Agent-Based Modeling

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Overview

Agent-based modeling represents people, organizations, vehicles, or other entities as individual agents whose decisions and interactions generate system-level outcomes. Researchers use rule-based and game-theoretic models, networks, reinforcement learning, and increasingly large language models, sometimes combining these with equations or continuous simulations. Applications include epidemics, markets, traffic, evacuation, public policy, and social opinion, where simulations can test mechanisms and compare interventions that are difficult to study directly. A central challenge is establishing that model behavior is empirically credible and robust: richer, larger simulations can be harder to validate, and fluent language-model agents do not by themselves guarantee realistic collective behavior.

Latest papers

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160 papers17 papers in the last four weeks, up 6% on the four weeks before. 0.1% of all new papers.

Apr 14, 2026

How memory can affect collective and cooperative behaviors in an LLM-Based Social Particle Swarm

This study examines how memory shapes the collective and cooperative dynamics of Large Language Model (LLM) agents in a multi-agent system. To this end, we extend the Social Particle Swarm (SPS) model, in which agents move in a two-dimensional space and play the Prisoner's Dilemma with neighboring agents, by replacing its rule-based agents with LLM agents endowed with Big Five personality scores and varying memory lengths. Using Gemini 2.0 Flash, we find that memory length is a critical parameter governing collective behavior: even a minimal memory drastically suppressed cooperation, transitioning the system from stable cooperative clusters through cyclical formation and collapse of clusters to a state of scattered defection as memory length increased. Big Five personality traits correlated with agent behaviors in partial agreement with findings from experiments with human participants, supporting the validity of the model. This effect of memory appeared whether or not personality was assigned. With heterogeneous personalities, individual behavior reflected the assigned traits and cooperation collapsed under long memory, whereas without personality Gemini's cooperative disposition dominated and cooperation was broadly maintained. Sentiment analysis of agents' reasoning texts showed that the model interprets memory increasingly negatively as its length grows, already in the early phase, providing a micro-level account of the suppression of cooperation. These results suggest that how an LLM interprets accumulated memory is a key driver of emergent social behavior in Generative Agent-Based Modeling.

Taisei Hishiki, Takaya Arita, Reiji Suzuki

Graduate School of Informatics, Nagoya University, Nagoya, Japan

Large Language Model-Based Social SimulationEmergent Behavior in Multi-Agent SystemsAgent-Based ModelingMemory-Augmented Language Model AgentsMulti-Agent Collaboration
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Apr 13, 2026

Collective Opinion Dynamics in Structured LLM Populations

Large Language Models are increasingly deployed as interacting agents in settings such as online platforms, recommendation systems, and multi-agent applications. Understanding the collective behaviors that emerge from their interactions is therefore increasingly crucial, especially as these behaviors may shape public opinion and contribute to polarization. In this work, we investigate how network structure and group composition shape the evolution of opinions in populations of LLM agents engaged in multi-round debates. We generate networks with controlled levels of homophily and varying group sizes, and perform ten independent runs per configuration. The results show that LLM agents exhibit patterns that are highly sensitive to network structure, relative group sizes, and to the model itself. We highlight that even within LLM populations, low homophily facilitates convergence by increasing opportunities for cross-group interaction, whereas higher homophily limits cross-group exposure and can preserve distinct opinion states, consistent with previous research. We further show that the choice of LLM substantially affects the resulting dynamics, with different models exhibiting different patterns of opinion updating under comparable network conditions. At the individual level, providing agents with information about their local neighborhood further modulates opinion transitions, revealing substantial differences between models in their sensitivity to local social context. Overall, our findings show that the collective dynamics of LLM populations arise from interactions among model-specific behavior, network structure, population composition, and local social information.

Erica Cau, Andrea Failla, Giulio Rossetti

Department of Computer Science, University of Pisa · ISTI-CNR

Multi-Agent LLM SystemsOpinion DynamicsMulti-Agent DebateAgent-Based Modeling
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Apr 11, 2026

A Dual-Positive Monotone Parameterization for Multi-Segment Bids and a Validity Assessment Framework for Reinforcement Learning Agent-based Simulation of Electricity Markets

Reinforcement learning agent-based simulation (RL-ABS) has become an important tool for electricity market mechanism analysis and evaluation. In the modeling of monotone, bounded, multi-segment stepwise bids, existing methods typically let the policy network first output an unconstrained action and then convert it into a feasible bid curve satisfying monotonicity and boundedness through post-processing mappings such as sorting, clipping, or projection. However, such post-processing mappings often fail to satisfy continuous differentiability, injectivity, and invertibility at boundaries or kinks, thereby causing gradient distortion and leading to spurious convergence in simulation results. Meanwhile, most existing studies conduct mechanism analysis and evaluation mainly on the basis of training-curve convergence, without rigorously assessing the distance between the simulation outcomes and Nash equilibrium, which severely undermines the credibility of the results. To address these issues, this paper proposes...

Zunnan Xu, Zhaoxia Jing, Zhanhua Pan

School of Electric Power Engineering, South China University of Technology, Guangzhou, 510640, Guangdong, China · Department of Engineering, University of Exeter, Exeter EX4 4PY, U.K.

