Quantitative Biology > Populations and Evolution
arXiv:2602.15957 (q-bio)
[Submitted on 17 Feb 2026 (v1), last revised 6 Aug 2026 (this version, v2)]
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.
| Comments: | 17 pages, 5 figures |
| Subjects: | Populations and Evolution (q-bio.PE); Neural and Evolutionary Computing (cs.NE); Theoretical Economics (econ.TH) |
| Cite as: | arXiv:2602.15957 [q-bio.PE] |
| (or arXiv:2602.15957v2 [q-bio.PE] for this version) | |
| https://doi.org/10.48550/arXiv.2602.15957 arXiv-issued DOI via DataCite |
|
| Journal reference: | Presented at the 44th International System Dynamics Conference (ISDC), Delft, The Netherlands, July 23, 2026 |
Submission history
From: Dan Adler [view email]
[v1]
Tue, 17 Feb 2026 19:17:50 UTC (627 KB)
[v2]
Thu, 6 Aug 2026 18:59:01 UTC (19,583 KB)
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