Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model

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Computer Science > Artificial Intelligence arXiv:2610.22420 (cs) [Submitted on 15 Oct 2026] Title: Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model Authors: Benjamin Pianet, Baptiste Lesquoy, Paul Saves, Benoit Gaudou, Jean Villerd, Nicolas Verstaevel, Olivier Therond.

计算机科学 > 人工智能 arXiv:2610.22420 (cs) [提交于 2026 年 10 月 15 日] 标题：基于主体的农业生态模型中决策规则的敏感性分析作者：Benjamin Pianet, Baptiste Lesquoy, Paul Saves, Benoit Gaudou, Jean Villerd, Nicolas Verstaevel, Olivier Therond。

Abstract: 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.

摘要：基于主体的模型非常适合用于决策支持的农业生态系统建模，然而要将模拟结果转化为可操作的见解，需要识别出哪些决策真正驱动了这些行为。在此背景下，敏感性分析遇到了许多复杂系统的一个结构性特征：参数并非一个平坦的向量，而是一个决策树，其中许多参数仅在更高层级的选择激活它们时才会出现。

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.

传统的全局敏感性分析预设了固定笛卡尔积上的独立输入，在这样的分支空间上是不适定的，且在构建上存在偏差。我们转而将决策结构视为一类建模对象。利用先进的代理建模技术，我们将高分辨率农业生态建模平台 MAELIA 的作物管理路径编码为一个包含分类变量和数值变量的分层设计空间。

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.

我们提出了一种专门用于分析此类分层空间的新颖方法——随机森林监督的 HSIC-ANOVA，它能够在树结构上原生排列主效应和交互作用，同时将罕见激活和始终激活的决策置于同等地位。我们展示了当该方法应用于氮淋失、土壤有机碳变化和产量预测等模拟输出时，如何提供一种可解释的、面向决策的解读。