Economic-Informed Persona Generation for Behaviorally Grounded LLM Populations

Large language models are increasingly used as persona-conditioned user simulators, yet a plausible persona prompt is not a behavioral specification: the same text can induce different choice patterns across simulators, and matching observed behavior may not identify responses to interventions. We introduce Economically-Informed Persona Generators (EIPG), a framework that calibrates persona generators from discrepancies between simulated and observed behavior rather than from persona labels. In a controlled benchmark, weak intervention coverage can improve calibration while worsening unseen counterfactuals; richer interventions make the calibration signal more identifying and improve frozen test performance. For natural-language personas, an anchored parameterization preserves authored prompts while exposing sparse, interpretable preference adjustments. Across four local LLMs from three model families, useful corrections are model-dependent, while structured economic search transfers more reliably than more flexible LLM- or text-evolution search in our finite-budget experiments. On public discrete-choice data, calibration improves an initially misspecified synthetic population on held-out human choices, although direct human estimation remains substantially stronger on the primary held-out choice metrics. EIPG therefore reframes persona grounding as an identification problem: the goal is not merely to write plausible personas, but to construct synthetic populations whose behavior remains credible under interventions.