Stability-driven assembly theory

The emergence of complexity and information remains a fundamental question in all scientific disciplines. This paper introduces Stability-Driven Assembly (SDA) systems, a theoretical framework that models pattern evolution through probabilistic interactions governed by differential stability. Our computational experiments demonstrate that stability differences alone can generate selection pressure, driving systems toward lower-entropy states characterized by dominant stable patterns, without requiring explicit replication mechanisms. Through controlled intervention experiments, we establish a causal relationship within the model between stability constraints and system evolution, showing how stability functions as a control parameter shaping the evolutionary landscape. The SDA framework reveals the interplay between bottom-up assembly processes and top-down selective pressures, where emergent stable patterns recursively influence future interactions. Dynamic network visualization further illuminates how stability-driven selection operates on all scales. These findings may provide the foundation for a mechanistic explanation of how complexity and information can emerge in abiotic systems. Looking ahead, we suggest that SDA could provide a useful lens for connecting stochastic dynamical systems with prebiotic chemistry, and for exploring hypotheses about how stability-driven dynamics might precede the emergence of autocatalysis, replication, and Darwinian evolution.