Automated BiddingConstrained RLAgent-Based ModelingMulti-Agent Reinforcement Learning
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Apr 6, 2026

Statistical Model Checking of the Island Model: An Established Economic Agent-Based Model of Endogenous Growth

Agent-based models (ABMs) are increasingly used to study complex economic phenomena such as endogenous growth, but their analysis typically relies on ad-hoc Monte Carlo exercises without formal statistical guarantees. We show how statistical model checking (SMC), and in particular Multi-VeStA, can automate and enrich the analysis of a seminal ABM: the Island Model of Fagiolo and Dosi, which captures the exploration-exploitation trade-off in technological search. We reproduce key stylized facts from the original model with formal confidence intervals, confirm the optimality of moderate exploration rates, and perform a counterfactual sensitivity analysis across returns to scale, skill transfer, and knowledge locality. Using MultiVeStA's built-in Welch's t-test, 6 out of 7 pairwise parameter comparisons yield statistically different growth trajectories, while the exception reveals a saturation effect in knowledge locality. Our results demonstrate that SMC offers a principled, reproducible methodology for the quantitative analysis of agent-based economic models.

Stefano Blando, Giorgio Fagiolo, Daniele Giachini +2

Institute of Economics and L'EMbeDS, Sant'Anna School of Advanced Studies · Institute of Economics and L’EMbeDS, Sant’Anna School of Advanced Studies, Pisa, Italy · Swiss Finance Institute and University of Geneve +1

Statistical Model CheckingAgent-Based ModelingExploration-Exploitation Tradeoff
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Apr 4, 2026

LLM-Agent-based Social Simulation for Attitude Diffusion

This paper introduces discourse_simulator, an open-source framework that combines LLMs with agent-based modelling. It offers a new way to simulate how public attitudes toward immigration change over time in response to salient events like protests, controversies, or policy debates. Large language models (LLMs) are used to generate social media posts, interpret opinions, and model how ideas spread through social networks. Unlike traditional agent-based models that rely on fixed, rule-based opinion updates and cannot generate natural language or consider current events, this approach integrates multidimensional sociological belief structures and real-world event timelines. This framework is wrapped into an open-source Python package that integrates generative agents into a small-world network topology and a live news retrieval system. discourse_sim is purpose-built as a social science research instrument specifically for studying attitude dynamics, polarisation, and belief evolution following real-world critical events. Unlike other LLM Agent Swarm frameworks, which treat the simulations as a prediction black box, discourse_sim treats it as a theory-testing instrument, which is fundamentally a different epistemological stance for studying social science problems. The paper further demonstrates the framework by modelling the Dublin anti-immigration march on April 26, 2025, with N=100 agents over a 15-day simulation. Package link: https://pypi.org/project/discourse-sim/

Deepak John Reji

Department of Sociology, University of Limerick, Limerick, Ireland

Large Language Model-Based Social SimulationComputational Social ScienceHuman Behavior SimulationAgent-Based Modeling
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Apr 3, 2026

From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models

Systematic exploration of Agent-Based Models (ABMs) is challenged by the curse of dimensionality and their inherent stochasticity. We present a multi-stage pipeline integrating the systematic design of experiments with machine learning surrogates. Using a predator-prey case study, our methodology proceeds in two steps. First, an automated model-based screening identifies dominant variables, assesses outcome variability, and segments the parameter space. Second, we train Machine Learning models to map the remaining nonlinear interaction effects. This approach automates the discovery of unstable regions where system outcomes are highly dependent on nonlinear interactions between many variables. Thus, this work provides modelers with a rigorous, hands-off framework for sensitivity analysis and policy testing, even when dealing with high-dimensional stochastic simulators.

Paul Saves, Matthieu Mastio, Nicolas Verstaevel +1

IRIT, Université Toulouse Capitole, Toulouse, France

Sensitivity AnalysisAgent-Based ModelingSurrogate Modeling
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Apr 3, 2026

STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation

Accurate crowd simulation is crucial for public safety management, emergency evacuation planning, and intelligent transportation systems. However, existing methods, which typically model crowds as a collection of independent individual trajectories, are limited in their ability to capture macroscopic physical laws. This microscopic approach often leads to error accumulation and compromises simulation stability. Furthermore, deep learning-driven methods tend to suffer from low inference efficiency and high computational overhead, making them impractical for large-scale, efficient simulations. To address these challenges, we propose the Spatio-Temporal Decoupled Differential Equation Network (STDDN), a novel framework that guides microscopic trajectory prediction with macroscopic physics. We innovatively introduce the continuity equation from fluid dynamics as a strong physical constraint. A Neural Ordinary Differential Equation (Neural ODE) is employed to model the macroscopic density evolution driven by individual movements, thereby physically regularizing the microscopic trajectory prediction model. We design a density-velocity coupled dynamic graph learning module to formulate the derivative of the density field within the Neural ODE, effectively mitigating error accumulation. We also propose a differentiable density mapping module to eliminate discontinuous gradients caused by discretization and introduce a cross-grid detection module to accurately model the impact of individual cross-grid movements on local density changes. The proposed STDDN method has demonstrated significantly superior simulation performance compared to state-of-the-art methods on long-term tasks across four real-world datasets, as well as a major reduction in inference latency.

