Modeling of Social Transitions Using Intelligent Systems

📝 Abstract

In this study, we reproduce two new hybrid intelligent systems, involve three prominent intelligent computing and approximate reasoning methods: Self Organizing feature Map (SOM), Neruo-Fuzzy Inference System and Rough Set Theory (RST),called SONFIS and SORST. We show how our algorithms can be construed as a linkage of government-society interactions, where government catches various states of behaviors: solid (absolute) or flexible. So, transition of society, by changing of connectivity parameters (noise) from order to disorder is inferred.

💡 Analysis

In this study, we reproduce two new hybrid intelligent systems, involve three prominent intelligent computing and approximate reasoning methods: Self Organizing feature Map (SOM), Neruo-Fuzzy Inference System and Rough Set Theory (RST),called SONFIS and SORST. We show how our algorithms can be construed as a linkage of government-society interactions, where government catches various states of behaviors: solid (absolute) or flexible. So, transition of society, by changing of connectivity parameters (noise) from order to disorder is inferred.

📄 Content

Modeling of Social Transitions Using Intelligent Systems

Hamed .O.Ghaffari* , Witold Pedrycz†& Mostafa.Sharifzadeh* Department of Mining and Metallurgical Engineering,
Amirkabir University of Technology, Tehran, Iran
h.o.ghaffari@gmail.com sharifzadeh@aut.ac.ir

†Department of Electrical and Computer Engineering ,University of Alberta Alberta, Canada - pedrycz@ece.ualberta.ca

Abstract In this study, we reproduce two new hybrid intelligent systems, involve three prominent intelligent computing and approximate reasoning methods: Self Organizing feature Map (SOM), Neruo-Fuzzy Inference System and Rough Set Theory (RST),called SONFIS and SORST. We show how our algorithms can be construed as a linkage of government-society interactions, where government catches various states of behaviors: “solid (absolute) or flexible”. So, transition of society, by changing of connectivity parameters (noise) from order to disorder is inferred.

1. Introduction

Complex systems are often congruous with uncertainty and order-disorder transitions. Apart of uncertainty, fluctuations forces due to competition of between constructive particles of system drive the system towards order and disorder. There are prominent examples which their behaviors show such anomalies in their evolution, i.e., physical systems, biological and financial systems [1]. In other view, in monitoring of most complex systems, there are some generic challenges for example sparse essence, conflicts in different levels, inaccuracy and limitation of measurements ,which in beyond of inherent feature of such interacted systems are real obstacles in their analysis and predicating of behaviors. There are many methods to analyzing of systems include many particles that are acting on each other, for example statistical methods [2], Vicsek model [3] etc.

A discrete motivation in this way is finding out of “main nominations of each distinct behavior which may has overlapping, in part, to others”. This advance is to bate of some mentioned difficulties that can be concluded in the “information granules” proposed by Zadeh [4]. In fact, more complex systems in their natural shape can be described in the sense of networks, which are made of connections among the units. These units are several facets of information granules as well as clusters, groups, communities, modules [5]. In this study, we reproduce two hybrid intelligent systems [9], [10]: Self Organizing Neruo-Fuzzy Inference System (SONFIS) and Self Organizing Rough Set Theory (SORST), and then investigate several levels of responses against the real information (cumulative information flow). We show how these methods can produce (mimic) the interacted behaviors of government-nation. Mutual relations between the proposed algorithms layers identify order-disorder transferring of similar systems.

2. Methods

In this section based upon Self Organizing feature Map (SOM) [6], adaptive Neuro-Fuzzy Inference System (NFIS) [7] and Rough Set Theory (RST) [8], we reproduce: Self Organizing Neuro-Fuzzy Inference System (SONFIS) and Self Organizing Rough Set Theory (SORST) [9], [10]. In this study our aim is to investigate order-disorder transition in the mentioned systems. The mentioned algorithms use four basic axioms upon the balancing of the successive granules assumption: Step (1): dividing the monitored data into groups of training and testing data Step (2): first granulation (crisp) by SOM or other crisp granulation methods
Step (2-1): selecting the level of granularity randomly or depend on the obtained error from the NFIS or RST (regular neuron growth) Step (2-2): construction of the granules (crisp). Step (3): second granulation (fuzzy or rough granules) by NFIS or RST Step (3-1): crisp granules as a new data. Step (3-2): selecting the level of granularity; (Error level, number of rules, strength threshold, scaling of inserted information…) Step (3-3): checking the suitability. (Close-open iteration: referring to the real data and reinspect closed world) Step (3-4): construction of fuzzy/rough granules. Step (4): extraction of knowledge rules Balancing assumption is satisfied by the close-open iterations: this process is a guideline to balancing of crisp and sub fuzzy/rough granules by some random/regular selection of initial granules or other optimal structures and increment of supporting rules (fuzzy partitions or increasing of lower /upper approximations ), gradually.

Information Nodes Government Rules External Forces Government Performance Society

Figure1. A general schematic of Society-Government network

The overall schematic of SONFIS and SORST-AS has been shown in Figure 2 and Figure 3. In first granulation step, we can use a linear relation or a power-law which is given by: 1 ; t t t t t N N E α β γ

• =
• ∆∆=

   (1) 

1 t t t N N E α β γ

• =

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