[Submitted on 15 Oct 2026]
Title:Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model
View a PDF of the paper titled Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model, by Benjamin Pianet and 6 other authors
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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. 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.
Comments: Accepted to PRIMA 2026
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2610.22420 [cs.AI]
(or arXiv:2610.22420v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.22420
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Paul Saves [view email] [v1] Thu, 15 Oct 2026 02:27:38 UTC (1,141 KB)
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