VirtLab: An AI-Powered System for Flexible, Customizable, and Large-scale Team Simulations
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918116160700416 |
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| author | Almutairi, Mohammed Chiang, Charles Guo, Haoze Belcher, Matthew Banerjee, Nandini Milkowski, Maria Volkova, Svitlana Nguyen, Daniel Weninger, Tim Yankoski, Michael Ford, Trenton W. Gomez-Zara, Diego |
| author_facet | Almutairi, Mohammed Chiang, Charles Guo, Haoze Belcher, Matthew Banerjee, Nandini Milkowski, Maria Volkova, Svitlana Nguyen, Daniel Weninger, Tim Yankoski, Michael Ford, Trenton W. Gomez-Zara, Diego |
| contents | Simulating how team members collaborate within complex environments using Agentic AI is a promising approach to explore hypotheses grounded in social science theories and study team behaviors. We introduce VirtLab, a user-friendly, customizable, multi-agent, and scalable team simulation system that enables testing teams with LLM-based agents in spatial and temporal settings. This system addresses the current frameworks' design and technical limitations that do not consider flexible simulation scenarios and spatial settings. VirtLab contains a simulation engine and a web interface that enables both technical and non-technical users to formulate, run, and analyze team simulations without programming. We demonstrate the system's utility by comparing ground truth data with simulated scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04634 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VirtLab: An AI-Powered System for Flexible, Customizable, and Large-scale Team Simulations Almutairi, Mohammed Chiang, Charles Guo, Haoze Belcher, Matthew Banerjee, Nandini Milkowski, Maria Volkova, Svitlana Nguyen, Daniel Weninger, Tim Yankoski, Michael Ford, Trenton W. Gomez-Zara, Diego Human-Computer Interaction Simulating how team members collaborate within complex environments using Agentic AI is a promising approach to explore hypotheses grounded in social science theories and study team behaviors. We introduce VirtLab, a user-friendly, customizable, multi-agent, and scalable team simulation system that enables testing teams with LLM-based agents in spatial and temporal settings. This system addresses the current frameworks' design and technical limitations that do not consider flexible simulation scenarios and spatial settings. VirtLab contains a simulation engine and a web interface that enables both technical and non-technical users to formulate, run, and analyze team simulations without programming. We demonstrate the system's utility by comparing ground truth data with simulated scenarios. |
| title | VirtLab: An AI-Powered System for Flexible, Customizable, and Large-scale Team Simulations |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.04634 |