Simulation on Process of Knowledge Creation and Evolutionary Learning of Organizations with Shared Information

基于本研究中引入的网络型动态超博弈，我们建立了几种类型的模型，可以处理游戏形式及其元素的学习过程：支付矩阵和策略集。在每个模型中，学习过程进行了分析与遗传算法。基于这些仿真结果，一般分布式模型，它表示全面的超对策的情况下沿着与基于代理的方法，介绍。利用新建立的一般模型，在一定的约束条件下，利用遗传算法研究了智能体的学习过程，主要研究结果如下：1）通过对网络型动态超对策的建模和仿真，在模型的描述层次上，明确区分代表两种组织学习类型的两个层次。2）我们制定了一个基于分布式模型的组织基本模型，这是迄今为止发展起来的超对策模型的推广和扩展。这一提法可以明确区分两种类型的学习水平在组织中。3）我们描述了一个内在的关系，这项研究的结果与基于代理的建模应用于组织。4）在这项研究中开发的基本模型可以给基本的学习模块发挥核心作用，在整个模型的组织学习。

Based on network-type dynamic hypergames introduced in this research, we formulated several types of models that can deal with the learning processes on game form and its elements: payoff matrix and the set of strategies. In each model, learning processes were analyzed with genetic algorithm. Based on these simulation results, general distributed model, which represents comprehensively hypergame situations along with agent-based approach, were introduced. Using the newly developed general model, learning processes of agents were investigated with genetic algorithm under some restricted conditions.The main results of this research are as follows.1) From the modeling and simulation results on network-type dynamic hypergame, we could, at the description level of models, make clear distinction between two levels representing two types of organizational learning.2) We formulated a basic model of organization based on distributive models, which are obtained as generalized and extended ones of hypergame models developed so far. This formulation can make clear the distinction between the two types of learning levels in organizations.3) We describes an intrinsic relationship of this research results with agent-based modeling applied to organizations.4) The basic models developed in this research can give fundamental learning modules playing central roles in a whole model of organizational learning.