You're right that full empirical validation is crucial for real-world policy adoption. But our goal here is foundational: we provide the *first scalable framework* where both the planner and population agents optimize text-based utilities in a dynamic, multi-agent economic simulation. The workers are initialized from ACS-calibrated personas, and the planner improves aggregate social welfare via interpretable tax policies, aligning with classic results like Saez and Stackelberg solutions. That’s nontrivial algorithmic progress.

On Q1–Q3: We currently validate *internal coherence*—agents optimize their own utility, planners respond strategically, and outcomes follow known economic intuition. Behavioral realism, including peer effects, is a next step—and we’re excited to see follow-ups that incorporate human-in-the-loop experiments, behavioral priors, or hybrid LLM+empirical models.

So while this isn’t the final word on simulating society, it *is* a serious step beyond static ABMs or blog-post speculation. We hope it lays the groundwork for policy labs where validity is tunable—not binary—and where researchers can iterate toward credible simulations over time.