World Models and Artificial Intelligence: New Approaches to Understanding Complex Physical and Social Systems

Stefanos Zenios

World Models and Artificial Intelligence: New Approaches to Understanding Complex Physical and Social Systems

Author(s)	Stefanos Zenios
Country	United States
Abstract	World models are emerging as an important direction in artificial intelligence for developing systems capable of representing, predicting, and reasoning about complex environments. Unlike conventional AI systems that primarily map inputs to outputs, world-model approaches attempt to construct internal representations of how physical, social, and digital environments behave over time. This capability has implications for robotics, autonomous vehicles, climate modelling, healthcare, economics, urban planning, scientific discovery, and decision support. This paper examines the conceptual foundations of world models and their applications to complex physical and social systems. A conceptual qualitative methodology is used to analyse developments in representation learning, multimodal AI, generative modelling, reinforcement learning, simulation, causal reasoning, and agent-based systems. The paper distinguishes between predictive world models, generative environment models, causal models, and socially grounded models. Particular attention is given to challenges involving uncertainty, long-horizon prediction, causal inference, emergent behaviour, data scarcity, distribution shift, model validation, and ethical governance. An Integrated World Model Framework is proposed that combines multimodal perception, latent-state representation, temporal prediction, causal reasoning, simulation, uncertainty estimation, and decision-making. The analysis suggests that future AI systems will increasingly combine learned representations with structured knowledge, physical constraints, simulations, and real-world feedback. However, world models should not be interpreted as complete representations of reality; their usefulness depends on the quality, scope, and assumptions of the environments from which they learn. The paper concludes that world models could become a foundational component of next-generation AI, particularly when integrated with scientific modelling, embodied intelligence, and responsible human oversight.
Keywords	World Models, Artificial Intelligence, Generative AI, Complex Systems, Physical Systems, Social Systems, Predictive Modelling, Causal AI, Simulation, Embodied Intelligence.
Field	Engineering
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-09-03

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American Journal of Advanced Multidisciplinary Research and Innovation

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World Models and Artificial Intelligence: New Approaches to Understanding Complex Physical and Social Systems

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