Zijin Liu, Xu Geng, Wenshuai Xu +3

School of Computer Science and Engineering, Beihang University, China · Beijing Digital Native Digital City Research Center, Beijing, China · School of Software, Beihang University, China

Pedestrian Trajectory PredictionPhysics-Based SimulationAgent-Based ModelingSpatiotemporal GNNs
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Apr 2, 2026

Stabilising Generative Models of Attitude Change

Attitude change - the process by which individuals revise their evaluative stances - has been explained by a set of influential but competing verbal theories. These accounts often function as mechanism sketches: rich in conceptual detail, yet lacking the technical specifications and operational constraints required to run as executable systems. We present a generative actor-based modelling workflow for "rendering" these sketches as runnable actor - environment simulations using the Concordia simulation library. In Concordia, actors operate by predictive pattern completion: an operation on natural language strings that generates a suffix which describes the actor's intended action from a prefix containing memories of their past and observations of the present. We render the theories of cognitive dissonance (Festinger 1957), self-consistency (Aronson 1969), and self-perception (Bem 1972) as distinct decision logics that populate and process the prefix through theory-specific sequences of reasoning steps. We evaluate these implementations across classic psychological experiments. Our implementations generate behavioural patterns consistent with known results from the original empirical literature. However, we find that achieving stable reproduction requires resolving the inherent underdetermination of the verbal accounts and the conflicts between modern linguistic priors and historical experimental assumptions. We document how this manual process of iterative model "stabilisation" surfaces specific operational and socio-ecological dependencies that were largely undocumented in the original verbal accounts. Ultimately, we argue that the manual stabilisation process itself should be regarded as a core part of the methodology functioning to clarify situational and representational commitments needed to generate characteristic effects.

Jayd Matyas, William A. Cunningham, Alexander Sasha Vezhnevets +3

Google DeepMind · University of Toronto · Caltech +1

Computational Cognitive ModelingHuman Behavior SimulationAgent-Based Modeling
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Apr 2, 2026

Optimizing Interventions for Agent-Based Infectious Disease Simulations

Non-pharmaceutical interventions (NPIs) are commonly used tools for controlling infectious disease transmission when pharmaceutical options are unavailable. Yet, identifying effective interventions that minimize societal disruption remains challenging. Agent-based simulation is a popular tool for analyzing the impact of possible interventions in epidemiology. However, automatically optimizing NPIs using agent-based simulations poses a complex problem because, in agent-based epidemiological models, interventions can target individuals based on multiple attributes, affect hierarchical group structures (e.g., schools, workplaces, and families), and be combined arbitrarily, resulting in a very large or even infinite search space. We aim to support decision-makers with our Agent-based Infectious Disease Intervention Optimization System (ADIOS) that optimizes NPIs for infectious disease simulations using Grammar-Guided Genetic Programming (GGGP). The core of ADIOS is a domain-specific language for expressing NPIs in agent-based simulations that structures the intervention search space through a context-free grammar. To make optimization more efficient, the search space can be further reduced by defining constraints that prevent the generation of semantically invalid intervention patterns. Using this constrained language and an interface that enables coupling with agent-based simulations, ADIOS adopts the GGGP approach for simulation-based optimization. Using the German Epidemic Micro-Simulation System (GEMS) as a case study, we demonstrate the potential of our approach to generate optimal interventions for realistic epidemiological models

Anja Wolpers, Johannes Ponge, Adelinde M. Uhrmacher

University of Rostock, Rostock, Germany · University of Münster, Münster, Germany*

Agent-Based Modeling
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Mar 29, 2026

RAINO: Anchoring Agents in Reality, A Systematic Review and Conceptual Framework for Realism in Agent-Based Modelling

Realism is a central yet seemingly under-theorized concept in Agent-Based Modelling. This paper presents a Systematic Literature Review, aiming to identify how realism is currently operationalized and demonstrated. The results show that realism is often poorly defined and lacks a consistent conceptual framework. A wide variety of methods are used to achieve and demonstrate realism, but explanations of whether and why these methods are appropriate for their intended purposes are generally limited. Building on this review, we introduce the Reality Anchor, Input, Output (RAINO) framework. RAINO identifies the key structures used to argue for realism in Agent-Based Models, consisting of Reality Anchors (e.g., empirical data, formal theory, expert knowledge, common-sense expectations) and their application as model Input or Output. RAINO broadens existing perspectives on how realism is framed. It explains why different assessors may evaluate the realism of a model in different ways, and it shows how this broader framing can lead to significantly different approaches to model development.

Loïs Vanhée, Melania Borit

Umeå Universitet; Umeå, Sweden · UiT The Arctic University of Norway; Tromsø, Norway

Agent-Based Modeling
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Mar 26, 2026

Ultra-fast Traffic Nowcasting and Control via Differentiable Agent-based Simulation

Traffic digital twins, which inform policymakers of effective interventions based on large-scale, high-fidelity computational models calibrated to real-world traffic, hold promise for addressing societal challenges in our rapidly urbanizing world. However, conventional fine-grained traffic simulations are non-differentiable and typically rely on inefficient gradient-free optimization, making calibration for real-world applications computationally infeasible. Here we present a differentiable agent-based traffic simulator that enables ultra-fast model calibration, traffic nowcasting, and control on large-scale networks. We develop several differentiable computing techniques for simulating individual vehicle movements, including stochastic decision-making and inter-agent interactions, while ensuring that entire simulation trajectories remain end-to-end differentiable for efficient gradient-based optimization. On the large-scale Chicago road network, with over 10,000 calibration parameters, our model simulates more than one million vehicles at 173 times real-time speed. This ultra-fast simulation, together with efficient gradient-based optimization, enables us to complete model calibration using the previous 30 minutes of traffic data in 455 s, provide a one-hour-ahead traffic nowcast in 21 s, and solve the resulting traffic control problem in 728 s. This yields a full calibration--nowcast--control loop in under 20 minutes, leaving about 40 minutes of lead time for implementing interventions. Our work thus provides a practical computational basis for realizing traffic digital twins.

