From Agent Simulation to Social Simulator: A Comprehensive Review (Part 2)

2026-01-16

AI Summary

This study addresses a key limitation of traditional agent-based modeling (ABM)—its emphasis on simulation at the expense of experimental rigor, which hinders the identification of causal mechanisms in complex systems. To overcome this, the authors propose an integrated framework that combines computational experimentation with ABM, enabling systematic manipulation of input variables and counterfactual simulations. This approach constructs a “parallel world” capable of exploring multiple evolutionary trajectories, thereby transcending the constraints of conventional scenario analysis that relies heavily on subjective reasoning. The framework facilitates causal inference regarding the dynamic evolution of complex social systems, offers interpretable causal pathways to understand emergent phenomena, and establishes a theoretical foundation for computational experimentation in complex systems research.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Reasoning under Uncertainty: Causality

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The study of system complexity primarily has two objectives: to explore underlying patterns and to develop theoretical explanations. Pattern exploration seeks to clarify the mechanisms behind the emergence of system complexity, while theoretical explanations aim to identify the fundamental causes of this complexity. Laws are generally defined as mappings between variables, whereas theories offer causal explanations of system behavior. Agent Based Modeling(ABM) is an important approach for studying complex systems, but it tends to emphasize simulation over experimentation. As a result, ABM often struggles to deeply uncover the governing operational principles. Unlike conventional scenario analysis that relies on human reasoning, computational experiments emphasize counterfactual experiments-that is, creating parallel worlds that simulate alternative"evolutionary paths"of real-world events. By systematically adjusting input variables and observing the resulting changes in output variables, computational experiments provide a robust tool for causal inference, thereby addressing the limitations of traditional ABM. Together, these methods offer causal insights into the dynamic evolution of systems. This part can help readers gain a preliminary understanding of the entire computational experiment method, laying the foundation for the subsequent study.

Problem

• Agent Based Modeling
• complex systems
• causal inference
• computational experiments
• system complexity

Innovation

• Agent-Based Modeling
• Computational Experiment
• Counterfactual Experiment
• Causal Inference
• Social Simulator