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

TL;DR

This paper reviews the evolution of agent-based modeling into comprehensive social simulation, emphasizing computational experiments for causal inference in complex systems.

Contribution

It provides a detailed overview of how computational experiments enhance agent-based modeling for understanding system complexity.

Findings

Computational experiments enable counterfactual analysis in ABM.

Systematic variable adjustments reveal causal relationships.

The review lays groundwork for future social simulation research.

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.