Fumiyasu Makinoshima, Yuya Yamaguchi, Eigo Segawa +2

Fujitsu Limited, Kawasaki, Japan · Fujitsu Research of America, Pittsburgh, PA, USA · Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA +1

Intelligent Transportation SystemsAgent-Based ModelingDigital TwinsTraffic ForecastingModel CalibrationDifferentiable Physics
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Mar 25, 2026

EnvSocial-Diff: A Diffusion-Based Crowd Simulation Model with Environmental Conditioning and Individual-Group Interaction

Modeling realistic pedestrian trajectories requires accounting for both social interactions and environmental context, yet most existing approaches largely emphasize social dynamics. We propose EnvSocial-Diff: a diffusion-based crowd simulation model informed by social physics and augmented with environmental conditioning and individual--group interaction. Our structured environmental conditioning module explicitly encodes obstacles, objects of interest, and lighting levels, providing interpretable signals that capture scene constraints and attractors. In parallel, the individual--group interaction module goes beyond individual-level modeling by capturing both fine-grained interpersonal relations and group-level conformity through a graph-based design. Experiments on multiple benchmark datasets demonstrate that EnvSocial-Diff outperforms the latest state-of-the-art methods, underscoring the importance of explicit environmental conditioning and multi-level social interaction for realistic crowd simulation. Code is here: https://github.com/zqyq/EnvSocial-Diff.

Bingxue Zhao, Qi Zhang, Hui Huang

VCC, College of Computer Science and Software Engineering, Shenzhen University

Pedestrian Behavior ModelingDiffusion Models for Motion GenerationAgent-Based Modeling
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Mar 24, 2026

Separating Diagnosis from Control: Auditable Policy Adaptation in Agent-Based Simulations with LLM-Based Diagnostics

Mitigating elderly loneliness requires policy interventions that achieve both adaptability and auditability. Existing methods struggle to reconcile these objectives: traditional agent-based models suffer from static rigidity, while direct large language model (LLM) controllers lack essential traceability. This work proposes a three-layer framework that separates diagnosis from control to achieve both properties simultaneously. LLMs operate strictly as diagnostic instruments that assess population state and generate structured risk evaluations, while deterministic formulas with explicit bounds translate these assessments into traceable parameter updates. This separation ensures that every policy decision can be attributed to inspectable rules while maintaining adaptive response to emergent needs. We validate the framework through systematic ablation across five experimental conditions in elderly care simulation. Results demonstrate that explicit control rules outperform end-to-end black-box LLM approaches by 11.7% while preserving full auditability, confirming that transparency need not compromise adaptive performance.

Shaoxin Zhong, Yuchen Su, Michael Witbrock

University of Auckland, Auckland, New Zealand

Agent-Based Modeling
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Mar 22, 2026

Personality-Driven Student Agent-Based Modeling in Mathematics Education: How Well Do Student Agents Align with Human Learners?

It is crucial to explore the impact of different teaching methods on student learning in educational research. However, real-person experiments face significant ethical constraints, and we cannot conduct repeated teaching experiments on the same student. LLM-based generative agents offer a promising avenue for simulating student behavior. Before large-scale experiments, a fundamental question must be addressed: are student agents truly credible, and can they faithfully simulate human learning? In this study, we built a Big Five Personality-based student agent model with a full pipeline of student-teacher interaction, self-study, and examination. To evaluate behavioral fidelity, we collected 13 empirical studies on Big Five traits and learning, and distilled them into 14 criteria. We found that the 71.4% of the student agents' behavior was aligned with human learners.

Bushi Xiao, Qian Shen

University of Florida

Mathematics EducationHuman Behavior SimulationAgent-Based ModelingLLM Agent EvaluationPersonality Modeling in Language Models
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Mar 22, 2026

The AI Scientific Community: Agentic Virtual Lab Swarms

In this short note we propose using agentic swarms of virtual labs as a model of an AI Science Community. In this paradigm, each particle in the swarm represents a complete virtual laboratory instance, enabling collective scientific exploration that mirrors real-world research communities. The framework leverages the inherent properties of swarm intelligence - decentralized coordination, balanced exploration-exploitation trade-offs, and emergent collective behavior - to simulate the behavior of a scientific community and potentially accelerate scientific discovery. We discuss architectural considerations, inter-laboratory communication and influence mechanisms including citation-analogous voting systems, fitness function design for quantifying scientific success, anticipated emergent behaviors, mechanisms for preventing lab dominance and preserving diversity, and computational efficiency strategies to enable large swarms exhibiting complex emergent behavior analogous to real-world scientific communities. A working instance of the AI Science Community is currently under development.

