SLALOM: Simulation Lifecycle Analysis via Longitudinal Observation Metrics for Social Simulation

2026-04-13

AI Summary

This study addresses the prevailing focus on outcome correctness in large language model–driven social simulations, which often neglects the structural authenticity of the underlying evolutionary processes. To bridge this gap, we propose the SLALOM framework, which models social phenomena as multivariate time series and introduces intermediate constraint gates—termed SLALOM gates—that correspond to critical phases of the process. By integrating Dynamic Time Warping (DTW) to align simulated trajectories with empirical data, our approach uniquely prioritizes process fidelity as a core validation dimension. Drawing on Pattern-Oriented Modeling principles and longitudinal observational metrics, SLALOM transcends conventional endpoint-oriented evaluation paradigms, enabling quantitative assessment of the structural realism of social dynamics. This method effectively distinguishes plausible generative mechanisms from random noise, thereby substantially enhancing the reliability and scientific rigor of policy simulations.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• social simulation
• validity
• process fidelity
• black box
• trajectory evaluation

Innovation

• SLALOM
• Process fidelity
• Dynamic Time Warping
• Pattern-Oriented Modeling
• Social simulation