【摘要】：一直以來,學(xué)者們認(rèn)為人類在社會及經(jīng)濟活動中的行為發(fā)生時間間隔,可以簡單地用泊松分布來描述。但自2005年以來,Albert Barabási等人進(jìn)行大量的實證研究后發(fā)現(xiàn):人類行為發(fā)生時間并不全部符合均勻的泊松分布,而大量是服從有胖尾的冪律分布,并提出了人類行為的兩個普適類的冪指數(shù)標(biāo)度。解釋這方面的行為理論主要有優(yōu)先排隊論、記憶、習(xí)慣、自適應(yīng)性模型等,但這些模型都只是人類行為的某一方面特性。 本文通過對維基百科進(jìn)行了較全面的實證分析,從而得出維基人類行為的基本特點,然后提出一個綜合的模型,進(jìn)而全面的檢驗實證的結(jié)果,并從理論上分析這種行為的深層原因。本文得到了以下的主要研究結(jié)論: 維基百科中所有用戶的登錄次數(shù)、部分用戶的登錄次數(shù)和登錄時間間隔都有近似的冪律分布擬合。實證所得到的冪指數(shù)主要可以劃分為三個(由小大到分別是:1、1.5、2),其中兩個冪指數(shù)(1和1.5)和Vázquez等提出的兩個普適類是相一致的。但僅僅把人類行為特征簡單地分成兩個普適類冪指數(shù)是不夠完整的,至少還應(yīng)該存在著第三個冪指數(shù)(指數(shù)大小為2)。 人類行為綜合模型仿真出來的結(jié)果可基本劃分為四個冪指數(shù),由小到大分別是0.6、1、1.5、2。后三個冪指數(shù),與維基百科的實證所得結(jié)果是相一致的,但模型還得出了第四個冪指數(shù)——0.6。本文通過對維基百科的十四個重要貢獻(xiàn)者的行為分析,揭示了人類什么樣的行為,會使其最終行為分布的冪指數(shù)是多少的對應(yīng)關(guān)系。 本文的研究意義在于:由于綜合模型仿真出來的結(jié)果與實證相符合,這說明本文所歸納綜合的人類行為模型是比較正確的,可以在公共設(shè)施資源的建設(shè)與分配中有比較大的實用價值。
[Abstract]:For a long time, scholars think that the time interval of human behavior in social and economic activities can be simply described by Poisson distribution. However, since 2005, Albert Barb 謾 si et al have conducted a large number of empirical studies and found that the occurrence time of human behavior does not all conform to the uniform Poisson distribution, but a large number of them follow the power-law distribution with fat tail. The power exponents of two universal classes of human behavior are also proposed. There are priority queuing theory, memory theory, habit model, self-adaptive model and so on to explain this aspect of behavior theory, but these models are only one aspect of human behavior characteristics. Through a comprehensive empirical analysis of Wikipedia, this paper obtains the basic characteristics of Wikipedia's human behavior, and then puts forward a comprehensive model, and then tests the results of empirical research in an all-round way. And from the theoretical analysis of the underlying causes of this behavior. In this paper, the following main conclusions are obtained: the number of login times of all users in Wikipedia, the number of login times of some users and the interval of login time have approximate power law distribution fitting. The power indices obtained by empirical analysis can be divided into three (from small to large: 1, 1.5, 2), in which two power indices (1 and 1.5) and two universal classes proposed by V 謾 zquez are consistent. But simply dividing the human behavior characteristics into two universal power indices is not complete enough, at least there should be a third power index (the size of the index is 2). The simulation results of the synthetic model of human behavior can be basically divided into four power exponents, from small to large, which are 0.6, 1, 1.5, 2. The last three power indices are consistent with the empirical results of Wikipedia, but the fourth power index-0.6 is obtained by the model. By analyzing the behavior of fourteen important contributors of Wikipedia, this paper reveals the relationship between what kind of human behavior and the power index of final behavior distribution. The research significance of this paper is as follows: because the simulation results of the synthetic model are consistent with the empirical results, this shows that the synthetic human behavior model in this paper is relatively correct. It has great practical value in the construction and distribution of public facilities resources.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：O211.3;C912

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