群組決策系統中－以多梯次密度叢集技術進行共識趨勢分析之研究

Employing Multistage Clustering Techniques to Analyze the Consensus Trend in Group Decision System

廖文豪(Wen-Hao Liao)

《資訊管理學報》 12卷1期 (2005/01) Pp. 29-56

https://doi.org/10.6382/JIM.200501.0029

摘要

本研究提出利用多梯次密度叢集技術建構線上共識趨勢分析系統，並藉以改善線上公民決策模式下所面臨之低共識程度問題，該系統可於每一決策梯次結束後，產生以前梯次之趨勢資訊，如各主流群體之分布圖、各偏好方案支持度排行、樞紐分析圖表及共識程度指標等，使決策者在充分掌握共識趨勢資訊下，並可藉此逐梯次調整個人之偏好以求得全體高共識高滿意度之決策方案。為驗證共識趨勢分析系統對共識程度提昇之效益，本研究以旅遊決策範例進行線上公民決策實驗並以因徑分析法探討其因果關係，結果顯示共識程度指標如一致性程度、期望落差及決策滿意度皆隨者梯次增加而產生直接而顯著之變化，也就是說，多梯次共識趨勢分析系統對共識程度之提昇確有顯著影響。

關鍵字

密度叢集法 ； 共識趨勢分析 ； 決策滿意度 ； 決策一致性程度

並列摘要

In order to improve consensus degree in the models of civil decision-making, this study proposes to employ multistage clustering techniques to construct a consensus-trend analytical system. This system can create consensus trend information that is needed in the consensus reaching process after each decision stage. After perceiving consensus trend of major groups, decision makers may adjust their preferences to reach a conclusion of high-consensus and high-satisfaction stage bcy stage. For verifying the benefits of the system, this study implements an on-line deision making experiment and then explores the causal relationship by path analysis. The results indicate that the consensus indices, such as coincidence degree, expectation gap and satisfaction degree, bring about significant and direct variations while we increasing the number of decision stage. This confirms that the consensus-trend analytical system is helpful for improving the problem of low consensus.

並列關鍵字

density-based clustering techniques ； consensus trend analysis ； satisfaction degree ； coincidence degree

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