What is System Dynamics Modeling? Defining Characteristics and the Opportunities they Create

TL;DR

This paper defines core characteristics of system dynamics modeling, clarifies its principles, and explores research opportunities to advance the field through causality, data science, and scientific contribution.

Contribution

It introduces a set of defining characteristics for system dynamics modeling, providing a clear framework to guide future research and practice.

Findings

Core characteristics of system dynamics identified

Implications for research and practice discussed

Opportunities in causality, AI, and scientific contribution highlighted

Abstract

A clear definition of system dynamics modeling can provide shared understanding and clarify the impact of the field. We introduce a set of characteristics that define quantitative system dynamics, selected to capture core philosophy, describe theoretical and practical principles, and apply to historical work but be flexible enough to remain relevant as the field progresses. The defining characteristics are: (1) models are based on causal feedback structure, (2) accumulations and delays are foundational, (3) models are equation-based, (4) concept of time is continuous, and (5) analysis focuses on feedback dynamics. We discuss the implications of these principles and use them to identify research opportunities in which the system dynamics field can advance. These research opportunities include causality, disaggregation, data science and artificial intelligence, and contributing to scientific advancement. Progress in these areas has the potential to improve both the science and practice of system dynamics.