PASM: Population Adaptive Symbolic Mixture-of-Experts Model for Cross-location Hurricane Evacuation Decision Prediction

2026-03-31

AI Summary

This study addresses the limited generalizability of existing hurricane evacuation decision models when transferred across regions, which often fail to capture behavioral heterogeneity among demographically similar populations, leading to misclassification of vulnerable groups and poor interpretability. To overcome these limitations, the authors propose a novel approach that integrates large language model–guided symbolic regression with a Mixture-of-Experts (MoE) architecture. This framework employs a dynamic routing mechanism to adaptively identify distinct behavioral subgroups and generate human-interpretable decision rules. Notably, it represents the first integration of symbolic regression with MoE. Evaluated in a cross-regional transfer scenario—from Florida and Texas to Georgia—with only 100 calibration samples, the method achieves a Matthews Correlation Coefficient of 0.607, significantly outperforming XGBoost, TabPFN, GPT-5-mini, and meta-learning baselines, while fairness audits reveal no significant demographic bias.

Abstract

Accurate prediction of evacuation behavior is critical for disaster preparedness, yet models trained in one region often fail elsewhere. Using a multi-state hurricane evacuation survey, we show this failure goes beyond feature distribution shift: households with similar characteristics follow systematically different decision patterns across states. As a result, single global models overfit dominant responses, misrepresent vulnerable subpopulations, and generalize poorly across locations. We propose Population-Adaptive Symbolic Mixture-of-Experts (PASM), which pairs large language model guided symbolic regression with a mixture-of-experts architecture. PASM discovers human-readable closed-form decision rules, specializes them to data-driven subpopulations, and routes each input to the appropriate expert at inference time. On Hurricanes Harvey and Irma data, transferring from Florida and Texas to Georgia with 100 calibration samples, PASM achieves a Matthews correlation coefficient of 0.607, compared to XGBoost (0.404), TabPFN (0.333), GPT-5-mini (0.434), and meta-learning baselines MAML and Prototypical Networks (MCC $\leq$ 0.346). The routing mechanism assigns distinct formula archetypes to subpopulations, so the resulting behavioral profiles are directly interpretable. A fairness audit across four demographic axes finds no statistically significant disparities after Bonferroni correction. PASM closes more than half the cross-location generalization gap while keeping decision rules transparent enough for real-world emergency planning.

Problem

• cross-location generalization
• evacuation decision prediction
• subpopulation heterogeneity
• disaster preparedness
• behavioral modeling

Innovation

• Symbolic Regression
• Mixture-of-Experts
• Cross-location Generalization
• Interpretable AI
• Evacuation Decision Modeling