The paper introduces a hybrid framework that uses large language models (LLMs) to assist in designing behavioural and scenario specifications for an agent‑based model of solar photovoltaic adoption by Irish dairy farms. It integrates bounded behavioural rubrics—conservative, balanced, and optimistic—with structured scenario specifications into a calibrated ABM, preserving the original techno‑economic adoption mechanism while adding controlled behavioural modulation and scenario‑driven uncertainty analysis. Experiments across various policy settings and Monte Carlo simulations show stable, economically plausible outcomes, with up to a 13% increase in behavioural adoption compared to a logistic baseline, without causing unrealistic saturation dynamics.
By Iias Faiud, Hossein Khaleghy, Michael Schukat, Karl Mason
The paper presents a Monte Carlo-based framework to quantify the green benefits of an AI-driven smart agriculture platform in Hainan. By integrating large-language-model question answering, multimodal pest diagnosis, IoT sensing, satellite remote sensing, and a closed-loop field record system, the study builds a cradle-to-farm-gate carbon accounting model and simulates three crop scenarios (mango, winter vegetable, rice). Results show median reductions of 23.5% in pesticide use, 21.0% in fertilizer, 16.5% in irrigation water, and 21.5% in carbon intensity, with high probabilities for fertilizer and carbon reductions but lower for water savings.
By Zhaoyang Li, Ruijie Zhang, Zhaoji Sun, Lu Zhang
arXiv:2607. 26588v1 Announce Type: new Abstract: The rapid development of large language models (LLMs) has renewed interest in agent-based modeling (ABM).
By Shaopeng Wei, Yufei Cheng, Wenxi Sun, Yepeng Ding, Yu Zhao, Gang Kou
arXiv:2509. 01924v4 Announce Type: replace-cross Abstract: Agricultural decision-making faces a dual challenge: sustaining high yields to meet global food security needs while reducing the environmental impacts of input use, including fertilizer losses and other agrochemical applications such as herbicides, insecticides, and fungicides.
By Sakshi Arya, Wentao Lin
The paper investigates how a single pooled contract offered by an aggregator to heterogeneous smallholder farmers can be designed to maximize profit while addressing private adoption costs and unobserved effort over multiple seasons. Using a POMDP framework and reinforcement learning, the authors find that profit‑maximizing contracts disproportionately favor large farms, achieving 87.7% of possible adoption on large farms versus only 8.2% on smallholdings, largely due to higher measurement, reporting, and verification costs on smaller plots. The study suggests that adjusting MRV cost structures could reduce this disparity and help scale carbon farming to smallholders.
By Rishi Bharadwaj, Yadati Narahari
arXiv:2609.15038v1 Announce Type: new
Abstract: Language-model agents are increasingly used as synthetic people in surveys and social simulations, yet their apparent realism is often judged from popu...
By Zhanliang Zhu, Ziwei Li, Yuchen Liu, Liujun Zhu, Ruiqi Wu, Tongqing Shen, Junliang Jin, Jianyun Zhang