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.

翻译：系统复杂性研究主要具有两个目标：探索潜在规律与构建理论解释。规律探索旨在阐明系统复杂性涌现背后的机制，而理论解释则致力于揭示这种复杂性的根本成因。规律通常被定义为变量间的映射关系，而理论则提供对系统行为的因果性解释。基于智能体的建模（ABM）是研究复杂系统的重要方法，但其往往侧重于仿真而非实验验证，因此难以深入揭示系统运行的根本原理。与传统依赖人工推理的情景分析不同，计算实验强调反事实实验——即构建模拟现实事件不同"演化路径"的平行世界。通过系统调整输入变量并观察输出变量的相应变化，计算实验为因果推断提供了有力工具，从而弥补了传统ABM方法的局限性。这些方法共同为系统动态演化的因果机制研究提供了新的视角。本部分有助于读者初步理解完整的计算实验方法体系，为后续研究奠定基础。