Comparison-aware Active Learning for Simulation-Efficient Global Change Analysis

Global change analysis investigates how complex systems respond under alternative socioeconomic, technological, and policy scenarios. It relies on computationally expensive global change models and draws conclusions from both individual scenario outcomes and differences between scenarios. To improve the efficiency of analyzing large numbers of scenarios, surrogate models provide a more efficient alternative to direct model simulation. However, their construction still depends on costly original simulator evaluations for training data, and low average prediction error from these surrogate models does not guarantee sufficient accuracy for individual scenarios or present their differences in scientific analysis. To address these limitations, we formulate simulation-efficient global change analysis as reducing original-model evaluations while maintaining accuracy for both scenario outcomes and scientifically relevant comparisons. Particularly, we propose a comparison-aware active learning method, which first constructs an input-derived comparison graph, and then estimates both outcome- and comparison-level uncertainty with an ensemble of surrogate models. Based on these estimates, a calibrated fallback strategy finally identifies scenarios that may require original-model evaluation. We further propose one acquisition strategy, which can select scenarios according to their expected reduction in future original-model evaluations. Experiments on a public energy dataset and a newly constructed dietary dataset show that our method improves performance over existing approaches, reducing the number of evaluations of the original model by 85.71% and 67.29%, respectively, while maintaining high edge sign accuracies of 96.65% and 90.22%. Overall, our method provides an efficient and reliable solution for surrogate modeling in global change analysis.