Prominent Causal Paths in a Simple Self-Organizing System

The dynamic interactions of interdependent components in complex, adaptive, self-organizing systems (CASOS) often seem to sequester system entropy or uncertainty through distributed, as opposed to central, control. This article presents a system dynamics (SD) simulation model that not only replicates self-organizing system uncertainty results, but also looks at self-organization causally. The model analysis articulates how circular causal pathways or feedback loops in CASOS produce nonlinear dynamics spontaneously out of local interactions. The SD simulation and model analysis results show exactly how distributed control leads positive feedback to explosive growth, which ends when all dynamics have been absorbed into an attractor, leaving the system in a stable, negative feedback state. Cast as a methodological contribution, the article’s SD model analysis explains why phenomena of interest emerge in agent-based models, a topic crucial in understanding and designing CASOS. Moreover, CASOS concepts inspired by nature and biology can motivate biologically-inspired IS research.