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

2026-02-17

AI Summary

Traditional system dynamics models are constrained by fixed state spaces and equilibrium assumptions, limiting their ability to capture open-ended evolution in economic, policy, and technological domains. This work proposes a Stability-Driven Assembly (SDA) framework that leverages stability as the sole driving force to enable endogenous selection through stochastic interactions and differential persistence, without requiring predefined fitness functions or genetic structures. Within this framework, system structure and dynamics co-evolve, and—remarkably—fitness-proportional sampling behavior emerges naturally in a non-equilibrium setting for the first time. By transcending the constraints of equilibrium models on sustained innovation and structural emergence, SDA offers a novel paradigm for modeling economics and policy that supports open-ended evolutionary processes.

Technology Category

• Game Theory and Economic Paradigms: Equilibrium
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• evolutionary dynamics
• open-ended evolution
• non-equilibrium systems
• structural emergence
• system dynamics

Innovation

• Stability-Driven Assembly
• non-equilibrium dynamics
• endogenous selection
• open-ended evolution
• emergent fitness