Uncaused Semantic Interpretation of Cognitive Models in Networked Decision Support Systems

The semantic interpretation of cognitive models helps to improve the quality of networked group decision-making processes. The consideration of illogical, uncaused factors' relationships of cognitive models is particular complexity. The present work is aimed constructing an approach of verifyin...

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Bibliographic Details
Main Author: Raikov, Alexander N.
Format: Conference Proceeding
Language:English
Subjects:
Big Data
Biological system modeling
Cognitive model
Data models
Decision making
decision support system
Decision support systems
Linguistics
resonant-analog mechanisms
semantic interpretation
Semantics
uncaused relationships
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Summary:The semantic interpretation of cognitive models helps to improve the quality of networked group decision-making processes. The consideration of illogical, uncaused factors' relationships of cognitive models is particular complexity. The present work is aimed constructing an approach of verifying factors and their mutual relationships in the cognitive models on the base of Big Data analyzing under both logical and uncaused factors' relationships. The paper also shows the possibility of distorting the semantic interpretation of cognitive models because of discrete data processing. The application of resonance-analog mechanisms for constructing semantic interpretations for modeling is substantiated.
ISSN:2472-8586
DOI:10.1109/ICAICT.2017.8686968