From Agent Simulation to Social Simulator: A Comprehensive Review (Part 1)

2025-10-20

AI Summary

Conventional physics-based simulation methods struggle to capture historical evolution, heterogeneity, and emergent phenomena inherent in social systems. Method: This study systematically reviews the development trajectory and design principles of agent-based modeling (ABM) in the social sciences. It introduces— for the first time—a tripartite classification of ABM social simulation paradigms: thought experiments, mechanism exploration, and parallel optimization. A generic three-layer modeling framework is proposed, integrating agents, environments, and interaction rules, alongside a taxonomy tailored for social simulator development. Contribution/Results: By synthesizing canonical case studies and core methodological challenges, the work establishes a theoretical foundation and practical guidelines for the standardization of ABM methodology and the systematic engineering of social simulators, thereby advancing rigorous, interpretable, and scalable computational social science.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This is the first part of the comprehensive review, focusing on the historical development of Agent-Based Modeling (ABM) and its classic cases. It begins by discussing the development history and design principles of Agent-Based Modeling (ABM), helping readers understand the significant challenges that traditional physical simulation methods face in the social domain. Then, it provides a detailed introduction to foundational models for simulating social systems, including individual models, environmental models, and rule-based models. Finally, it presents classic cases of social simulation, covering three types: thought experiments, mechanism exploration, and parallel optimization.

Problem

• Reviewing historical development of Agent-Based Modeling approaches
• Addressing limitations of traditional simulation in social domains
• Examining foundational models for simulating complex social systems

Innovation

• Agent-Based Modeling for social simulation
• Individual and environmental rule-based models
• Thought experiments and parallel optimization cases