity, or saturation of different risk zones. Then, the corresponding strategy, such as acceptance, mitigation, transfer, and avoidance can be decided. Finally, we apply the proposed evaluation model to validate the suggested strategy.
When the outcome is uncertain we also proposed a fuzzy risk management process to deal with the imprecise information and achieve a more accurate result. The applicability of the proposed model is evaluated by the A-C court case with both crisp and fuzzy outcomes. The results have shown that the propose method is able to provide more useful and pertinent information than the traditional expected monetary value method.

1. 清華大學作業研究教材編寫組, 作業研究, 儒林圖書公司, 1992
2. 張智星，MATLAB程式設計與應用，清蔚科技，2000
3. 林大舜, “建構電子化政府資訊推動計畫的風險管理制度之研究—以某大學電子公文計畫推動為例”, 碩士論文, 元智大學工業工程與管理研究所, 2002
4. 陳奕學, “空間資料叢集演算法之設計”, 碩士論文, 義守大學資訊工程研究所, 2002
5. Bleichrodt H., J. L. Pinto, “A Parameter-Free Elicitation of the Probability Weighting Function in Medical Decision Analysis”, Management Science, Vol 46, No 11, pp. 1485-1496, 2000
6. Bonvicini S. P. L., G. Spadoni, “Risk Analysis of Hazardous Materials Transportation: Evaluating Uncertainty by Means of Fuzzy Logic”, Journal of Hazardous Materials, Vol. 62, pp. 59-47, 1998
7. Bordley R. F., “Reformulating Decision Theory Using Fuzzy Set Theory and Shafer’s Theory of Evidence”, Fuzzy Sets and Systems, Vol. 139, pp. 243-266, 2003
8. Chateauneuf A., P. Wakker, “An Axiomatization of Cumulative Prospect Theory for Decision under Risk”, Journal of Risk and Uncertainty, Vol 18, No 2, pp.137-145, 1999
9. Clemen R. T., T. Reilly, Making Hard Decisions with Decision Tools, Duxbury, Belmont California, 2001
10. Fennema H., P. Wakker, “Original and Cumulative Prospect Theory: A Discussion of Empirical Differences”, Journal of Behavioral Decision Making, Vol 10, pp. 53-64, 1997
11. Fishburn P. C., The Foundations of Expected Utility, Reidel, Dordrecht Holland, 1982
12. Hillier F. S., G. J. Lieberman, Introduction to Operation Research, 7th Ed., McGraw-Hill, Dubuque Iowa, 2001
13. Hillson D., “Developing Effective Risk Responses”, Proceedings of 30th Annual Project Management Institute 1999 Seminar & Symposium Philadelphia, Pennsylvania, USA, Papers Presented October 10 to 16, 1999
14. Hojati M., C. R. Bector, K. Smimou, “A Simple Method for Computation of Fuzzy Linear Regression”, European Journal of Operational Research, Vol 166, pp.172-184, 2005
15. Kahneman D., A. Tversky, “Prospect Theory: An Analysis of Decision under Risk”, Econometrica, Vol. 47, No 2, pp. 263-292, 1979
16. Kaufman L., P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wily & Sons, New York, 1990
17. Lee E. S., Q. Zhu, Fuzzy and Evidence Reasoning, Physica-Verlag, Heidelberg, 1995
18. Lee H., H. Tanaka, “Upper and Lower Approximation Models in Interval Regression Using Regression Quantile Techniques”, European Journal of Operational Research, Vol. 116, pp. 653-666, 1999
19. Montgomery D. C., E. A. Peck, Introduction to Linear Regression Analysis, Wiley, New York, 1982
20. Montgomery D. C., Design and Analysis of Experiments, 5th Ed., Wiley, New York, 2001
21. Neilson W., J. Stowe, “A Further Examination of Cumulative Prospect Theory Parameterizations”, The Journal of Risk and Uncertainty, Vol 24, No 1, pp. 31-46, 2002
22. Ohashi T., M. Motomura, “Expert System of Cold Forging Defects Using Risk Analysis Tree Network with Fuzzy Language”, Journal of Materials Processing Technology, Vol. 107, pp. 260-266, 2000
23. Piney C., “ Applying Utility Theory To Risk Management”, Project Management Journal, Vol 34, No 3, pp.26-31, 2003
24. Prelec D., “The Probability Weighting Function”, Econometrica, Vol. 66, No 3, pp. 497-527, 1998
25. Tanaka H., S. Uejima, K. Asai, “Linear Regression Analysis with Fuzzy Model”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 12, pp. 903-907,1982
26. Theoddoridis S., K. Koutriumbas, Pattern Recognition, 2nd Ed., Acadmic Press, San Diego, 2003
27. Tummala V. M. Rao, Y. H. Leung, “A Risk Management Model to Assess Safety and Reliability Risks”, International Journal of Quality & Reliability Management, Vol 13, No 8, pp. 53-62, 1996
28. Tummala V M R., J. F Burchett, “Applying a Risk Management Process (RMP) to Manage Cost Risk for an EHV Transmission Line Project”, International Journal of Project Management, Vol 17, No 4, pp. 223-235, 1999
29. Tversky A., D. Kahneman, “Advanced in Prospect Theory: Cumulative Representation of Uncertainty”, Journal of Risk and Uncertainty, Vol 5, No.4, pp. 297-323, 1992
30. Yaniv G., “Tax Compliance and Advance Tax Payments: A Prospect Theory Analysis”, National Tax Journal, Vol 52, No 4, pp.753-765, 1999
31. Zadeh L. A., “Fuzzy Sets,” Information and Control, Vol 8, pp.338-353, 1965
32. Wang H. F., Lecture Notes of Fuzzy Sets of Theory, http://140.114.54.175/~hfwang/, 2004