发表机构
Mistral AI; Ollama（米斯特拉尔人工智能公司; 奥拉马）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究利用感知交易成本，开发摩擦感知角色建模方法，以模拟租户对能源政策干预的反应。通过荷兰公民调查数据，比较不同模型和设置，发现纳入该方法能提升模型性能，为政策理论与LLM政策模拟搭建桥梁。
AI 中文摘要
近期研究用大语言模型（LLMs）通过基于人口统计学、态度或角色的描述来模拟人类意见和决策，但很少模拟影响人们对政策干预反应的实际、认知或社会摩擦。感知交易成本（PTC）有助于模拟此类摩擦。本文以此为视角开发了基于LLM模拟的摩擦感知角色建模方法。在节能改造背景下，用荷兰1068名公民的调查数据，比较了不同模型和设置，结果表明纳入基于PTC的角色和推理能提升模型性能，为政策理论与基于LLM的政策模拟搭建了桥梁。
英文摘要
Recent studies use Large language models (LLMs) to simulate human opinions and decisions by prompting models with demographic, attitudinal, or persona-based descriptions. Yet such simulations rarely model the practical, cognitive, or social frictions that shape how people respond to policy interventions. Perceived transaction cost (PTC) provides a useful lens for modeling the practical frictions that shape policy responses, such as information burden, administrative effort, coordination demands, and perceived uncertainty. We use this lens to develop a friction-aware persona modeling approach for LLM-based simulation. In the context of energy-efficient renovation (EER), tenants are represented not only by who they are demographically, but by how they perceive the costs, benefits, barriers, and uncertainties associated with proposed renovation plans. Using survey data collected from 1,068 citizens in the Netherlands, comprising approximately 40,548 survey question and answer pairs, we compare prompt-only and fine-tuned settings across GPT-3.5-turbo, Ministral-8B-Instruct, and Llama-3.1-8B-Instruct, and evaluate supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO) for local open-weight models. Results show that incorporating PTC-based personas and reasoning consistently improves model performance across both prompt-only and fine-tuned settings, suggesting that PTC-based persona design provides a useful bridge between institutional policy theory and interpretable LLM-based policy simulation. Code is available at https://github.com/xiaweijie1996/socialagent.
