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Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review.

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    This review explores integrating machine learning (ML) with agent-based modeling (ABM) to enhance adaptive decision-making. It details ML applications across four scenarios, improving predictions and policy interventions.

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    Area of Science:

    • Computational Social Science
    • Artificial Intelligence
    • Complex Systems Modeling

    Background:

    • Agent-based modeling (ABM) traditionally uses hardcoded rules for agent behavior.
    • Machine learning (ML) offers adaptive learning capabilities to improve agent decision-making.
    • Integrating ML into ABM is an emerging field with underexplored frameworks and procedures.

    Purpose of the Study:

    • To provide a comprehensive review of applying ML within ABM frameworks.
    • To investigate generalized scenarios, implementation procedures, and multidisciplinary applications.
    • To discuss ML's role in enhancing prediction and sequential decision-making in ABMs.

    Main Methods:

    • Review of existing literature on ML applications in ABM.
    • Categorization of ML applications into four key scenarios: situational awareness, behavior intervention, emulator, and sequential decision-making.
    • Analysis of algorithms, frameworks, and implementation procedures for each scenario.

    Main Results:

    • Identified four major scenarios for ML integration in ABM.
    • Discussed how ML improves ABM predictions by managing variance-bias trade-offs.
    • Highlighted ML's potential for reinforced behavioral intervention in micro and macro-level decision-making.

    Conclusions:

    • ML significantly enhances adaptive decision-making in ABM.
    • Future research should address data quality, reinforcement learning convergence, interpretability, and bounded rationality.
    • This integration offers powerful tools for complex systems analysis and policy design.