The calibration of simulators for complex social systems aims to identify the optimal parameter that drives the output of the simulator best matching the target data observed from the system. As many social systems may change internally over time, calibration naturally becomes an online task, requiring parameters to be updated continuously to maintain the simulator's fidelity. In this work, the online setting is first formulated as a dynamic optimization problem (DOP), requiring the search for a sequence of optimal parameters that fit the simulator to real system changes. However, in contrast to traditional DOP formulations, online calibration explicitly incorporates the observational data as the driver of environmental dynamics. Due to this fundamental difference, existing Evolutionary Dynamic Optimization (EDO) methods, despite being extensively studied for black-box DOPs, are ill-equipped to handle such a scenario. As a result, online calibration problems constitute a new set of challenging DOPs. Here, we propose to explicitly learn the posterior distributions of the parameters and the observational data, thereby facilitating both change detection and environmental adaptation of existing EDOs for this scenario. We thus present a pretrained posterior model for implementation, and fine-tune it during the optimization. Extensive tests on both economic and financial simulators verify that the posterior distribution strongly promotes EDOs in such DOPs widely existed in social science.

翻译：复杂社会系统模拟器的校准旨在识别最优参数，使模拟器输出与从系统观测到的目标数据最佳匹配。由于许多社会系统可能随时间发生内部变化，校准自然成为一项在线任务，需要持续更新参数以维持模拟器的保真度。本文首次将在线设置形式化为动态优化问题，要求搜索一系列最优参数以使模拟器适应真实系统的变化。然而，与传统动态优化问题表述不同，在线校准明确将观测数据作为环境动态的驱动因素。基于这一根本差异，现有进化动态优化方法虽在针对黑箱动态优化问题中已得到广泛研究，却难以有效处理此类场景。因此，在线校准问题构成了一类新的具有挑战性的动态优化问题。本研究提出显式学习参数与观测数据的后验分布，从而促进现有进化动态优化方法在此场景下的变化检测与环境适应。我们据此提出一种预训练后验模型进行实现，并在优化过程中对其进行微调。在经济与金融模拟器上的大量测试验证了后验分布在社会科学领域广泛存在的此类动态优化问题中对进化动态优化方法的显著促进作用。