為取得最令人滿意之決策結果，決策者的風險態度而不僅僅是待選項目的效用價值應被納入考量中。本研究使用Ｓ形曲線價值函數來取代傳統多屬性決策方法中的期望效用函數以反映決策者的風險趨避和風險追求行為。在此基礎上，為了進一步減輕使用者在標量參考點上遇到的困難，本研究使用價值函數和權重加總方法來定義每個待選項目相對於極端可行解的心理價值距離以衡量它們的總體展望價值。該方法的效能在比較分析和敏感度分析中得到了驗證，證明其能夠幫助減少多屬性決策中的常見的問題例如排序顛倒問題．之後，該方法被擴展到群體決策領域，將多位決策者的偏好加總後得出公正的解決方案。實驗證明了該方法是適當且穩定的。最後，為了處理現實社會中存在的動態多階段決策場景．本研究將此方法發展為動態多階段決策方法並應用在一個挑選海量資料服務建構商的實際標案過程中，前一輪決策結果被以回饋機制帶入到下一輪的決策過程中。使用者接受了最終結果並認為該決策過程是易用且有幫助的。

To achieve the most satisfying decision results, not only the utility value of the alternatives but also the risk attitudes of the decision makers need to be considered. In this proposed model, the s-shape value function is adopted to replace the expected utility function that is often used in traditional MADM methods to reflect the risk-averse and risk-seeking behavior of decision makers. On top of that, to further reduce the user burden of identifying the reference points, the psychological value distance is defined to measure the overall prospect values of each alternative reference to extreme feasible solutions using the value function and the additive weighting method. Performance of the proposed algorithms is comparatively analyzed and sensitivity analysis is conducted, to prove that this mechanism can help reduce issues like rank reversal. After that, the method is extended to a group decision setting, and the preferences of multiple decision makers are aggregated to produce a fair result. The experiments show that it is an appropriate and robust MADM method. Finally, considering the real world dynamic decision making scenario, the model is further developed to be dynamic (can handle more than one rounds of decision making, as defined in another research of a dynamic multiple-criteria decision making framework) (Campanella and Ribeiro, 2011), and then was applied in a big data service provider selection bidding case and the results from previous decision making process were carried to the following round using a feedback mechanism. The users accepted the final results and were satisfied with the easy and helpful decision making process.

CONTENTS
Chapter 1 Introduction	1
1.1 Research motivation	1
1.2 Research objectives	4
1.3 Research methodology	5
1.4 Research limitations	8
1.5 Dissertation structure	9
Chapter 2 Literature review	10
2.1 MADM	10
2.2 The Prospect Theory	13
2.3 The usage of the Prospect Theory in MADM	16
2.4 Dynamic decision process studies	19
Chapter 3 The proposed approach for static scenario	22
3.1 Election based on relative value distances	22
3.2 Election based on value distances	34
3.3 EBVD used in group decision scenario	38
Chapter 4 Evaluations and experiments	46
4.1 Evaluations for ERVD	46
4.2 Evaluations for EBVD	59
4.3 Comparison of ERVD and EBVD	70
Chapter 5 The proposed approach in dynamic scenarios	74
5.1 The framework of the dynamic EBVD	74
5.2 Detailed steps of the dynamic EBVD	77
Chapter 6 Illustrative example for dynamic scenario	83
6.1 Project background	83
6.2 Assessment procedures	86
6.3 The second round of the bidding process	89
Chapter 7 Conclusion and discussions	92
7.1 Advantages of the proposed methods	92
7.2 Scenarios to apply the proposed methods	95
7.3 Management implications and future studies	96
I.	Appendix I: call center support system evaluation form – first round	98
II.	Appendix II: call center support system evaluation form – second round	99
References	100

 
LIST OF TABLES

Table 2 1 Recent MADM methods based on prospect theory	19
Table 2 2 Dynamic MADM methods	21
Table 3 1 Decision matrix of human resource selection (Opricovic, 1998)	29
Table 3 2 Weights on criteria (Opricovic, 1998)	30
Table 3 3 Normalized decision matrix	31
Table 3 4 Normalized value based decision matrix	32
Table 3 5 The relative closeness and rank by ERVD	33
Table 3 6 Decision matrix defined by decision maker 1 (Shih et al., 2007)	42
Table 3 7 Separation measures	44
Table 3 8 The aggregated relative closeness and rank by group EBVD	45
Table 4 1 The decision matrix and results obtained by TOPSIS and ERVD of Experiment 1	48
Table 4 2 The decision matrix and results obtained by TOPSIS and ERVD of Experiment 2	50
Table 4 3 Results obtained by ERVD with parameter α and β varied	52
Table 4 4 Results obtained by ERVD with parameter λ varied	56
Table 4 5  Results obtained by ERVD with reference point of C2 varied	58
Table 4 6 The decision matrix results obtained by TOPSIS and EBVD of Example 1	60
Table 4 7 The final values and rank obtained by TODIM	62
Table 4 8 Decision matrix of Example 2 (García-Cascales and Lamata, 2012)	63
Table 4 9 Results obtained by TOPSIS and EBVD of Example 2	63
Table 4 10 Results obtained by TOPSIS and EBVD of  Example 2 with the incorporation of a new alternative	64
Table 4 11 Results obtained by EBVD with parameter α = β varied	66
Table 4 12 Results obtained by EBVD with attenuation factor of the losses varied	70
Table 4 13 EBVD and ERVD comparison using 4.1 experiment 1	71
Table 4 14 Subsection 4.2 Example 2 original scenario	72
Table 4 15 Subsection 4.2 Example 2 add new alternative	72
Table 6 1 The sample call-in phone call counts for short (<10 seconds) and longer (10-50 seconds) calls	84
Table 6 2 Sample report	85
Table 6 3 Assessment criteria for the first round of decision-making	87
Table 6 4 The parameters for the first round decision	88
Table 6 5 The decision matrix for the first round	89
Table 6 6 Ranking results for the first round	89
Table 6 7 Parameters used in the second round of decision-making	91
Table 6 8 Decision matrix for the second round	91
Table 6 9 Results for the second round	91


 
LIST OF FIGURES
Figure 2 1 Value Function (Kahneman and Tversky, 1979)	14
Figure 4 1 Experiment on the range of α = β	51
Figure 4 2 Experiment on the range of α	53
Figure 4 3 Experiment on the range of β	54
Figure 4 4 Experiment on the range of λ (0.25 -  7)	55
Figure 4 5 Experiment on the range of λ (2.0 -  2.55)	57
Figure 4 6 The values according to various reference points.	59
Figure 4 7 Experiment on the range of α = β	65
Figure 4 8 Experiment on the range of α	67
Figure 4 9 Experiment on the range of β	68
Figure 4 10 Experiment on the range of λ (0.25 -  7)	69
Figure 5 1 The framework for a Dynamic EBVD	76
Figure 7 1 Different scenario to apply the corresponding proposed methods	95
Figure I 1 Call center support system evaluation form round 1	98
Figure II 1 Call center support system evaluation form round 2	99

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