Ulisses Braga-Neto

Department of Electrical and Computer Engineering, Texas A&M University

AI for ScienceAutonomous Scientific DiscoveryMulti-Agent SystemsAgent-Based Modeling
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Mar 21, 2026

Subjective-Graph LLM Agents for Simulating Uncertainty in Classroom Social Perception

Social actors do not observe a common social world: each individual forms judgments from a partial and potentially distorted view of the surrounding network. We study whether graph-local evidence and credibility-weighted communication can generate persistent distortions in perceived academic standing, even when agents repeatedly receive objective performance signals. We introduce a data-constrained multi-agent framework in which LLM agents operate through individualized subjective graphs that determine peer visibility, evidence access, and interaction opportunities. Agents exchange uncertainty-annotated assessments, evaluate message credibility, and maintain explicit Gaussian belief states updated through Bayesian fusion. We evaluate the framework on 12 middle-school classrooms comprising 482 students, using questionnaire-derived social information and six consecutive examinations. On the Social-Observed subset (n=419), collective ranking error increases from 0.066 \pm 0.008 to 0.124 \pm 0.009 across six epochs despite repeated exam-based anchoring. Ablations associate individualized visibility and LLM-based trust gating with more stable long-horizon behavior, while constrained retrieval primarily safeguards against global-information leakage. Compared with evaluated DeGroot configurations, the proposed framework achieves lower final ranking error; those DeGroot configurations exhibit near-zero terminal opinion diversity. These findings establish subjective-graph LLM agents as a mechanism-oriented framework for data-constrained simulated social perception. Code is available at https://anonymous.4open.science/r/Rashomonomon-0126.

Jinming Yang, Xinyu Jiang, Xinshan Jiao +1

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

Multi-Agent LLM SystemsOpinion DynamicsAI in EducationEducationComputational Social ScienceAgent-Based Modeling
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Mar 12, 2026

Increasing intelligence in AI agents can worsen collective outcomes

When resources are scarce, will a population of AI agents coordinate in harmony, or descend into tribal chaos? Diverse decision-making AI from different developers is entering everyday devices -- from phones and medical devices to battlefield drones and cars -- and these AI agents typically compete for finite shared resources such as charging slots, relay bandwidth, and traffic priority. Yet their collective dynamics and hence risks to users and society are poorly understood. Here we study AI-agent populations as the first system of real agents in which four key variables governing collective behaviour can be independently toggled: nature (innate LLM diversity), nurture (individual reinforcement learning), culture (emergent tribe formation), and resource scarcity. We show empirically and mathematically that when resources are scarce, AI model diversity and reinforcement learning increase dangerous system overload, though tribe formation lessens this risk. Meanwhile, some individuals profit handsomely. When resources are abundant, the same ingredients drive overload to near zero, though tribe formation makes the overload slightly worse. The crossover is arithmetical: it is where opposing tribes that form spontaneously first fit inside the available capacity. More sophisticated AI-agent populations are not better: whether their sophistication helps or harms depends entirely on a single number -- the capacity-to-population ratio -- that is knowable before any AI-agent ships.

Neil F. Johnson

Dynamic Online Networks Laboratory and Physics Department, George Washington University, Washington D.C. 20052, USA

Multi-Agent Resource AllocationMulti-Agent CoordinationMulti-Agent SystemsEmergent Behavior in Multi-Agent SystemsAgent-Based Modeling
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Mar 10, 2026

Emotional Modulation in Swarm Decision Dynamics

Collective decision-making in biological and human groups often emerges from simple interaction rules that amplify minor differences into consensus. The bee equation, developed initially to describe nest-site selection in honeybee swarms, captures this dynamic through recruitment and inhibition processes. Here, we extend the bee equation into an agent-based model in which emotional valence (positive-negative) and arousal (low-high) act as modulators of interaction rates, effectively altering the recruitment and cross-inhibition parameters. Agents display simulated facial expressions mapped from their valence-arousal states, allowing the study of emotional contagion in consensus formation. Three scenarios are explored: (1) the joint effect of valence and arousal on consensus outcomes and speed, (2) the role of arousal in breaking ties when valence is matched, and (3) the "snowball effect" in which consensus accelerates after surpassing intermediate support thresholds. Results show that emotional modulation can bias decision outcomes and alter convergence times by shifting effective recruitment and inhibition rates. At the same time, intrinsic non-linear amplification can produce decisive wins even in fully symmetric emotional conditions. These findings link classical swarm decision theory with affective and social modelling, highlighting how both emotional asymmetries and structural tipping points shape collective outcomes. The proposed framework offers a flexible tool for studying the emotional dimensions of collective choice in both natural and artificial systems.

David Freire-Obregón

SIANI, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

Multi-Agent ConsensusMulti-Agent SystemsValence-Arousal ModelingAgent-Based Modeling
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Mar 6, 2026

TerraLingua: Emergence and Analysis of Open-endedness in LLM Ecologies

As autonomous agents increasingly operate in real-world digital ecosystems, understanding how they coordinate, form institutions, and accumulate shared culture becomes both a scientific and practical priority. This paper introduces TerraLingua, a persistent multi-agent ecology designed to study open-ended dynamics in such systems. Unlike prior large language model simulations with static or consequence-free environments, TerraLingua imposes resource constraints and limited lifespans for the agents. As a result, agents create artifacts that persist beyond individuals, shaping future interactions and selection pressures. To characterize the dynamics, an AI Anthropologist systematically analyzes agent behavior, group structure, and artifact evolution. Across experimental conditions, the results reveal the emergence of cooperative norms, division of labor, governance attempts, and branching artifact lineages consistent with cumulative cultural processes. Divergent outcomes across experimental runs can be traced back to specific innovations and organizational structures. TerraLingua thus provides a platform for characterizing the mechanisms of cumulative culture and social organization in artificial populations, and can serve as a foundation for guiding real-world agentic populations to socially beneficial outcomes.

