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題名：考慮屬性門檻與變異資料之多屬性效用方案評選模式
作者：周宏彥
作者(外文)：Hung-Yen Chou
校院名稱：國立成功大學
系所名稱：交通管理學系碩博士班
指導教授：段良雄
學位類別：博士
出版日期：2003
主題關鍵詞：多屬性效用；方案評選；變異資料；屬性門檻；multi-attribute utility；project evaluation；variability data；attribute thresholds
原始連結：連回原系統網址new window
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本研究利用敘述偏好數據建立多屬性效用模式進行運輸替選方案之評選，在模式的構建過程同時探討屬性的可接受門檻與無異門檻問題。研究結果發現利用線性羅機模式所建立之多屬性效用函數解釋能力尚可，大部分屬性均相當顯著。此結果表示利用敘述偏好數據建立多屬性效用模式確實可找出各屬性間的交互損益關係。
在考慮同一決策者之決策具一致性，而不同決策者間具異質性之假設下，所建立的可接受門檻或無異門檻模式均可大幅增加模式的解釋能力，由於大部分的屬性都有適用於部分決策者的可接受門檻或無異門檻，故對各屬性間的交互損益關係有相當大的影響。無異門檻模式的解釋能力優於可接受門檻模式。同時考慮兩種門檻的整合模式的解釋能力最佳，所有係數均相當顯著且符號正確，各屬性係數值間之交互損益關係也相當合理。
本研究對於具有變異資料之屬性以期望效用函數方式建立模式。當以全部樣本數建立模式時，發現考慮有變異資料之非線性函數型態並沒有較中間值線性函數佳。
依資料變異特性將樣本數分成不同群組，分別建立平均數及標準差指數模式與機率分配模式，實證發現分群後所建立之多屬性效用模式之解釋度較好。最後本研究以機率分配模式為基礎，考慮不同屬性間之資料變異特性，建立分群組後之多屬性效用方案評估模式，解釋能力非常好。但實際之決策偏好行為而言，決策者是否能依各個屬性之變異資料特性區分出風險偏好，需進一步探討。
本研究發現屬性門檻與不確定性資料均會影響決策之偏好選擇，對於兩者之處理方式本研究係採分開處理。未來可考慮兩者模式整併處理，以增加模式之解釋能力與應用價值。也就是屬性變異資料與屬性門檻整合模式之構建與處理問題，值得後續之研究。
This research established multi-attribute utility models with stated preference data to evaluate transportation projects. We discussed the attribute thresholds of acceptance and indifference. The results showed that the multi-attribute utility function of linear logit model had relatively good explanatory ability and most attributes were significant. This means that multi-attribute utility model established with stated preference data can indeed find the tradeoff between attributes.
Assuming that an individual should behave consistently and different individuals may behave heterogeneously, we found that the threshold models could greatly increase explanatory power. Due to the fact that most attributes had threshold of acceptance and/or indifference, the tradeoff between attributes were greatly affected. Of the two threshold models, the threshold of indifference model had better explanatory power. The integrated model with both thresholds had best explanatory power. Its coefficients of all attributes were significant and had the correct signs. The tradeoffs between attributes were reasonable.
The research also consider the variability data to estabilished the expected utility model. When using all the observation data to construct the utility models, we find several nonlinear models with variability data are not better than the linear model.
Assumption the observation samples can divide from several group using the senna theory and probability distribution model. The two multi-attribute utility models which be classified to several groups can increase the explanatory power. Finally, we take the probability distribution model as a foudation model that consider different attribute variability characteristics. The results show the model explanatory power is very well, but the decision-maker who can consider with several different attribute variability problem or not is the question for the further study.
The empirical results show each threshold or variability factor really influenced the decision preference choice. The research analyzed separately the two factor models. In the further, we can develop the integration model to increase the model explatory power and its values. In other words, developing the integration multi-attribute utility project evaluation model with both attribute threshold and variability problem is the direction for the next research.
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