Internal vs. External: Comparing Deliberation and Evolution for Multi-Agent Constitutional Design

Multi-agent AI systems need behavioral constitutions, but it is unresolved whether such rules should emerge internally through agent self-governance or be discovered externally through optimization. We present the first controlled comparison of internal deliberation and external evolution across three social environments: a coordination grid-world, an iterated public goods game, and a bilateral trading market. Across 180 simulation runs, evolution significantly outperforms deliberation in collective-action settings ($p < 0.01$), while neither method improves outcomes in bilateral trading. A multiplier ablation reveals that evolution’s advantage inverts when incentives shift: at pool multiplier $m = 0.75$ the evolved constitution forces value-destroying cooperation and becomes the worst-performing method. Notably, no deliberation run across thirty trials ever proposed punishment --- the canonical cooperation-sustaining mechanism evolution reliably discovers --- suggesting external optimization wins on peaks while internal self-governance trades peaks for structural responsiveness.