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.

翻译：在经济学、政策与技术领域，复杂变化常被描述为“演化性”的，然而大多数系统动力学模型仍局限于固定状态空间与均衡寻求行为。本文主张，演化动力学应被视为系统思维的核心问题，而非生物学隐喻。我们引入稳定性驱动组装（SDA）作为一种极简的非均衡框架，其中随机相互作用与差异持续性相结合，可在无需基因、复制或预定义适应度函数的情况下产生内生选择。在SDA中，寿命更长的模式在群体中积累，从而偏置未来的相互作用，并在群体构成与系统动力学之间建立反馈。这种反馈使适应度比例采样作为涌现特性得以实现，形成了一种完全由稳定性驱动的自然遗传算法。通过SDA，我们论证了为何受均衡约束的模型（即使经过数值模拟）也无法展现开放演化：演化系统需要依赖群体的非平稳动力学，其中结构与动力学共同演化。最后，我们讨论其对系统动力学、经济学与政策建模的启示，并概述基于主体与人工智能赋能的方法如何支持能够持续产生新颖性与结构涌现的演化模型。