Agent-Based Modeling

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18 papers in the last four weeks, against 2 the four weeks before. 0.2% of all new papers.

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Generating synthetic electronic health record data using agent-based models to evaluate machine learning robustness under mass casualty incidents
May 11, 2026Roben Delos Reyes, Daniel Capurro, Nicholas GeardDistribution Shift Robustness Synthetic Data Generation
School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia · Department of Medicine, The University of Melbourne, Parkville, Victoria, Australia
GASim: A Graph-Accelerated Hybrid Framework for Social Simulation
May 8, 2026Xuan Zhou, Yanhui Sun, Hantao Yao +3Multi-Agent LLM Systems Human Behavior Simulation
University of Science and Technology of China · BASIS International School Park Lane Harbour
comokit4py : a python package to ease COMOKIT agent based model simulation integration into a high performance computing workflow
May 6, 2026Arthur Brugière, Kévin ChapuisHigh-Performance Computing Agent-Based Modeling
UMMISCO UMI 209, SU/IRD Bondy, France · Thuyloi University 175 Tay Son, Dong Da, Hanoi, Vietnam · ESPACE-DEV, Univ Montpellier, IRD, Univ Antilles, Univ Guyane, Univ R´eunion Montpellier, France
Agent-Based Modeling of Low-Emission Fertilizer Adoption for Dairy Farm Decarbonisation using Empirical Farm Data
May 5, 2026Surya Jayakumar, Kieran Sullivan, John McLaughlin +2Agent-Based Modeling
1Walton Institute, South East Technological University, Waterford, Ireland
Koopman Representations for Early Outbreak Warning and Minimal Counterfactual Intervention in Multi-Agent Epidemic Simulations
May 3, 2026Florin LeonEpidemic Forecasting Agent-Based Modeling
Department of Computers Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iași, Romania
The $\textit{Silicon Society}$ Cookbook: Design Space of LLM-based Social Simulations
Apr 30, 2026Aurélien Bück-Kaeffer, Sneheel Sarangi, Maximilian Puelma Touzel +3Multi-Agent LLM Systems Human Behavior Simulation
McGill University · Mila - Quebec Artificial Intelligence Institute · Ubisoft La Forge +2
A Grid-Aware Agent-Based Model for Analyzing Electric Vehicle Charging Systems
Apr 30, 2026Khalil Al-Rahman Youssefi, Marija Gojkovic, Walter Stefanutti +2Electric Vehicle Charging Agent-Based Modeling
Lakeside Labs, Lakeside Park B04b, Klagenfurt, Austria · Alpen-Adria-Universität, Universitätsstraße 65–67, Klagenfurt, Austria · Silicon Austria Labs, Power Electronics division, High Tech Campus Villach - Europastraße 12, Austria
TEAM-SimHRA: A Team-Based Simulation Framework for Human Reliability Analysis Using Multi-Agent Large Language Models
Apr 21, 2026Xingyu Xiao, Jiejuan Tong, Jingang Liang +1Human Behavior Simulation Agent-Based Modeling
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China · National Key Laboratory of Human Factors Engineering, Beijing, 100094, China
EvoMarket: A High-Fidelity and Scalable Financial Market Simulator
Apr 20, 2026Muyao Zhong, Zhenhua Yang, Yuxiang Liu +2Quantitative Finance Model Calibration
Department of Electronic Information, Harbin Institute of Technology, Harbin, 150001, China · Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China · Zhongguancun Academy, Beijing, 100094, China +1
Collective Opinion Dynamics in Structured LLM Populations
Apr 13, 2026Erica Cau, Andrea Failla, Giulio RossettiMulti-Agent LLM Systems Opinion Dynamics
Department of Computer Science, University of Pisa · ISTI-CNR
Stabilising Generative Models of Attitude Change
Apr 2, 2026Jayd Matyas, William A. Cunningham, Alexander Sasha Vezhnevets +3Computational Cognitive Modeling Human Behavior Simulation
Google DeepMind · University of Toronto · Caltech +1
Subjective-Graph LLM Agents for Simulating Uncertainty in Classroom Social Perception
Mar 21, 2026Jinming Yang, Xinyu Jiang, Xinshan Jiao +1Multi-Agent LLM Systems Opinion Dynamics
Complex Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China · Department of Computer Science, City University of Hong Kong, Hong Kong · School of Health and Medical Technology, Chengdu Neusoft University, Chengdu,2026 China
From the Fluency Fallacy to the Micro-to-Macro Validity Gap: Opportunities and Pitfalls of LLMs in Social Simulation
Jul 25, 2025Patrick Taillandier, Jean Daniel Zucker, Arnaud Grignard +4Human Behavior Simulation Computational Social Science
UR MIAT, University of Toulouse, INRAE, Castanet-Tolosan, France · UMI UMMISCO, IRD, Sorbonne University, Bondy, France · UMR IRIT, University Toulouse Capitole, Toulouse, France +2
Modeling Earth-Scale Human-Like Societies with One Billion Agents
Jun 7, 2025Haoxiang Guan, Jiyan He, Liyang Fan +10Human Behavior Simulation Computational Social Science
University of Science and Technology of China, Hefei, China. · Zhongguancun Academy, Beijing, China. · Zhongguancun Institute of Artificial Intelligence, Beijing, China. +4
AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations
Apr 13, 2025Jenny Ma, Riya Sahni, Karthik Sreedhar +1Multi-Agent LLM Systems Human Behavior Simulation
Columbia University New York City, USA
A Hybrid ABM-PDE Framework for Real-World Infectious Disease Simulations
Apr 11, 2025Kristina Kehrer, Tim O. F. ConradDynamical Systems Agent-Based Modeling
Zuse Institute Berlin Berlin, 14195
FREIDA: A Framework for developing quantitative agent based models based on qualitative expert knowledge
Jul 21, 2023Frederike Oetker, Vittorio Nespeca, Rick QuaxAgent-Based Modeling
Computational Science Lab, Institute of Informatics, University of Amsterdam, Amsterdam, Netherlands · Institute for Advanced Study, University of Amsterdam, Amsterdam, Netherlands
Simulation of Crowd Egress with Environmental Stressors
Jun 3, 2022Peng Wang, Xiaoda Wang, Peter Luh +4Pedestrian Behavior Modeling Human Behavior Simulation
Department of Electrical and Computer Engineering, University of Connecticut, Storrs
The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies
Date pendingJiaxu Zhou, Jen-tse Huang, Xuhui Zhou +5LLM Evaluation LLM Auditing
CUHK · JHU · CMU +3

