Negotiating Comfort: Simulating Personality-Driven LLM Agents in Shared Residential Social Networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911052969541632 |
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| author | Rende, Ann Nedime Nese Yilmaz, Tolga Ulusoy, Özgür |
| author_facet | Rende, Ann Nedime Nese Yilmaz, Tolga Ulusoy, Özgür |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09657 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Negotiating Comfort: Simulating Personality-Driven LLM Agents in Shared Residential Social Networks Rende, Ann Nedime Nese Yilmaz, Tolga Ulusoy, Özgür Social and Information Networks Multiagent Systems 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. |
| title | Negotiating Comfort: Simulating Personality-Driven LLM Agents in Shared Residential Social Networks |
| topic | Social and Information Networks Multiagent Systems |
| url | https://arxiv.org/abs/2507.09657 |