Giuseppe Paolo, Jamieson Warner, Hormoz Shahrzad +3

Cognizant AI Lab · The University of Texas at Austin

Open-Ended EvolutionEmergent Behavior in Multi-Agent SystemsCultural EvolutionLarge Language Model-Based Social SimulationAgent-Based Modeling
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Mar 5, 2026

The effect of a toroidal opinion space on opinion bi-polarisation

Many models of opinion dynamics include measures of distance between opinions. Such models are susceptible to boundary effects where the choice of the topology of the opinion space may influence the dynamics. In this paper we study an opinion dynamics model following the seminal model by Axelrod, with the goal of understanding the effect of a toroidal opinion space. To do this we systematically compare two versions of the model: one with toroidal opinion space and one with cubic opinion space. In their most basic form the two versions of our model result in similar dynamics (consensus is attained eventually). However, as we include bounded confidence and eventually per agent weighting of opinion elements the dynamics become quite contrasting. The toroidal opinion space consistently allows for a greater number of groups in steady state than the cubic opinion space model. Furthermore, the outcome of the dynamics in the toroidal opinion space model are more sensitive to the inclusion of extensions than in the cubic opinion space model.

Frank P. Pijpers, Benedikt V. Meylahn, Michel R. H. Mandjes

Statistics Netherlands The Hague · Korteweg-de Vries Institute for Mathematics University of Amsterdam Amsterdam · Institute of Physics Dutch Institute for Emergent Phenomena University of Amsterdam Amsterdam +1

Opinion DynamicsAgent-Based Modeling
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Mar 3, 2026

Multi-Agent-Based Simulation of Archaeological Mobility in Uneven Landscapes

Understanding mobility, movement, and interaction in archaeological landscapes is essential for interpreting past human behavior, transport strategies, and spatial organization, yet such processes are difficult to reconstruct from static archaeological evidence alone. This paper presents a multi-agent-based modeling framework for simulating archaeological mobility in uneven landscapes, integrating realistic terrain reconstruction, heterogeneous agent modeling, and adaptive navigation strategies. The proposed approach combines global path planning with local dynamic adaptation, through reinforcment learning, enabling agents to respond efficiently to dynamic obstacles and interactions without costly global replanning. Real-world digital elevation data are processed into high-fidelity three-dimensional environments, preserving slope and terrain constraints that directly influence agent movement. The framework explicitly models diverse agent types, including human groups and animal-based transport systems, each parameterized by empirically grounded mobility characteristics such as load, slope tolerance, and physical dimensions. Two archaeological-inspired use cases demonstrate the applicability of the approach: a terrain-aware pursuit and evasion scenario and a comparative transport analysis involving pack animals and wheeled carts. The results highlight the impact of terrain morphology, visibility, and agent heterogeneity on movement outcomes, while the proposed hybrid navigation strategy provides a computationally efficient and interpretable solution for large-scale, dynamic archaeological simulations.

Chairi Kiourt, Vassilis Evangelidis, Dimitris Grigoropoulos

Agent-Based ModelingMulti-Agent Path Finding
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Mar 1, 2026

Agent-Based Simulation of Trust Development in Human-Robot Teams: An Empirically-Validated Framework

This paper presents an empirically grounded agent-based model capturing trust dynamics, workload distribution, and collaborative performance in human-robot teams. The model, implemented in NetLogo 6.4.0, simulates teams of 2--10 agents performing tasks of varying complexity. We validate against Hancock et al.'s (2021) meta-analysis, achieving interval validity for 4 of 8 trust antecedent categories and strong ordinal validity (Spearman \r{ho}=0.833ρ= 0.833 \r{ho}=0.833). Sensitivity analysis using OFAT and full factorial designs (n=50n = 50 n=50 replications per condition) reveals robot reliability exhibits the strongest effect on trust (η2=0.35η^2 = 0.35 η2=0.35) and dominates task success (η2=0.93η^2 = 0.93 η2=0.93) and productivity (η2=0.89η^2 = 0.89 η2=0.89), consistent with meta-analytic findings. Trust asymmetry ratios ranged from 0.07 to 0.55 -- below the meta-analytic benchmark of 1.50 -- revealing that per-event asymmetry does not guarantee cumulative asymmetry when trust repair mechanisms remain active. Scenario analysis uncovered trust-performance decoupling: the Trust Recovery scenario achieved the highest productivity (4.29) despite the lowest trust (38.2), while the Unreliable Robot scenario produced the highest trust (73.2) despite the lowest task success (33.4%), establishing calibration error as a critical diagnostic distinct from trust magnitude. Factorial ANOVA confirmed significant main effects for reliability, transparency, communication, and collaboration (p<.001p < .001 p<.001), explaining 45.4% of trust variance. The open-source implementation provides an evidence-based tool for identifying overtrust and undertrust conditions prior to deployment.

Ravi Kalluri

College of Professional Studies, Northeastern University – San Jose, California, United States

Trust in HRIHuman-Robot CollaborationHuman-Robot InteractionTrust in AIAgent-Based Modeling
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Feb 28, 2026

Agent-Based User-Adaptive Filtering for Categorized Harassing Communication

We propose an agent-based framework for personalized filtering of categorized harassing communication in online social networks. Unlike global moderation systems that apply uniform filtering rules, our approach models user-specific tolerance levels and preferences through adaptive filtering agents. These agents learn from user feedback and dynamically adjust filtering thresholds across multiple harassment categories, including offensive, abusive, and hateful content. We implement and evaluate the framework using supervised classification techniques and simulated user interaction data. Experimental results demonstrate that adaptive agents improve filtering precision and user satisfaction compared to static models. The proposed system illustrates how agent-based personalization can enhance content moderation while preserving user autonomy in digital social environments.

