基于元胞自动机的个体知识传播行为研究

Research on Knowledge Transfer Behavior of individuals Based on Cellular Automata

摘要知识交流与传播是组织获取知识，提高自身知识存量及知识竞争优势的重要手段之一，越来越受到知识管理者的重视。因此，本文立足于组织内个体之间的知识获取与互传播，考虑个体不同的学习态度将个体分为主动学习和被动学习两类，并将个体知识表示为一个多维知识向量，个体在知识交流过程中相互影响会产生学习态度、行为及知识存量的改变，在此基础上，建立了基于状态更新和知识传播双层规则的元胞自动机知识传播模型，模拟分析了邻域结构及初始主动学习率对组织内个体知识传播行为的影响，并对模型中加入人员移动和专家引进机制的情形进行了模拟研究。研究发现，组织内人员微观层次上的局部交互行为在宏观上表现出自组织的复杂特性；Moore邻域结构更有利于组织内个体间的知识交流与传播；组织平均知识存量的增长速度随着初始主动学习率的提高而加快；人员移动和专家机制的引入能够提高组织内部知识传播的效率和知识存量；此外还发现组织平均知识存量的增长速度与人员的自主学习能力成正相关。

Knowledge exchange and transfer are key means for the organization to acquire knowledge and hence promote its knowledge stock and competitive competence, which has attracted more attention from many knowledge managers. This paper starts with this issue and focuses on the different learning styles of individuals within the organization. The individuals involved are divided into two types, i.e. active learner or passive learner, whose learning attitude, behavior and knowledge stock will be changed during the process of knowledge exchange. The knowledge to be exchanged is expressed by a multi-dimensional vector. Hereby, a cellular automata knowledge transfer model is proposed to study the influence of structure of neighborhood, the ratio of initial active learners, the people movement and expert introduction mechanisms on the performance of knowledge transfer processes by using two kinds of rules which are the state update rule and the knowledge exchange rule respectively. Simulation results show that the local interactions between individuals at the micro-level reveal some complex properties like self-organization at the macro-level within the organization. The structure of Moore neighborhood is more effective on the knowledge exchange and transfer within the organization, and the growth of the average knowledge stock is going up with the increase of the ratio of initial active learners. Both the people movement and the expert introduction mechanisms can improve the performance of knowledge transfer and knowledge stocks. Besides, the growth of the average knowledge stock is proportional to the self-learning ability of individuals.