May 11, 2026, cs.LGJ/K move · Enter open · S save

Generating synthetic electronic health record data using agent-based models to evaluate machine learning robustness under mass casualty incidents

Roben Delos Reyes, Daniel Capurro, Nicholas Geard

School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia · Department of Medicine, The University of Melbourne, Parkville, Victoria, Australia

Distribution Shift Robustness Synthetic Data Generation Agent-Based Modeling Synthetic Data Evaluation

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ML models in healthcare are typically evaluated using curated real-world EHR data. A key limitation of such evaluations is that they may fail to assess the robustness of ML models to changes in the data at deployment, which is a common issue because EHR data used for ML model development cannot capture all such changes. Mass casualty incidents (MCIs) caused by disasters are critical instances where this will be an issue, as they induce rare, uncertain, and novel changes to routine system conditions. Because real-world EHR data from MCIs are often limited or unavailable, assessing ML robustness under such conditions before deployment remains challenging. Here, we propose an agent-based modelling approach for generating synthetic EHR data to evaluate the robustness of ML models under MCI scenarios. We use real-world EHR data to develop and calibrate an agent-based model (ABM) of an emergency department (ED) that explicitly models patient arrivals, resource capacity, and clinical workflow. By changing these system conditions to reflect plausible MCI scenarios, the ED model generates synthetic versions of the real-world EHR data that exhibit shifts in system behaviour. Using these synthetic data, we test ML models for predicting length of stay. We observed consistent declines in recall under MCI conditions relative to baseline system conditions, resulting in an increase in the number of patients with prolonged length of stay that were missed by the ML models. These results highlight the impact of changes in system conditions on patient outcomes, EHR data, and ML model performance. Our work establishes ABM-based synthetic EHR data generation as a proactive and systematic approach for evaluating the robustness of ML models under MCI or other system conditions not captured in real-world EHR data, supporting the safer and more effective deployment of ML models in healthcare systems.

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