  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.
