Agent-Based Modeling and System Dynamics Integrated with AI and Analytical Methods: A Structured Review of Hybrid Approaches, Applications, and Future Directions for Decision Making in Complex Systems (2021–2026)

Abstract

The complexity of socio-economic, ecological, and public-health systems demands simulation frameworks capturing both macro dynamics and micro agent heterogeneity. Agent-based modeling (ABM) and system dynamics (SD), increasingly coupled with AI and analytical methods, support decision making in complex systems, yet no systematic review covers 2021–2026. A two-round PRISMA 2020 search (September 2025; May 2026) in IEEE Xplore, Web of Science, and ProQuest identified 70 eligible papers across 14 thematic clusters; because the second search round closed in May 2026, papers published later in 2026 are necessarily under-represented relative to complete prior years. Data were extracted along eleven dimensions; inter-rater reliability was κ = 0.88–0.90. Output grew 133% between 2023 and 2025. C14 (Multi-Method Simulation) and C7 (Healthcare & Epidemiology) are the largest clusters; AnyLogic dominates as the only natively tri-paradigm platform (22.9%). Hybrid models consistently identify critical intervention thresholds invisible to mono-paradigm approaches. Only 44.3% of papers report formal structural validation and 88.6% withhold code. Nine papers (12.9%) integrate ML/AI, but none apply interpretability techniques (SHAP, LIME, ICE). ABM–SD hybridization is a maturing paradigm whose epistemic gain depends on closing validation, reproducibility, and interpretability gaps. Six gaps and five priority directions for 2026–2030 are identified, including a minimum validation protocol, a standardized repository, and a dedicated interpretability framework for ML+ABM–SD hybrids that preserves causal transparency for decision making in complex systems.