Learning to Share: Query-Efficient Reconstruction of LLM-Simulated Population Responses

Simulating how heterogeneous populations respond to alternative policies with large language models (LLMs) requires evaluating a costly Cartesian product of personas and interventions. We introduce the new zero-history task of population-response reconstruction: given n personas, m policies, and a budget of B ≪ nm teacher queries, the goal is to reconstruct the response distribution for every persona–policy pair. We construct a hierarchical benchmark of 1,024 World Values Survey personas and 600 COFOG-based policies—614,400 pairs per teacher—spanning domains, families, and implementation variants. Diagnostics across four teacher models yield two findings: response sharing is compact but policy-conditioned, and informative queries derive much of their value from improving unobserved pairs. We introduce SCOPE (Shared-response Calibration for Online Population Emulation), a closed-loop framework that calibrates response sharing across personas and policies from queried answers, then uses the calibrated structure to select queries for population-wide reconstruction. Across four teachers and all evaluated query budgets, SCOPE achieves the lowest three-seed mean full-pool error at every reconstruction level. Code, benchmark data, and complete results are available at https://anonymous.4open.science/r/scope-F646/.