Zenefa Rahaman, Sandip Sen

The University of Tulsa, Tulsa, Oklahoma

Safety FilteringAgent-Based ModelingUser Preference Modeling
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Feb 25, 2026

ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning

Agent-based epidemic models (ABMs) encode behavioral and policy heterogeneity but are too slow for nightly hospital planning. We develop county-ready surrogates that learn directly from exascale ABM trajectories using Universal Differential Equations (UDEs): mechanistic SEIR-family ODEs with a neural-parameterized contact rate κφ(u,t)κ_φ(u,t) (no additive residual). Our contributions are threefold: we adapt multiple shooting and an observer-based prediction-error method (PEM) to stabilize identification of neural-augmented epidemiological dynamics across intervention-driven regime shifts; we enforce positivity and mass conservation and show the learned contact-rate parameterization yields a well-posed vector field; and we quantify accuracy, calibration, and compute against ABM ensembles and UDE baselines. On a representative ExaEpi scenario, PEM-UDE reduces mean MSE by 77% relative to single-shooting UDE (3.00 vs. 13.14) and by 20% relative to MS-UDE (3.75). Reliability improves in parallel: empirical coverage of ABM 1010-9090% and 2525-7575% bands rises from 0.68/0.43 (UDE) and 0.79/0.55 (MS-UDE) to 0.86/0.61 with PEM-UDE and 0.94/0.69 with MS+PEM-UDE, indicating calibrated uncertainty rather than overconfident fits. Inference runs in seconds on commodity CPUs (20-35 s per ∼\sim90-day forecast), enabling nightly ''what-if'' sweeps on a laptop. Relative to a ∼\sim100 CPU-hour ABM reference run, this yields ∼104×\sim10^{4}\times lower wall-clock per scenario. This closes the realism-cadence gap, supports threshold-aware decision-making (e.g., maintaining ICU occupancy <75<75%), preserves mechanistic interpretability, and enables calibrated, risk-aware scenario planning on standard institutional hardware. Beyond epidemics, the ABM→\toUDE recipe provides a portable path to distill agent-based simulators into fast, trustworthy surrogates for other scientific domains.

Sharv Murgai, Utkarsh Utkarsh, Kyle C. Nguyen +3

Monta Vista High School · MIT CSAIL · Sandia National Laboratories

Neural Surrogate ModelingEpidemic ForecastingAgent-Based ModelingUncertainty Calibration
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Feb 19, 2026

AI Agents Alone Are Not (Yet) Sufficient for Social Simulation

Recent advances in large language models (LLMs) have spurred growing interest in using LLM-integrated agents for social simulation, often under the implicit assumption that realistic population dynamics will emerge once role-specified agents are placed in a networked multi-agent setting. This position paper argues that LLM-based agents alone are not (yet) sufficient for social simulation. We attribute this over-optimism to a systematic mismatch between what current agent pipelines are typically optimized and validated to produce and what simulation-as-science requires. Concretely, role-playing plausibility does not imply faithful human behavioral validity; collective outcomes are frequently mediated by agent-environment co-dynamics rather than agent-agent messaging alone; and results can be dominated by interaction protocols, scheduling, and initial information priors. To make these underlying mechanisms explicit and auditable, we propose a unified formulation of AI agent-based social simulation as an environment-involved Markov game with explicit exposure and scheduling mechanisms, from which we derive concrete actions for design, evaluation, and interpretation.

Yiming Li, Dacheng Tao

College of Computing and Data Science, Nanyang Technological University, Singapore

Large Language Model-Based Social SimulationHuman Behavior SimulationAgent-Based ModelingLLM Agent Evaluation
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Feb 18, 2026

Modeling Trust and Liquidity Under Payment System Stress: A Multi-Agent Approach

Operational disruptions in retail payments can induce behavioral responses that outlast technical recovery and may amplify liquidity stress. We propose a multi-agent model linking card payment outages to trust dynamics, channel avoidance, and threshold-gated withdrawals. Customers and merchants interact through repeated payment attempts, while customers additionally influence one another on a Watts-Strogatz small-world network. Customers update bounded memory variables capturing accumulated negative experience (scar) and perceived systemic risk (rumor), with merchants contributing persistent broadcast signals that may lag operational recovery. We prove that, under mild conditions on memory persistence and threshold gating, aggregate withdrawal pressure can peak strictly after the outage nadir, including during the recovery phase. Simulations reproduce behavioral hysteresis and confirm delayed peaks of outflows. We further study payment substitution via instant transfer: substitution consistently reduces peak avoidance, yet its effect on cumulative outflows is non-monotonic under realistic merchant broadcast persistence. Robustness experiments across random seeds show stable qualitative behavior. The model highlights why "status green" is not equivalent to risk resolution and motivates incident response strategies that address perception, merchant messaging, and post-recovery communication in addition to technical remediation.

