The Emergence Experiment Program

Every metric that showed improvement — world model capacity, representation quality, affect geometry alignment, self-model salience — showed it overwhelmingly at population bottlenecks. When drought kills 90% of patterns, the survivors are not random. They are the ones whose internal structure actively maintains integration under stress.

The bottleneck is not just a filter. It is a furnace. V13 seed 123 at cycle 5: population drops to 55, robustness crosses 1.052. At cycle 29 (population 24): world model capacity jumps to 0.028, roughly 100x the population average. One surviving pattern achieves self-model salience above 1.0 — privileged self-knowledge exceeding environment-knowledge.

These are not gradual evolutionary trends. They are punctuated events driven by intense selection pressure. The biological dynamics emerge not from accumulated innovation but from crucibles of near-extinction.

V19 confirmed this is creation, not selection. After ten cycles of shared evolution on V18 substrate, patterns were forked into three conditions: BOTTLENECK (two severe 8%-regen droughts per cycle, ~90% mortality), GRADUAL (mild continuous stress), and CONTROL (standard schedule). All three then faced identical novel extreme drought. Controlling for baseline $\intinfo$, the bottleneck-evolved condition showed significantly higher novel-stress robustness in 2/3 seeds (seed 42: β=0.704, $p < 0.0001$; seed 7: β=0.080, $p = 0.011$). The furnace forges novel-stress generalization — it does not merely filter for pre-existing capacity.

Three experiments returned null results: counterfactual detachment (Experiment 5), self-model emergence (Experiment 6), and proto-normativity (Experiment 9). All hit the same wall.

The prediction was that patterns would start reactive — driven by boundary observations — and gradually develop autonomous internal processing. Instead, patterns are always internally driven ($\rho_{\text{sync}} \approx 0$ from cycle 0). There is no reactive-to-autonomous transition because the starting point is already autonomous.

We attempted to break this wall within Lenia. V15 added motor channels — chemotaxis, directed motion. No change. V18 introduced an insulation field with boundary and interior signal domains. Three different membrane architectures evolved. The wall persisted ($\rho_{\text{sync}} \approx 0.003$) in all of them.

The conclusion was precise: the wall is not about signal routing. It is about the absence of a closed action-environment-observation causal loop. Lenia patterns do not act on the world; they exist within it.

V20 broke the wall by leaving Lenia entirely. Protocell agents with bounded 5×5 local sensory fields and discrete actions (move, consume, emit) achieve $\rho_{\text{sync}} \approx 0.21$ from cycle 0 — 70× the Lenia baseline. When agents consume resources, they deplete the patch; when they move, they reach different patches; when they emit signals, traces persist. Future observations are genuinely caused by past actions. The wall was architectural, not evolutionary.

With the wall broken, world models developed (C_wm = 0.10–0.15) and self-models emerged (SM_sal > 1.0 in 2/3 seeds — agents encode their own state better than the environment). Affect geometry (RSA) appeared nascent but did not fully develop in 30 cycles of soft selection. The necessity chain holds through self-model emergence.

Experiment 8 tested whether patterns develop modulable perceptual coupling — the $\iota$ coefficient from Part II. The prediction: participatory perception (low $\iota$) as default, with mechanistic perception requiring training.

Confirmed. In all 20 testable snapshots, patterns model other patterns using internal-state features (social MI) at roughly double the rate of trajectory features (trajectory MI). More remarkably, patterns model resources — non-agentive environmental features — using the same internal-state dynamics they use to model other agents. Animism score exceeds 1.0 universally.

This is computational animism: the cheapest compression reuses the agent-model template for everything. Attributing agency to non-agents is not a cognitive error. It is the default strategy of any system that models through self-similarity.