用大模型生成可解释的多设施选址机制，效果优于传统方法。
Large Language Models for Multi-Facility Location Mechanism Design
- 结合大模型与进化算法自动设计机制
- 在多种场景下社会成本更低，策略性行为几乎无效
- 无需调参、可解释，适合需要透明决策的场景
多设施选址机制设计中，基于代理偏好优化社会成本并保证策略性诚实一直面临挑战，主要源于所需领域知识广、最坏情况保证差。近年深度学习模型被提出作为替代，但需一定先验知识、大量超参数调优且缺乏可解释性，而实际应用中透明性至关重要。本文提出新方法LLMMech，将大语言模型（LLMs）融入进化框架，生成可解释、免调参、经验上策略性诚实且近乎最优的机制。实验在多种设置下验证：社会成本权重任意分配、代理偏好非均匀分布，结果表明，该方法生成的机制普遍优于现有手工设计基准与深度学习模型。此外，机制对分布外代理偏好及更大规模实例具有显著泛化能力。
原文摘要 · Abstract (English)
Designing strategyproof mechanisms for multi-facility location that optimize social costs based on agent preferences had been challenging due to the extensive domain knowledge required and poor worst-case guarantees. Recently, deep learning models have been proposed as alternatives. However, these models require some domain knowledge and extensive hyperparameter tuning as well as lacking interpretability, which is crucial in practice when transparency of the learned mechanisms is mandatory. In this paper, we introduce a novel approach, named LLMMech, that addresses these limitations by incorporating large language models (LLMs) into an evolutionary framework for generating interpretable, hyperparameter-free, empirically strategyproof, and nearly optimal mechanisms. Our experimental results, evaluated on various problem settings where the social cost is arbitrarily weighted across agents and the agent preferences may not be uniformly distributed, demonstrate that the LLM-generated mechanisms generally outperform existing handcrafted baselines and deep learning models. Furthermore, the mechanisms exhibit impressive generalizability to out-of-distribution agent preferences and to larger instances with more agents.
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