Masoud Amouzgar

Sharif University of Technology, Computer Science · Head of Payments, blu Bank

HysteresisFinancial ServicesFinanceAgent-Based Modeling
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Feb 17, 2026

Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model

Agent-based modelling (ABM) is a widespread approach to simulate complex systems. Advancements in computational processing and storage have facilitated the adoption of ABMs across many fields; however, ABMs face challenges that limit their use as decision-support tools. A significant issue is parameter estimation in large-scale ABMs, particularly due to computational constraints on exploring the parameter space. This study evaluates a state-of-the-art simulation-based inference (SBI) framework that uses neural networks (NN) for parameter estimation. This framework is applied to an established labour market ABM based on job transition networks. The ABM is initiated with synthetic datasets and the real U.S. labour market. Next, we compare the effectiveness of summary statistics derived from a list of statistical measures with that learned by an embedded NN. The results demonstrate that the NN-based approach recovers the original parameters when evaluating posterior distributions across various dataset scales and improves efficiency compared to traditional Bayesian methods.

M Lopes Alves, Joel Dyer, Doyne Farmer +2

Department of Computer Science Institute of New Economic Thinking University of Oxford Oxford, UK · Department of Computer Science University of Oxford Oxford, UK

Agent-Based ModelingSimulation-Based InferenceNeural Posterior Estimation
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Feb 17, 2026

Enhancing Computational Efficiency in NetLogo: Best Practices for Running Large-Scale Agent-Based Models on AWS and Cloud Infrastructures

The rising complexity and scale of agent-based models (ABMs) necessitate efficient computational strategies to manage the increasing demand for processing power and memory. This manuscript provides a comprehensive guide to optimizing NetLogo, a widely used platform for ABMs, for running large-scale models on Amazon Web Services (AWS) and other cloud infrastructures. It covers best practices in memory management, Java options, BehaviorSpace execution, and AWS instance selection. By implementing these optimizations and selecting appropriate AWS instances, we achieved a 32% reduction in computational costs and improved performance consistency. Through a comparative analysis of NetLogo simulations on different AWS instances using the wolf-sheep predation model, we demonstrate the performance gains achievable through these optimizations.

Michael A. Duprey, Georgiy V. Bobashev

RTI International

Agent-Based Modeling
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Jan 12, 2026

Adapting Neural Posterior Estimators for Simulation-Based Inference in Agent-Based Models

Agent-based models (ABMs) are widely used in economics and social sciences. They represent heterogeneous personal behaviors and their interactions by using simulations, whereas their likelihoods are often intractable for parameter estimations. Therefore, most traditional statistical inference techniques are hindered. Driven by recent advances in probabilistic machine learning, recent works have applied neural networks to achieve likelihood-free inference in large-scale ABMs. Specifically, Neural Posterior Estimation using normalizing flows provide amortized Bayesian inference from simulations, yet a pre-trained estimator may become inaccurate when the target task changes. In this work, we investigate the practicality of adapting a pre-trained conditional normalizing flow through a unified sequential neural posterior estimation test-time training (SNPE-TTT) framework. The framework includes four adaptation strategies: full-parameter adaptation, low-rank adaptation, and two update spaces identified from target-task gradients. We evaluate all four methods across economic simulation models with both tractable and intractable likelihoods. These benchmarks demonstrate that the sequential neural posterior estimation test-time training framework can adapt pre-trained posterior estimators to related inference tasks using a limited budget of target simulations. Posterior recovery and computational benefits depend on the task and adaptation strategy, with lower target-stage cost in some settings but no consistently superior posterior accuracy.

Junyao Zhang, Jinglai Li, Junqi Tang

School of Mathematics, University of Birmingham

Simulation-Based InferenceNeural Posterior EstimationTest-Time TrainingAgent-Based Modeling
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Sep 28, 2025

TeraAgent: A Distributed Agent-Based Simulation Engine for Simulating Half a Trillion Agents

Agent-based simulation is a method for studying highly complex systems in disciplines as diverse as biology, medicine, sociology, economics, finance, and more. In this method, an agent represents a small part of the system, such as a human cell, and, through interactions among many agents, the system's behavior emerges, like the growth of cancer in the human body. Real-world complex systems, such as a human organ, comprise billions of agents. However, the state-of-the-art agent-based simulation platform, BioDynaMo, does not support the distributed processing of a simulation across servers. This limitation restricts the scalability of agent counts to the computational resources available within a single server, hindering users from conducting larger and more meaningful simulations. In this paper, we introduce TeraAgent, a new distributed agent-based simulation engine. To overcome BioDynaMo's scalability limitation, TeraAgent enables efficient exchange of agent information across servers by implementing two solutions: 1) a tailored serialization mechanism that allows agents to be accessed and mutated directly at the receive buffer, and 2) delta encoding that significantly reduces data transfer across servers by leveraging the iterative nature of agent-based simulations. Built on our solutions, TeraAgent 1) enables extreme-scale simulations with half a trillion agents (292x that of BioDynaMo) on 84,096 CPU cores, 2) reduces time-to-result leveraging parallelism across compute nodes, 3) improves interoperability with third-party tools, and 4) provides users with more hardware flexibility. TeraAgent serializes agents with a median speedup of 110x (37x for deserialization) over the state-of-the-art ROOT I/O baseline, delivers 8x the agent update rate per CPU core of Biocellion, and improves in-situ visualization performance by 39x.

Lukas Breitwieser, Ahmad Hesam, Abdullah Giray Yağlıkçı +3

ETH Zurich, Switzerland · Delft University of Technology, The Netherlands · CERN, Switzerland

Agent-Based ModelingHigh-Performance Computing
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