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

arXiv: https://arxiv.org/abs/2507.09657 · HTML: https://mlsanity.com/paper/2507.09657/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 13 Jul 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 8 of 8)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [AgentSims: An Open-Source Sandbox for Large Language Model Evaluation](https://mlsanity.com/paper/2308.04026/) (2023)
- [Multi-Agent Consensus Seeking via Large Language Models](https://mlsanity.com/paper/2310.20151/) (2023)
- [Epidemic Modeling with Generative Agents](https://mlsanity.com/paper/2307.04986/) (2023)
- [Knowledge Distillation from Large Language Models for Household Energy Modeling](https://mlsanity.com/paper/2502.03034/) (2025)
- [Crowd: A Social Network Simulation Framework](https://mlsanity.com/paper/2412.10781/) (2024)

## Built on by (most cited first, 2 of 2)

- [BuildOcc: A Large Language Model Occupant Agent Platform for Building Energy Research](https://mlsanity.com/paper/2609.02729/) (2026)
- [Do Personality-Tuned LLMs Make Better Social Agents?](https://mlsanity.com/paper/2609.21857/) (2026)

## Abstract (arXiv)

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.