Agent-Based Modeling and Simulation in Complex Systems

Agent-based modelling and simulation (ABMS) constitutes a powerful computational paradigm for exploring the dynamics of complex systems through the interactions of discrete, autonomous agents. Each agent embodies individual decision rules, attributes and behaviours, operating within a defined environment and interacting with other agents or environmental elements. This bottom-up approach enables the emergence of system-level phenomena—patterns, trends or structures not readily apparent from the specification of individual rules alone. ABMS excels in domains characterised by heterogeneous actors, non-linear feedback, adaptive behaviours and localised interactions, spanning ecological networks, epidemiological spread, social and economic systems, infrastructure resilience and urban planning. Recent methodological advances have sought to integrate data-driven techniques, including machine learning and process mining, to enhance inference of behavioural rules, parameter calibration and real-time emulation. Developments in high-performance computing and cloud-based simulation frameworks have alleviated computational bottlenecks, enabling the exploration of multi-scale and high-resolution scenarios. Achieving robust insights from ABMS requires careful sensitivity analysis, calibration against empirical data and verification of algorithmic implementations. By offering transparency in model structure and facilitating ‘what-if’ experimentation, ABMS has become indispensable for policy exploration, risk assessment and the design of resilient socio-technical systems.

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Research from all publishers

Recent multidisciplinary reviews have highlighted the synergistic integration of machine learning with agent-based modelling. By embedding learning algorithms within agent decision cycles, models achieve adaptive behaviour, improved prediction accuracy and enhanced emulation of complex policies. This integration addresses challenges of hardcoded rule specification, enabling dynamic adjustment of agent strategies in evolving environments. In parallel, the emerging discipline of process mining has been combined with ABMS to extract procedural knowledge from event logs, offering a systematic framework for model discovery, conformance checking and scenario enhancement in socio-technical systems. These studies reveal a modest but growing body of work applying process-mining techniques to enrich model realism and support data-driven validation. Advances in open-source simulation platforms have also been reported, with comparative evaluations guiding newcomers in selecting appropriate tools based on performance, usability and extensibility. Benchmarks demonstrate wide variance in development effort and computational efficiency across platforms, underlining the importance of tool choice in facilitating reproducible and scalable ABMS studies.

Agent-Based Modeling and Simulation in Complex Systems publication trend

The graph below shows the total number of articles in agent-based modeling and simulation in complex systems across all publications each year (not limited to Nature Index journals).

Technical terms

Agent-based model (ABM): A computational framework in which individual entities, or agents, operate autonomously according to defined rules, interacting with each other and the environment to generate system-level outcomes.

Emergent phenomenon: A macro-scale pattern or behaviour arising from the collective interactions of micro-level components, not directly encoded in individual rules.

Calibration: The process of adjusting model parameters systematically to align simulation output with empirical observations or desired benchmarks.

Validation: The evaluation of model fidelity, ensuring that simulation outcomes adequately represent real-world dynamics within specified contexts.

Process mining: A data-driven technique for discovering, monitoring and improving real processes by extracting knowledge from event logs and integrating it into modelling frameworks.

Sensitivity analysis: A collection of methods used to assess how uncertainty in model inputs affects outputs, guiding robustness checks and parameter prioritisation.

High-performance computing: The use of parallel computing resources, such as multi-core processors and distributed clusters, to execute computationally intensive simulations efficiently.

References

1. Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review. IEEE Transactions on Neural Networks and Learning Systems (2023).
2. Towards integrating process mining with agent-based modeling and simulation: State of the art and outlook. Expert Systems with Applications (2025).
3. Experimenting with Agent-Based Model Simulation Tools. Applied Sciences (2022).
4. Which Sensitivity Analysis Method Should I Use for My Agent-Based Model?. Journal of Artificial Societies and Social Simulation (2016).
5. How to Relate Models to Reality? An Epistemological Framework for the Validation and Verification of Computational Models. Journal of Artificial Societies and Social Simulation (2018).