A computational Intelligence-based Method to ‘Learn’ Causal Loop Diagram-like Structures from Observed Data

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