Towards Agentic Agent-based Models: Feasibility, Performance, and Statistical Model Checking
Stefano Blando, Emmanuele Guerrazzi, Riccardo Porcedda, Giuseppe Squillace, Max Tschaikowski, Andrea Vandin
Cite this article generated from arXiv
@misc{blando2026agentic,
title = {Towards Agentic Agent-based Models: Feasibility, Performance, and Statistical Model Checking},
author = {Blando, Stefano and Guerrazzi, Emmanuele and Porcedda, Riccardo and Squillace, Giuseppe and Tschaikowski, Max and Vandin, Andrea},
year = {2026},
eprint = {2607.17948},
archivePrefix = {arXiv},
primaryClass = {econ.GN},
note = {Submitted to AISoLA 2026 (arXiv:2607.17948)}
}Abstract
Integrating Large Language Models (LLMs) into traditional Agent-Based Models (ABMs) allows agents to leverage tool calls, semantic reasoning, and adaptive decision-making. In this paper, we evaluate the feasibility, performance, and emergent behavior of agentic ABMs using Mesa and statistical model checking via MultiVeStA. By conducting systematic statistical specification testing across agentic parameter spaces, we quantify the impact of LLM decision-making on macroeconomic and social simulation dynamics.