标题： 协商舒适度：在共享住宅社交网络中模拟个性驱动的LLM代理 显示英文标题

标题： Negotiating Comfort: Simulating Personality-Driven LLM Agents in Shared Residential Social Networks

摘要： 我们使用由大型语言模型（LLMs）驱动的生成代理来模拟共享住宅楼中的社交网络，推动中央供暖系统的温度决策。 代理分为家庭成员和代表，考虑个人偏好、个人特质、联系和天气状况。 每日模拟包括家庭层面的共识，然后是代表之间的楼宇范围内的决策。 我们测试了三种人格特质分布（积极、混合和消极），发现积极特质与更高的幸福感和更牢固的友谊相关。 温度偏好、坚持性和无私性对幸福感和决策有显著影响。 这项工作展示了LLM驱动的代理如何帮助模拟微妙的人类行为，其中复杂的现实生活人类模拟难以设定。 显示英文摘要

摘要： We use generative agents powered by large language models (LLMs) to simulate a social network in a shared residential building, driving the temperature decisions for a central heating system. Agents, divided into Family Members and Representatives, consider personal preferences, personal traits, connections, and weather conditions. Daily simulations involve family-level consensus followed by building-wide decisions among representatives. We tested three personality traits distributions (positive, mixed, and negative) and found that positive traits correlate with higher happiness and stronger friendships. Temperature preferences, assertiveness, and selflessness have a significant impact on happiness and decisions. This work demonstrates how LLM-driven agents can help simulate nuanced human behavior where complex real-life human simulations are difficult to set.