Evolutionary Systems Thinking: From Equilibrium Models to Open-Ended Adaptive Dynamics
Complex change is often described as ``evolutionary'' in economics, policy, technology, and organizations, yet many system dynamics models represent behavior within a fixed set of stocks, flows, relationships, and transition rules. Such models can generate nonlinear, oscillatory, path-dependent, or chaotic behavior, but structural novelty must ordinarily be specified in advance. This paper argues that evolutionary dynamics should be treated as a core systems-thinking problem rather than as a biological metaphor. We introduce Stability-Driven Assembly (SDA), a minimal non-equilibrium framework in which stochastic interactions and differential persistence generate endogenous selection without genes, template-based replication, or an externally specified fitness function. Longer-lived configurations accumulate in the population and therefore become more likely to participate in subsequent interactions. This creates feedback among persistence, population composition, and future pattern formation. The resulting abundance-weighted sampling is equivalent to fitness-proportional selection, allowing SDA to be interpreted as a natural genetic algorithm driven by persistence-weighted population dynamics. SDA provides a conceptual basis for distinguishing fixed-state-space dynamics from evolving possibility spaces, in which persistent structures can reshape future flows, interactions, and available configurations. It also suggests that equilibrium should be treated as provisional: a quasi-stable regime may be reorganized when a more persistent configuration emerges. We conclude by outlining two ways to extend system dynamics practice: constructing an SDA-style population model alongside a stock-flow model, and using SDA perturbation analysis to examine the vulnerability of an existing regime to structural innovation.