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[2509.01924] Non-Linear Model-Based Sequential Decision-Making in Agriculture
Summary
This paper presents a novel approach to sequential decision-making in agriculture using nonlinear model-based algorithms, enhancing resource optimization under uncertainty.
Why It Matters
The research addresses the challenges of making informed agricultural decisions with limited data, promoting sustainable practices. By integrating domain-specific insights into decision-making models, it offers a pathway to improve efficiency and transparency in agricultural management, which is crucial for food security and environmental sustainability.
Key Takeaways
- Proposes nonlinear, model-based bandit algorithms for agriculture.
- Achieves sublinear regret and near-optimal sample complexity.
- Utilizes mechanistic insights to enhance decision-making in resource-constrained settings.
- Demonstrates consistent improvements over traditional linear models.
- Supports sustainable and transparent agricultural practices.
Statistics > Machine Learning arXiv:2509.01924 (stat) [Submitted on 2 Sep 2025 (v1), last revised 21 Feb 2026 (this version, v3)] Title:Non-Linear Model-Based Sequential Decision-Making in Agriculture Authors:Sakshi Arya, Wentao Lin View a PDF of the paper titled Non-Linear Model-Based Sequential Decision-Making in Agriculture, by Sakshi Arya and Wentao Lin View PDF Abstract:Sequential decision-making is central to sustainable agricultural management and precision agriculture, where resource inputs must be optimized under uncertainty and over time. However, such decisions must often be made with limited observations, whereas classical bandit and reinforcement learning approaches typically rely on either linear or black-box reward models that may misrepresent domain knowledge or require large amounts of data. We propose a family of \emph{nonlinear, model-based bandit algorithms} that embed domain-specific response curves directly into the exploration-exploitation loop. By coupling (i) principled uncertainty quantification with (ii) closed-form or rapidly computable profit optima, these algorithms achieve sublinear regret and near-optimal sample complexity while preserving interpretability. Theoretical analysis establishes regret and sample complexity bounds, and extensive simulations emulating real-world fertilizer-rate decisions show consistent improvements over both linear and nonparametric baselines (such as linear UCB and $k$-NN UCB) in the low-sample regime, under both...
