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群體信息集結(jié)過(guò)程中無(wú)量綱化方法的選擇

發(fā)布時(shí)間:2018-05-01 15:13

  本文選題:綜合評(píng)價(jià) + 群體評(píng)價(jià) ; 參考:《運(yùn)籌與管理》2017年05期


【摘要】:針對(duì)線性無(wú)量綱化方法對(duì)群體評(píng)價(jià)中信息集結(jié)結(jié)果的影響問(wèn)題,本文以線性加權(quán)的群體信息集結(jié)方法為背景,以集結(jié)成的群體信息最大程度地?cái)U(kuò)大被評(píng)價(jià)對(duì)象間的差異為導(dǎo)向,給出了群體信息集結(jié)過(guò)程中無(wú)量綱化方法選擇的若干結(jié)論和建議。首先設(shè)定評(píng)價(jià)情景并提出研究假設(shè),分析不同無(wú)量綱化方法集結(jié)成的群體信息對(duì)各被評(píng)價(jià)對(duì)象間的差異影響;然后對(duì)造成被評(píng)價(jià)對(duì)象之間差異的主要因素進(jìn)行了討論;通過(guò)對(duì)不同因素的分析以及與未經(jīng)過(guò)無(wú)量綱化處理集結(jié)成的群體信息中各被評(píng)價(jià)對(duì)象間差異的比較,得出一些重要的結(jié)論,并給出一些針對(duì)群體信息結(jié)集過(guò)程中選擇無(wú)量綱化方法的建議;最后,用一個(gè)算例檢驗(yàn)了結(jié)論的有效性。
[Abstract]:Aiming at the influence of linear dimensionless method on the result of information aggregation in group evaluation, this paper takes the linear weighted group information assembly method as the background, and takes the assembled group information as the direction to maximize the difference between the evaluated objects. Some conclusions and suggestions on the selection of dimensionless methods in the process of mass information gathering are given. Firstly, the evaluation scenarios are set up and the research hypotheses are put forward to analyze the influence of the group information gathered by different dimensionless methods on the differences among the evaluated objects, and then the main factors that cause the differences among the evaluated objects are discussed. Based on the analysis of different factors and the comparison of the differences between the groups of information collected without dimensionalization, some important conclusions are drawn. Some suggestions for selecting dimensionless method in the process of group information gathering are given. Finally, an example is given to verify the validity of the conclusion.
【作者單位】: 東北大學(xué)工商管理學(xué)院;東北評(píng)價(jià)中心;
【基金】:國(guó)家自然科學(xué)基金資助項(xiàng)目(71671031)
【分類(lèi)號(hào)】:C934

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相關(guān)期刊論文 前1條

1 張笑;馮廷勇;;決策信心在信息化從眾中的作用[J];心理科學(xué);2014年03期

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本文編號(hào):1829905

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