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

Dan Adler

TL;DR

The paper argues that conventional equilibrium-centric system dynamics cannot capture open-ended evolution, and proposes Stability-Driven Assembly (SDA) as a minimal non-equilibrium mechanism in which differential persistence biases the accumulation of patterns, producing endogenous selection without genes or predefined fitness. The population distribution evolves under a nonlinear, population-dependent drift that can be described by a $Fokker$–$Planck$-type framework, $ rac{",

Abstract

Complex change is often described as "evolutionary" in economics, policy, and technology, yet most system dynamics models remain constrained to fixed state spaces and equilibrium-seeking behavior. This paper argues that evolutionary dynamics should be treated as a core system-thinking problem rather than as a biological metaphor. We introduce Stability-Driven Assembly (SDA) as a minimal, non-equilibrium framework in which stochastic interactions combined with differential persistence generate endogenous selection without genes, replication, or predefined fitness functions. In SDA, longer-lived patterns accumulate in the population, biasing future interactions and creating feedback between population composition and system dynamics. This feedback yields fitness-proportional sampling as an emergent property, realizing a natural genetic algorithm driven solely by stability. Using SDA, we demonstrate why equilibrium-constrained models, even when simulated numerically, cannot exhibit open-ended evolution: evolutionary systems require population-dependent, non-stationary dynamics in which structure and dynamics co-evolve. We conclude by discussing implications for system dynamics, economics, and policy modeling, and outline how agent-based and AI-enabled approaches may support evolutionary models capable of sustained novelty and structural emergence.

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

TL;DR

The paper argues that conventional equilibrium-centric system dynamics cannot capture open-ended evolution, and proposes Stability-Driven Assembly (SDA) as a minimal non-equilibrium mechanism in which differential persistence biases the accumulation of patterns, producing endogenous selection without genes or predefined fitness. The population distribution evolves under a nonlinear, population-dependent drift that can be described by a –-type framework, $ rac{",

Abstract

Complex change is often described as "evolutionary" in economics, policy, and technology, yet most system dynamics models remain constrained to fixed state spaces and equilibrium-seeking behavior. This paper argues that evolutionary dynamics should be treated as a core system-thinking problem rather than as a biological metaphor. We introduce Stability-Driven Assembly (SDA) as a minimal, non-equilibrium framework in which stochastic interactions combined with differential persistence generate endogenous selection without genes, replication, or predefined fitness functions. In SDA, longer-lived patterns accumulate in the population, biasing future interactions and creating feedback between population composition and system dynamics. This feedback yields fitness-proportional sampling as an emergent property, realizing a natural genetic algorithm driven solely by stability. Using SDA, we demonstrate why equilibrium-constrained models, even when simulated numerically, cannot exhibit open-ended evolution: evolutionary systems require population-dependent, non-stationary dynamics in which structure and dynamics co-evolve. We conclude by discussing implications for system dynamics, economics, and policy modeling, and outline how agent-based and AI-enabled approaches may support evolutionary models capable of sustained novelty and structural emergence.
Paper Structure (14 sections, 1 equation, 5 figures, 1 algorithm)

This paper contains 14 sections, 1 equation, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. The Informal Use of "Evolution" in Systems Discourse
  3. Stability-Driven Assembly as an Evolutionary Mechanism
  4. Illustrative Simulation Results
  5. Limits of Equilibrium-Constrained System Models
  6. Limits of Agent-Based Simulation for Evolutionary Dynamics
  7. Agent-Based Models Without Population Feedback
  8. Stochastic Interaction as a Systems-Level Primitive
  9. Adding Evolutionary Dynamics to Stocks-and-Flows Models
  10. Toward Open-Ended Evolutionary System Models
  11. Implications for Economics and Policy Modeling
  12. Illustrative Example: Industry Ecosystems
  13. Implications for Economic and Policy Systems
  14. Conclusion

Figures (5)

  • Figure 1: Google Trends interest for domain-specific uses of the term "evolution." The term is used persistently and comparably across biological, physical, and socio-technical domains, motivating the need for a general mechanistic account of evolutionary dynamics.
  • Figure 2: Stability-Driven Assembly (SDA/GA) loop. Base elements are continuously replenished while unstable motifs expire, shaping the active population through differential persistence. Patterns are sampled from the population and combined through interaction (concatenation in SDA or recombination in SDA/GA), generating new motifs that re-enter the population with lifetimes determined by their stability. The resulting feedback from stability to persistence to population composition produces emergent selection without genes, replication, or an explicit fitness function.
  • Figure 3: Core feedback structure underlying Stability-Driven Assembly. Stochastic interactions generate patterns, while differential persistence shapes population composition. The evolving population distribution biases future interactions, closing a feedback loop between persistence, population statistics, and pattern generation. Selection emerges endogenously from this loop without genes, replication, or an externally imposed fitness function.
  • Figure 4: Shannon entropy of the population distribution under Stability-Driven Assembly (SDA) compared to an unconstrained control. In the absence of persistence bias, entropy remains high, reflecting broad exploration of pattern space. Under SDA dynamics, entropy decreases steadily as probability mass accumulates on long-lived motifs, demonstrating emergent selection and order in a non-equilibrium system without explicit fitness functions.
  • Figure 5: Conceptual illustration of an industry ecosystem modeled as a Stability-Driven Assembly (SDA) process. Base economic entities (e.g., manufacturers, retailers, logistics providers) interact to form composite organizational structures. Configurations that persist longer under competitive and institutional pressures accumulate resources and participation, biasing future interactions. Over time, differential persistence shapes the population of organizational forms, producing evolutionary dynamics without centralized optimization or explicit replication.