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

共享信息组织知识创造与进化学习过程模拟

基本信息

批准号：
12680449
负责人：
TAKAHASHI Shingo
金额：
$ 2.3万
依托单位：
Waseda University (2001)Chiba Institute of Technology (2000)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12680449/
关键词：
organizational learning hypergame agent agent-based modeling genetic algorithm 進化的方法内部モデル

项目摘要

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.

基于本研究引入的网络型动态超博弈，我们建立了几种能够处理博弈形式及其要素：收益矩阵和策略集学习过程的模型。在每个模型中，使用遗传算法分析学习过程。在仿真结果的基础上，提出了综合描述超博弈情况的通用分布式模型和基于智能体的方法。利用新建立的通用模型，利用遗传算法研究了智能体在一定约束条件下的学习过程。本研究的主要成果如下：1)从网络型动态超博弈的建模和仿真结果来看，在模型的描述层面，我们可以明确区分代表两种组织学习类型的两个层次。2)在分布模型的基础上建立了组织的基本模型，该模型是迄今为止发展的超博弈模型的推广和扩展。这个提法可以明确区分组织中两种类型的学习水平。3)本文描述了本研究结果与应用于组织的基于agent的建模之间的内在关系。(4)本研究建立的基础学习模型可以给出在整个组织学习模型中发挥核心作用的基础学习模块。