Library and Information Service >
Research on the Birth Mode of System Self-organizing in Scientific Knowledge Growth Process
Received date: 2015-09-06
Revised date: 2015-12-07
Online published: 2015-12-20
[Purpose/significance] This paper aims to explain the necessity of power-law distribution phenomenon in the field of informetrics through the causal interaction of individuals on the micro level, which will make it transform from the statistical regularity of random into the dynamic rule of necessity.[Method/process] From the perspective of more sophisticated complex system theory rather than the rough mechanical reductionism, this paper reveals the necessity of power-law distribution. It takes the Price citation network for instance, and uses the Mater Equation and the Markov Chain to deduce the mathematical description of Euler β of double parameters generalized Price Theorem with three mathematical properties.[Result/conclusion] In conclusion, this article makes the prevalent skewed distribution evolving from macroscopic random statistics to microscopic explicit dynamics in informetrics. Namely, under the constraint of a simple linear cumulative advantage rule rather than the Matthew effect rule, the system can achieve static steady architecture with rigorous mathematical network dynamics according to the positive feedbacks of causal interactions of 2-tuples under the minimal granularity with numerous iterations via critical state fluctuation and symmetry breaking in the process of self-organizing ordering.
Wan Hao , Tan Zongying , Zhu Xiangli , Zhang Chaoxing . Research on the Birth Mode of System Self-organizing in Scientific Knowledge Growth Process[J]. Library and Information Service, 2015 , 59(24) : 93 -101 . DOI: 10.13266/j.issn.0252-3116.2015.24.014
[1] Garfield E.The history and meaning of the journal impact factor[J]. Journal of the American Medical Association, 2006, 295(1): 90-93.
[2] Price D J D S. Networks of scientific papers[J]. Science, 1965,149(368):510-515.
[3] Hill B M. Rank frequency forms of Zipf's law[J]. Journal of the American Statistical Association, 1974, 69(348): 1017-1026.
[4] Price D J D S. A general theory of bibliometric and other cumulative advantage processes[J]. Journal of the American Society for Information Science, 1976, 27(5-6): 292-306.
[5] Haitun S D. Stationary scientometric distributions.1. different approximations [J]. Scientometrics, 1982, 4(1): 5-25.
[6] Haitun S D. Stationary scientometric distributions.2. non-gaussian nature of scientific activities[J]. Scientometrics, 1982, 4(2): 89-104.
[7] Haitun S D. Scientometrics and physics[J].Czechoslovak Journal of Physics, 1986, 36(1):54-57.
[8] van Raan A F J. On growth, aging, and fractal differentiation of science[J]. Scientometrics, 2000, 47(2): 347-362.
[9] Egghe L. Power laws in the information production process: Lotkaian informetrics[M]. Kidlington: Elsevier Academic Press, 2005.
[10] Leydesdorff L. The Triple Helix perspective of innovation systems[J]. Technology Analysis & Strategic Management,2010, 22(7): 789-804.
[11] Leydesdorff L.The knowledge-based economy and the Triple Helix model[J]. Annual Review of Information Science and Technology, 2010, 44(年刊):378-417.
[12] Leydesdorff L. Meyer M. Triple Helix indicators of knowledge-based innovation systems-Introduction to the special issue[J].Research Policy, 2006, 35(10): 1441-1449.
[13] Leydesdorff L, Zhou P. Are the contributions of China and Korea upsetting the world system of science?[J]. Scientometrics, 2005, 63(3): 617-630.
[14] Leydesdorff L, Wagner C. Is the United States losing ground in science? A global perspective on the world science system[J]. Scientometrics, 2009, 78(1): 23-36.
[15] Meyer M. Tracing knowledge flows in innovation systems[J]. Scientometrics, 2002, 54(2): 193-212.
[16] Chen C M, Chen Y, Horowitza M, et al. Towards an explanatory and computational theory of scientific discovery[J]. Journal of Informetrics, 2009,3(3):191-209.
[17] Zhou Q J, Rousseau R, Yang L Y. A general framework for describing diversity within systems and similarity between systems with applications in informetrics[J]. Scientometrics, 2012, 93(3): 787-812.
[18] 霍兰. 隐秩序:适应性造就复杂性[M]. 周晓牧, 韩晖, 译. 上海:上海世纪出版集团,2005.
[19] Castiglione F. Agent based modeling[EB/OL].[2015-11-25].http://www.schalarpedia.org/article/Agent-basod-modeling.Scholarpedia, 2006, 1(10):1562.
[20] Tesfatsion L. Agent-based computational economics[EB/OL].[2015-11-25].http://www.schalarpedia.org/article/Agent-basod-computational-economics.Scholarpedia, 2007, 2(2): 1970.
[21] Stone J M. Computational astrophysics[EB/OL].[2015-11-25].http://www.schalarpedia.org/article/Computional-astrophysics.Scholarpedia, 2007, 2(10): 2419.
[22] Leydesdorff L. Can intellectual processes in the sciences also be simulated? The anticipation and visualization of possible future states[J]. Scientometrics, 2015, 105(3): 2197-2214.
[23] Haken H. Synergetics, an introduction[M]. Berlin: Springer, 1978.
[24] Burrell Q L. The use of the generalized waring process in modeling informetric data[J]. Scientometrics, 2005, 64(3):247-270.
[25] Egghe L. The power of power laws and an interpretation of Lotkaian informetric systems as self-similar fractals[J]. Journal of the American Society for Information Science and Technology, 2005, 56(7): 669-675.
[26] Egghe L. A new short proof of Naranan's theorem, explaining Lotka's law and Zipf's law[J]. Journal of the American Society for Information Science and Technology, 2010, 61(12): 2581-2583.
[27] Newman M E J.The structure and function of complex networks[J]. Society for Industrial and Applied Mathematics Review, 2003, 45(2): 167-256.
[28] Newman M E J. Power laws, Pareto distributions and Zipf's law[J]. Contemporary Physics, 2005, 46(5): 323-351.
[29] Yule G U. A mathematical theory of evolution based on the conclusions of Dr. J. C. Willis, F.R.S[J]. Journal of the Royal Statistical Society, 1925, 88(3): 433-436.
[30] Simon H A. On a class of skew distribution functions[J]. Biometrika, 1955, 42 (3-4): 425-440.
[31] Barabási A L, Albert R, Jeong H. Mean-field theory for scale-free random netwroks[J]. Physica A, 1999, 272(1-2): 173-187.
[32] Brin S, Page L. The anatomy of a large-scale hypertextual Web search engine[J]. Computer Networks and ISDN Systems, 1998, 30(1-7): 107-117.
[33] 生志荣. 负二项分布的两种近似分布及其比较[J]. 统计与信息论坛,2011, 26(1): 20-22.
[34] Merton R K. Matthew effect in science [J]. Science, 1968, 159(3810): 56-63.
[35] 库恩. 科学革命的结构[M]. 金吾伦, 胡新和, 译. 第四版. 北京: 北京大学出版社, 2012.
[36] 波普尔. 猜想与反驳: 科学知识的增长[M].傅季重, 译. 上海: 上海译文出版社,1986.
[37] 邱均平. 信息计量学[M]. 武汉: 武汉大学出版社, 2007.
/
| 〈 |
|
〉 |
