在社会学动态系统模拟中,IAC神经网络系统作为自组织的基础。

D V Duong, K D Reilly
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引用次数: 0

摘要

这个社会学模拟使用符号学和符号互动主义的思想来证明,在微观层面上,个人头脑中适当发展的联想记忆如何能够自我组织成宏观层面的社会消散结构,如种族、文化/经济阶层、地位象征和时尚。所使用的联想记忆是基于IAC神经网络(交互激活和竞争网络)的扩展。几个IAC网络通过其直觉和创造力等人类属性共同行动,形成一个社会。这些属性赋予他们创造和理解符号的能力,从而形成社会的宏观结构。该系统采用面向对象的分层容器类实现,便于深层结构的改变。给出了该动力系统的一般趋势图和模拟运行的历史记录。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A system of IAC neural networks as the basis for self-organization in a sociological dynamical system simulation.

This sociological simulation uses the ideas of semiotics and symbolic interactionism to demonstrate how an appropriately developed associative memory in the minds of individuals on the microlevel can self-organize into macrolevel dissipative structures of societies such as racial cultural/economic classes, status symbols and fads. The associative memory used is based on an extension of the IAC neural network (the Interactive Activation and Competition network). Several IAC networks act together to form a society by virtue of their human-like properties of intuition and creativity. These properties give them the ability to create and understand signs, which lead to the macrolevel structures of society. This system is implemented in hierarchical object oriented container classes which facilitate change in deep structure. Graphs of general trends and an historical account of a simulation run of this dynamical system are presented.

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