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

Researchers Juhoon Lee and Joseph Seering have proposed a new framework called SLALOM (Simulation Lifecycle Analysis via Longitudinal Observation Metrics) to tackle a critical validity crisis in using Large Language Model (LLM) agents for social science. Current methods for evaluating these AI-driven social simulations suffer from a 'stopped clock' problem: they only confirm if a simulation reached a correct final outcome, ignoring whether the path it took was sociologically plausible. Because LLMs are opaque 'black boxes,' verifying the internal social mechanisms they generate has been a persistent challenge. SLALOM shifts the validation paradigm from simple outcome verification to assessing the fidelity of the entire simulation process.

Drawing on established social science techniques like Pattern-Oriented Modeling (POM), SLALOM treats social phenomena as multivariate time series that must pass through specific intermediate waypoints, or 'SLALOM gates.' The core innovation is using Dynamic Time Warping (DTW), an algorithm for aligning two temporal sequences, to compare the simulated trajectory against empirical ground truth data. This provides a quantitative metric for 'structural realism,' allowing researchers to measure how well an AI simulation captures the nuanced progression of real-world social dynamics, rather than just stumbling onto the right answer by chance. The work, presented at the CHI 2026 PoliSim workshop, aims to establish more rigorous standards for using generative AI in policy testing and social science research.