From Agent Simulation to Social Simulator: A Comprehensive Review (Part 2)
Xiao Xue, Deyu Zhou, Ming Zhang, Xiangning Yu, Fei-Yue Wang

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…
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Taxonomy
TopicsComplex Systems and Decision Making · Simulation Techniques and Applications · Opinion Dynamics and Social Influence
