用时间轨迹比对方法，检验社会模拟过程是否真实可信。
SLALOM: Simulation Lifecycle Analysis via Longitudinal Observation Metrics for Social Simulation
- 以多变量时间序列比对模拟与真实社会演化路径
- 通过动态时间规整量化过程相似性，识别合理社会机制
- 适合政策模拟、社会动力学研究者使用
大语言模型代理为生成式社会科学提供了潜在变革路径，但面临有效性危机。现有模拟评估方法存在‘停摆的时钟’问题：仅验证最终结果正确，却忽略通向该结果的路径是否具有社会学合理性。由于大语言模型内部推理过程不透明，验证社会机制的‘黑箱’仍是长期挑战。本文提出SLALOM（基于纵向观测指标的社会模拟生命周期分析），将验证从结果确认转向过程保真度评估。借鉴模式导向建模（POM）思想，SLALOM将社会现象视为需经过特定‘门限’（即中间阶段约束）的多变量时间序列。通过动态时间规整（DTW）对齐模拟轨迹与实证真实数据，提供量化指标评估结构现实性，有助于区分合理社会动态与随机噪声，提升政策模拟标准的可靠性。
原文摘要 · Abstract (English)
Large Language Model (LLM) agents offer a potentially-transformative path forward for generative social science but face a critical crisis of validity. Current simulation evaluation methodologies suffer from the "stopped clock" problem: they confirm that a simulation reached the correct final outcome while ignoring whether the trajectory leading to it was sociologically plausible. Because the internal reasoning of LLMs is opaque, verifying the "black box" of social mechanisms remains a persistent challenge. In this paper, we introduce SLALOM (Simulation Lifecycle Analysis via Longitudinal Observation Metrics), a framework that shifts validation from outcome verification to process fidelity. Drawing on Pattern-Oriented Modeling (POM), SLALOM treats social phenomena as multivariate time series that must traverse specific SLALOM gates, or intermediate waypoint constraints representing distinct phases. By utilizing Dynamic Time Warping (DTW) to align simulated trajectories with empirical ground truth, SLALOM offers a quantitative metric to assess structural realism, helping to differentiate plausible social dynamics from stochastic noise and contributing to more robust policy simulation standards.
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