科学知识增长过程中系统自组织创生模式研究

Research on the Birth Mode of System Self-organizing in Scientific Knowledge Growth Process

[目的 /意义]通过微观层面上个体的因果相互作用来阐述信息计量领域宏观幂律分布现象形成的必然性,将其从随机性的统计规律转变成为必然性的动力学规律。[方法 /过程]在幂律分布必然性的揭示上,抛弃粗糙的机械还原论视角,而将其放在更加精密的复杂系统的分析框架下。数学论证上以普赖斯引文网络为实例,运用主方程、隐马尔科夫链推导出双参数广义普赖斯定理β函数数学描述并进一步推导出3条数学性质。[结果/结论]将信息计量领域普遍存在的偏态随机性统计规律发展成为确定性的系统动力学规律,即在简单线性累加优势规则而非马太效应规则的约束下,通过最细粒度层级上的因果二元组的多次正向性互动反馈,经由临界涨落和对称性打破,根据严谨的网络动力学数学语言描述出系统的自组织有序性稳态建构。

[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 powerlaw 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.