Predicting dynamic behaviors is one of the goals of science in general as
well as essential to many specific applications of human knowledge to real
world systems. Here we introduce an analytic approach using the sigmoid growth
curve to model the dynamics of individual entities within complex systems.
Despite the challenges posed by nonlinearity and unpredictability in system
behaviors, we demonstrate the applicability of the sigmoid curve to capture the
acceleration and deceleration of growth, predicting an entitys ultimate state
well in advance of reaching it. We show that our analysis can be applied to
diverse systems where entities exhibit nonlinear growth using case studies of
(1) customer purchasing and (2) U.S. legislation adoption. This showcases the
ability to forecast months to years ahead of time, providing valuable insights
for business leaders and policymakers. Moreover, our characterization of
individual component dynamics offers a framework to reveal the aggregate
behavior of the entire system. We introduce a classification of entities based
upon similar lifepaths. This study contributes to the understanding of complex
system behaviors, offering a practical tool for prediction and system behavior
insight that can inform strategic decision making in multiple domains.
- Predicting System Dynamics of Universal Growth Patterns in Complex Systems
- Leila HedayatifarAlfredo J MoralesDominic E SaadiRachel A RiggOlha BuchelAmir AkhavanEgemen SertAabir Abubaker KarMehrzad SasanpourIrving R EpsteinYaneer Bar-Yam
- 9924433662701921
- Neuroscience Program; Benjamin and Mae Volen National Center for Complex Systems; Department of Chemistry
- English
- Preprint