Open-Ended Evolution (Davies-Walker Framework)

Open-ended evolution is the capacity of a system to generate genuine novelty without reaching a ceiling—to continue producing new forms, new capabilities, new structures indefinitely rather than exhausting the space of possibilities defined by its initial rules. Biological evolution on Earth is the paradigmatic case: 3.8 billion years of continuous innovation, from single cells to multicellular organisms to nervous systems to brains to symbolic thought, with no sign of approaching an endpoint. Paul Davies and Sara Imari Walker's 2017 research formalized the conditions required for open-endedness: state-dependent dynamics, where the rules governing the system's behavior change in response to the system's own outputs. Fixed-rule systems, no matter how complex, eventually exhaust their novelty. Systems whose rules co-evolve with their states do not. Current AI architectures operate on fixed rules applied to fixed (or slowly updating) training distributions and therefore lack the capacity for genuinely open-ended creativity. But human-AI collaboration can exhibit state-dependent dynamics through the real-time feedback loop: the human's