q-bio.PEFeb 17, 2026

Evolutionary Systems Thinking: From Equilibrium Models to Open-Ended Adaptive Dynamics

Authors: Dan Adler

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Abstract

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.

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Figure 1: Fixed-state-space models move through a predefined possibility space, whereas evolutionary systems can reshape the effective space of future possibilities as persistent structures accumulate.
Figure 2: From random assembly to Stability-Driven Assembly. In the baseline case, stochastic interaction alone performs an unbiased search over nearby possibilities. Adding only two operations—removal of expired patterns and assignment of persistence according to a stability function SS —creates a population bias. Persistent motifs remain in the population longer, accumulate, and participate more often in subsequent interactions.
Figure 3: Core feedback structure underlying SDA. Stochastic interactions generate patterns; differential persistence reshapes population composition; and the evolving population distribution biases future interactions. Selection emerges endogenously from this feedback loop.
Figure 4: Shannon entropy of the population distribution under SDA and a uniform-persistence control. Entropy remains comparatively high when all patterns receive the same lifetime. Under differential persistence, probability mass becomes concentrated on a smaller set of long-lived motifs, producing lower population entropy.
Figure 5: Punctuated reorganization in an SDA population. A quasi-stable regime dominated by persistent configurations can be disrupted when a higher-persistence configuration emerges or is introduced. As the new configuration accumulates, it redirects interaction flows and establishes a new quasi-stable regime.
Figure 6: Illustrative pathways through which persistent structures can reshape future possibilities in three system dynamics domains. In markets, a widely adopted strategy can create niches for complementary strategies and services. In organizations, persistent operations, metrics, workflows, and tools can enable new roles and capabilities. In policy systems, programs, agencies, metrics, and constituencies created by one intervention can enable or constrain subsequent policy choices. These examples are conceptual mappings rather than calibrated models.
Figure 7: Two proposed approaches to introducing evolutionary dynamics into system dynamics models. Left: A coupled SDA population model represents the formation, context-dependent persistence, turnover, and accumulation of entities such as strategies, technologies, institutions, or organizational configurations. Right: SDA perturbation analysis begins from a reference regime generated by an existing stock–flow model and introduces candidate structural disruptors to test whether they disappear, coexist with existing structures, or reorganize the regime.
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