World Models and Intelligent Simulation: Emerging Approaches to Understanding Complex Real-World Systems

Joydeep Chandra

World Models and Intelligent Simulation: Emerging Approaches to Understanding Complex Real-World Systems

Author(s)	Joydeep Chandra
Country	United States
Abstract	Complex real-world systems are difficult to understand because their behavior arises from dynamic interactions among numerous physical, social, environmental, and technological components. Conventional simulations can represent selected mechanisms with considerable precision, but they often depend on fixed assumptions, manually specified rules, and narrow operating conditions. World models offer a complementary approach by learning compressed representations of an environment, estimating its transition dynamics, and generating plausible future states from accumulated observations. When integrated with intelligent simulation, these models can support adaptive forecasting, counterfactual experimentation, autonomous planning, and decision evaluation without requiring every possible system condition to be encountered directly. This article examines the conceptual foundations, principal architectures, and emerging applications of world-model-based intelligent simulation. It synthesizes literature on model-based reinforcement learning, latent state-space modeling, agent-based simulation, causal reasoning, digital twins, and uncertainty-aware forecasting. A transparent simulation protocol is also introduced to compare four generalized modeling approaches: conventional rule-based simulation, agent-based simulation, data-driven predictive modeling, and hybrid world-model simulation. The benchmark values are explicitly simulated and are used only to demonstrate a reproducible analytical procedure rather than to claim completed empirical experimentation. Under the defined assumptions, the hybrid world-model approach produced the strongest overall balance across predictive accuracy, multistep rollout stability, adaptation to environmental change, and decision utility. Its computational requirements, however, remained higher than those of conventional approaches. The study concludes that world models can strengthen the explanatory and decision-support capacity of intelligent simulations when they combine learned representations with domain knowledge, causal constraints, uncertainty estimation, and systematic validation. Their practical value depends not merely on prediction accuracy but also on transparency, robustness, governance, and the ability to distinguish plausible imagined futures from reliable representations of reality.
Keywords	World models; intelligent simulation; complex systems; model-based reinforcement learning; latent dynamics; agent-based modeling; digital twins; causal reasoning; uncertainty quantification
Field	Engineering
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-02

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American Journal of Advanced Multidisciplinary Research and Innovation

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World Models and Intelligent Simulation: Emerging Approaches to Understanding Complex Real-World Systems

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