[论文] Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecolo...

论文概要

研究领域: ML 作者: Benjamin Pianet, Baptiste Lesquoy, Paul Saves, Benoit Gaudou, Jean Villerd, Nicolas Verstaevel, Olivier Therond 发布时间: 2026-10-19 arXiv: 2610.22420

中文摘要

基于智能体（agent-based）的模型很适合为农业生态系统建模以辅助决策，但把仿真变成可行动洞见，需要先弄清哪些决策真正驱动行为。此类系统的敏感性分析撞上一个结构性特征：参数不是平坦向量，而是一棵决策树——许多参数只在高层选择激活后才存在。经典全局敏感性分析假设固定笛卡尔积上的独立输入，在这种分支空间上是病态的、构造上有偏。本文反其道而行，把决策结构当作一等建模对象：用先进的代理建模技术，把高分辨率农业生态建模平台 MAELIA 的作物管理方案编码为含类别与数值变量的层级设计空间，并提出专门面向此类层级空间的方法——随机森林监督的 HSIC-ANOVA，直接在树上对主效应与交互排序，让「很少激活」与「总是激活」的决策站在同一起跑线。应用于氮淋溶、土壤有机碳变化与产量预测等仿真输出时，该方法能给出可解释、面向决策的解读。

原文摘要

Agent-based models are well-suited for modelling agro-ecological systems for decision-making, yet turning their simulations into actionable insight requires identifying which decisions actually drive the behaviours. In such a context, sensitivity analysis stumbles upon a structural feature of many complex systems: the parameters are not a flat vector but a decision tree in which many parameters appear only when a higher-level choice activates them. Classical global sensitivity analysis presupposes independent inputs over a fixed Cartesian product and is ill-posed on such a branched space and biased by construction. We instead treat the structure of the decisions as a first-class modelling object. Using advanced surrogate modelling techniques, we encode the crop-management itinerary of the high-resolution agro-ecological modelling platform MAELIA as a hierarchical design space featuring both categorical and numerical variables. We propose a novel dedicated method to analyse this kind of hierarchical space, a random-forest-supervised HSIC-ANOVA, which ranks main effects and interactions natively on the tree while placing rarely and always active decisions on an equal footing. We show how, when applied to simulation outputs such as nitrogen leaching, soil organic carbon variation, and yield predictions, it can deliver an interpretable, decision-oriented reading.

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*自动采集于 2026-10-19*

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