HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation
This addresses the need for credible and topic-adaptive agent generation in simulation domains, representing a novel method for a known bottleneck.
The paper tackles the problem of generating high-fidelity agents for Agent-Based Modeling by proposing HAG, a hierarchical framework that aligns macro-level distributions and ensures micro-level consistency, resulting in a 37.7% reduction in population alignment errors and an 18.8% improvement in sociological consistency.
High-fidelity agent initialization is crucial for credible Agent-Based Modeling across diverse domains. A robust framework should be Topic-Adaptive, capturing macro-level joint distributions while ensuring micro-level individual rationality. Existing approaches fall into two categories: static data-based retrieval methods that fail to adapt to unseen topics absent from the data, and LLM-based generation methods that lack macro-level distribution awareness, resulting in inconsistencies between micro-level persona attributes and reality. To address these problems, we propose HAG, a Hierarchical Agent Generation framework that formalizes population generation as a two-stage decision process. Firstly, utilizing a World Knowledge Model to infer hierarchical conditional probabilities to construct the Topic-Adaptive Tree, achieving macro-level distribution alignment. Then, grounded real-world data, instantiation and agentic augmentation are carried out to ensure micro-level consistency. Given the lack of specialized evaluation, we establish a multi-domain benchmark and a comprehensive PACE evaluation framework. Extensive experiments show that HAG significantly outperforms representative baselines, reducing population alignment errors by an average of 37.7% and enhancing sociological consistency by 18.8%.