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A computational Intelligence‐based Method to ‘Learn’ Causal Loop Diagram‐like Structures from Observed Data
Author(s) -
Abdelbari Hassan,
Shafi Kamran
Publication year - 2017
Publication title -
system dynamics review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.491
H-Index - 57
eISSN - 1099-1727
pISSN - 0883-7066
DOI - 10.1002/sdr.1567
Subject(s) - computer science , causal loop diagram , loop (graph theory) , process (computing) , artificial intelligence , system dynamics , causal model , key (lock) , data mining , data flow diagram , conceptual model , artificial neural network , machine learning , complex system , theoretical computer science , mathematics , combinatorics , database , statistics , computer security , operating system
The development of conceptual models using causal loop diagrams and their variants is a key step in the system dynamics modeling process. This work seeks to explore to what extent such models can be inferred directly from system observations using computational methods. A novel echo state neural network‐based methodology is proposed to automatically learn causal loop diagram‐like structures directly from system observations. The proposed data‐driven approach aims at complementing the conceptual model development process by providing modelers with several probable model structures that can be accepted readily or considered for refinement. Three measures, used in comparing mental models, are adopted to compute similarity between the learned and target model structures. Using three well‐known system dynamics case studies, we show the effectiveness of the proposed method in learning close model structures directly from the system observations, generated by simulating the stock‐and‐flow models for these cases. Copyright © 2017 System Dynamics Society