EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation

• URL: http://arxiv.org/abs/2505.06904v1
• Date: Sun, 11 May 2025 08:51:56 GMT
• Title: EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation
• Authors: Xinyi Mou, Chen Qian, Wei Liu, Xuanjing Huang, Zhongyu Wei,
• Abstract summary: Large language models (LLMs) have demonstrated an impressive ability to role-play humans and replicate complex social dynamics.<n>Existing solutions, such as distributed mechanisms or hybrid agent-based model (ABM) integrations, either fail to address inference costs or compromise accuracy and generalizability.<n>We propose EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation.
• Score: 49.789575209305724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated an impressive ability to role-play humans and replicate complex social dynamics. While large-scale social simulations are gaining increasing attention, they still face significant challenges, particularly regarding high time and computation costs. Existing solutions, such as distributed mechanisms or hybrid agent-based model (ABM) integrations, either fail to address inference costs or compromise accuracy and generalizability. To this end, we propose EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation. EcoLANG operates in two stages: (1) language evolution, where we filter synonymous words and optimize sentence-level rules through natural selection, and (2) language utilization, where agents in social simulations communicate using the evolved language. Experimental results demonstrate that EcoLANG reduces token consumption by over 20%, enhancing efficiency without sacrificing simulation accuracy.

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