Food Information and Competitive Performance of Evolved Behavioral Programs in a Spatial Digital-Evolution Model

Inherited program growth does not by itself establish adaptive function. We developed a spatial digital-evolution model and used neutral inheritance controls followed by frozen-program competition to distinguish sequence growth from the competitive contribution of food information. An initial functional study tested the predominant programs of 90 source populations in 540 competitions. Intact programs generally outperformed information-withheld versions that retained the same per-call scanning cost, and also outperformed the ancestor under the tested conditions. We then independently evolved 90 additional source populations and performed 1,800 further competitions on two fresh maps per source with complementary founder assignments. At baseline, intact-minus-withheld differences were 1.83, 1.99, 1.88 living descendants per founder across scattered, small-patch and large-patch resources. The baseline information-benefit result recurred in all three layouts. Across five assay settings per layout, including one-at-a-time changes in regeneration interval and scanning price, 15/15 pointwise bootstrap intervals were above zero. Sources evolved only under baseline conditions. The assays excluded predators and measured performance over 600 steps, so their results support a conditional functional benefit of usable food information rather than general fitness, increased complexity or ecosystem stability. Independent seeds and parameter sensitivity strengthen internal evidence, while the literature contribution and wider applicability remain bounded. AI use: Generative AI assisted with design, implementation, analysis, model verification, literature searches and writing. This is an author-review draft, not a peer-reviewed publication. The supplementary archive covers independent validation v0.7; earlier study data are retained separately.