Premium
A system of IAC neural networks as the basis for self‐organization in a sociological dynamical system simulation
Author(s) -
Duong Deborah Vakas,
Reilly Kevin D.
Publication year - 1995
Publication title -
behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 0005-7940
DOI - 10.1002/bs.3830400402
Subject(s) - computer science , semiotics , cognitive science , sociology , associative property , connectionism , epistemology , content addressable memory , artificial intelligence , theoretical computer science , artificial neural network , psychology , mathematics , philosophy , pure mathematics
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.