arXiv cs.CY· Muyao Zhong, Peng Yang·· 11 小时前AI 评分20
摊销优化方法加速随机社会模拟器校准
Amortized Optimization for Calibrating Stochastic Social Simulators
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研究者提出一种摊销优化器，利用预先收集的模拟数据学习如何选择参数，使模拟结果与观测一致，拟合后无需再调用模拟器即可将新观测映射为可行参数。在真实金融数据的回溯对比中，该方法比 10 分钟高斯过程贝叶斯优化搜索快约 $1.07\times10^{7}$ 倍，平均校准误差高 3.06%。实验在四个模拟器上比较了校准精度与查询时间，局部校正相比经验选择和代理模型选择可将查询时间减半以上。
正文 · 原文
Abstract:Stochastic simulators need suitable parameter values to reproduce observed behaviour in complex systems. Searching for these values through repeated simulations makes frequent recalibration computationally expensive. We develop an amortized optimizer that uses simulations collected in advance to learn how to choose parameters that make simulated outcomes agree with observations. Once fitted, it maps new observations to feasible parameters without further simulator calls. The method retains alternative parameter settings that can explain similar observations and compares their responses, avoiding the conflicting labels that arise when training a single parameter regressor. Historical observations also provide supervision by showing which simulated responses agree with the data, even when the underlying parameters are unknown. Experiments across four simulators compare calibration accuracy and query time. Dedicated cost analyses quantify traffic preparation and the recorded financial reuse this http URL new traffic targets, local correction more than halves query time relative to empirical and surrogate selection; a learned inverse is faster still, with similar mean error. In a retrospective comparison on real financial data, our method returns parameters about $1.07\times10^{7}$ times faster than a 10-minute Gaussian-process Bayesian optimization search, with mean calibration error 3.06% higher. Reusing simulation work across observations supports repeated parameter updates when the available time is too short for a new search.
| Comments: | This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible |
| Subjects: | Computational Engineering, Finance, and Science (cs.CE); Computers and Society (cs.CY) |
| Cite as: | arXiv:2611.06623 [cs.CE] |
| (or arXiv:2611.06623v1 [cs.CE] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06623 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Muyao Zhong [view email]
[v1]
Wed, 4 Nov 2026 15:08:28 UTC (326 KB)
来源：arXiv cs.CY · arxiv.org