用自动微分让复杂模型参数校准更快更准
Automatic Differentiation of Agent-Based Models
- 把自动微分技术引入代理模型，直接计算模拟器梯度
- 在3个经典模型上实现参数校准速度大幅提升
- 适合做复杂系统建模与敏感性分析的研究者
代理模型（ABMs）通过个体代理间的底层互动来模拟复杂系统，常用于流行病或金融市场等场景，但因涉及成千上万代理而计算成本高，且需校准大量自由参数，严重制约其广泛应用。本文证明，自动微分（AD）技术可有效缓解此类计算负担。通过将AD应用于ABMs，模拟器的梯度可直接获得，极大便利了参数校准和敏感性分析等任务。具体而言，我们展示了如何利用变分推断（VI）进行高效参数校准。实验表明，在Axtell的企业模型、Sugarscape及SIR流行病模型三个代表性模型上，该方法均实现了显著性能提升与计算节省。本方法显著提升了ABMs在研究复杂系统时的实用性与可扩展性。
原文摘要 · Abstract (English)
Agent-based models (ABMs) simulate complex systems by capturing the bottom-up interactions of individual agents comprising the system. Many complex systems of interest, such as epidemics or financial markets, involve thousands or even millions of agents. Consequently, ABMs often become computationally demanding and rely on the calibration of numerous free parameters, which has significantly hindered their widespread adoption. In this paper, we demonstrate that automatic differentiation (AD) techniques can effectively alleviate these computational burdens. By applying AD to ABMs, the gradients of the simulator become readily available, greatly facilitating essential tasks such as calibration and sensitivity analysis. Specifically, we show how AD enables variational inference (VI) techniques for efficient parameter calibration. Our experiments demonstrate substantial performance improvements and computational savings using VI on three prominent ABMs: Axtell's model of firms; Sugarscape; and the SIR epidemiological model. Our approach thus significantly enhances the practicality and scalability of ABMs for studying complex systems.
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