Adaptive resource allocation for effective and efficient LLM social survey simulation

Large Language Models (LLMs) enable scalable social survey simulation, yet existing pipelines typically assign the same strong general-purpose model and a fixed, often large, respondent history to every respondent–question request. This uniform design overlooks three properties of survey simulation. First, a stronger general-purpose model may rely more on its own knowledge and deviate from respondent-specific evidence, while also costing more. Second, more history can help when evidence is insufficient, but irrelevant responses may introduce noise and lengthen the input. Third, changing the history budget can change the preferred model, while changing the model can change the preferred history budget. These considerations call for joint request-level allocation under an explicit accuracy–cost trade-off. We propose E2Sim, an adaptive resource allocation framework that treats each model–history configuration as a joint request-level decision. Conditioned on the respondent persona, ranked response history, and target question, a lightweight policy predicts the accuracy–cost score of every configuration and selects the most suitable one. To reduce sensitivity to incomplete or variable histories, we apply respondent-history drop-and-swap augmentation; to handle differences in allocation ambiguity, we use a margin-based curriculum that progresses from clearly separated configurations to harder cases. Experiments on four real-world social-survey datasets, multiple model pools, and different history-budget spaces demonstrate improvements over oracle best-fixed configurations, with gains of up to 6.3 percentage points in Accuracy and reductions of up to 55.3% in cost. Our code is available at https://anonymous.4open.science/r/E2Sim-DE66.