When Learned Action Rules Matter: A Matched-Seed Ablation in an Agent-Based Spatial Ecology

Whether learned cognition can affect evolutionary outcomes remains a long-standing question. This study addresses a narrower mechanism: whether a model-based planner benefits from learned rules that explicitly condition on the action just taken. The testbed is a spatial artificial ecology with plants, shelters, a predator, reproduction, and a day/night cycle. Five rule-use arms are evaluated on matched simulation seeds. At age 200, agents switch to a weaker learned-lite planner that relies more strongly on learned rule predictions. The pre-specified hypothesis is that access to filtered action-conditioned rules improves outcomes relative to an otherwise identical no-rule-policy baseline, in which rules are still induced and stored but are not used for action selection. In thirty paired replicates under the default reproductive gates, the action-conditioned arm outperforms the no-rule baseline on all four pre-specified primary endpoints. The strongest effect is behavioural: the action arm produces 91.4 additional successful post-switch eating events per run (dz=1.56, 93.3% paired win rate, p<10−4). It also produces 10 additional crystallized clean-causal rules per replicate (dz=0.58, pt=0.0034). All four primary paired-tp-values remain significant after Bonferroni correction across the four-endpoint family. A diagnostic check shows that omitting reproductive cooldown from the planner’s rollout reverses the arm ordering on the same paired seeds; reinstating cooldown recovers the reported result. Two exploratory checks delimit the claim: broad unfiltered rule access can impair foraging, and a means–ends extension shifts behaviour toward reproduction without producing a robust whole-life fitness gain. Within this simulation, access to action-conditioned rules has a measurable effect on post-switch behaviour that is distinct from passive environmental prediction and from clean-crystallized rules alone.