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World Models Should Prioritize the Unification of Physical and Social Dynamics

Xiaoyuan Zhang, Chengdong Ma, Yizhe Huang, Weidong Huang, Siyuan Qi, Song-Chun Zhu, Xue Feng, Yaodong Yang

TL;DR

This paper argues that AI world models remain incomplete when they model physical and social dynamics in isolation. It introduces ACE Principles—Abstraction, Contingent Causality, and Entangled Emergence—and the WMP-S framework to jointly model socio-physical dynamics with a unified transition function $T(s'|s)$, formalized as $WMP\text{-}S = \langle N, \mathcal{S}, T \rangle$. It outlines a detailed framework for integrating physical and social representations, plus a roadmap spanning data, architectures, algorithms, and evaluation to enable robust, generalizable socio-physical reasoning. The authors illustrate potential applications in smart urban mobility and human-AI teaming, arguing that such integrated models improve prediction, planning, and decision-making in complex real-world settings.

Abstract

World models, which explicitly learn environmental dynamics to lay the foundation for planning, reasoning, and decision-making, are rapidly advancing in predicting both physical dynamics and aspects of social behavior, yet predominantly in separate silos. This division results in a systemic failure to model the crucial interplay between physical environments and social constructs, rendering current models fundamentally incapable of adequately addressing the true complexity of real-world systems where physical and social realities are inextricably intertwined. This position paper argues that the systematic, bidirectional unification of physical and social predictive capabilities is the next crucial frontier for world model development. We contend that comprehensive world models must holistically integrate objective physical laws with the subjective, evolving, and context-dependent nature of social dynamics. Such unification is paramount for AI to robustly navigate complex real-world challenges and achieve more generalizable intelligence. This paper substantiates this imperative by analyzing core impediments to integration, proposing foundational guiding principles (ACE Principles), and outlining a conceptual framework alongside a research roadmap towards truly holistic world models.

World Models Should Prioritize the Unification of Physical and Social Dynamics

TL;DR

This paper argues that AI world models remain incomplete when they model physical and social dynamics in isolation. It introduces ACE Principles—Abstraction, Contingent Causality, and Entangled Emergence—and the WMP-S framework to jointly model socio-physical dynamics with a unified transition function , formalized as . It outlines a detailed framework for integrating physical and social representations, plus a roadmap spanning data, architectures, algorithms, and evaluation to enable robust, generalizable socio-physical reasoning. The authors illustrate potential applications in smart urban mobility and human-AI teaming, arguing that such integrated models improve prediction, planning, and decision-making in complex real-world settings.

Abstract

World models, which explicitly learn environmental dynamics to lay the foundation for planning, reasoning, and decision-making, are rapidly advancing in predicting both physical dynamics and aspects of social behavior, yet predominantly in separate silos. This division results in a systemic failure to model the crucial interplay between physical environments and social constructs, rendering current models fundamentally incapable of adequately addressing the true complexity of real-world systems where physical and social realities are inextricably intertwined. This position paper argues that the systematic, bidirectional unification of physical and social predictive capabilities is the next crucial frontier for world model development. We contend that comprehensive world models must holistically integrate objective physical laws with the subjective, evolving, and context-dependent nature of social dynamics. Such unification is paramount for AI to robustly navigate complex real-world challenges and achieve more generalizable intelligence. This paper substantiates this imperative by analyzing core impediments to integration, proposing foundational guiding principles (ACE Principles), and outlining a conceptual framework alongside a research roadmap towards truly holistic world models.
Paper Structure (31 sections, 1 equation, 3 figures, 5 tables)

This paper contains 31 sections, 1 equation, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. The Duality of World Model Predictions: Physical and Social Dimensions
  3. The Physical Dimension: Modeling Objective Reality and Natural Laws
  4. Classification and Scope of Physical Predictions.
  5. Prominent Methodologies in Physical World Modeling:
  6. Predominant Limitation of Current Physical World Models.
  7. The Social Dimension: Modeling Subjectivity, Interaction, and Group Dynamics
  8. Levels of Social Abstraction.
  9. Prominent Methodologies in Social World Modeling:
  10. Predominant Limitation of Current Social World Modeling Efforts.
  11. Integrating Physical and Social World Model
  12. The Inextricable Link Between Physical and Social World
  13. Social Shaping of Physical Reality.
  14. Physical Influence on Social Dynamics.
  15. The Indispensable Entangled Loop and Its Implications.
  16. ...and 16 more sections

Figures (3)

  • Figure 1: Real-world systems are composed of both physical and social dimensions. Physical aspects include vehicle movement, pedestrian flows, and power grid distribution (lines), while social aspects encompass competitive/cooperative relationships (connecting lines) and emotional states (facial expressions).
  • Figure 2: Physical & Social world model diagram summarizing. The social aspect is divided into Agent-Agent and Agent-Group Interaction, while the physical aspect distinguishes between interactive and non-interactive processes. Interactive dynamics are further classified into strategic and non-strategic interactions. Modalities are categorized into language, state, pixel, and 3D spaces. This is not a strict classification but a representative summary of current research directions. \ref{['tab:physical_table']} and \ref{['tab:social_table']} provide detailed examples.
  • Figure 3: Integrated Physical-Social World Model. The physical state evolution (governed by physical laws) and social state evolution (driven by Contingent Causality) are unified into a physical-social world model, which follows the ACE Principles, leading to impactful applications.