Testing, not presuming, adequacy: calibrating generative social simulators against emergent network structure
Read the original on arXiv AI →The paper introduces an adequacy‑aware calibration protocol for generative social simulators that integrates amortized posterior estimation, synthetic identifiability assessment, matched‑sample‑size adequacy checks, diagnosis‑guided repair, and held‑out audits. Applied to a second‑hand luxury resale market, the protocol reveals that behavioural parameters are recoverable but calibration is approximate and overconfident for one parameter, and that the simulator’s reachability reference is violated in every cell, particularly in mean purchased tier. The repair improves two of four cells but fails to restore full adequacy, and a held‑out audit uncovers a buyer‑breadth‑dispersion miss not detected earlier; profile‑source ablation shows language‑model‑derived persona profiles outperform a flat rule baseline, though within‑category brand relabelling has no consistent effect. whyItMatters":"The study demonstrates that without an adequacy check, generative social models may appear valid descriptively yet fail to capture key emergent network structures, highlighting the need for rigorous calibration protocols in social simulation research."
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