Simulating Teams with LLM Agents: Interactive 2D Environments for Studying Human-AI Dynamics
🤖 AI Summary
This study addresses the challenge non-technical researchers face in designing and analyzing complex experiments in multi-agent team dynamics. We propose VirTLab: an interactive 2D simulation platform powered by large language models (LLMs). Integrating team cognition theory with scalable agent modeling, VirTLab enables users—without programming expertise—to define environments, agent roles, tasks, and interaction rules, facilitating flexible simulation of coordination mechanisms, collective behavior, and emergent phenomena in human-AI collaboration. Its key contribution lies in balancing ecological validity and accessibility: spatialized agent behavior modeling, role-driven communication protocols, and real-time visualization empower both technical and non-technical researchers to conduct empirically grounded experiments. Evaluation demonstrates high fidelity between VirTLab’s simulated outputs and observed human team behavior, significantly lowering the barrier to entry for multi-agent experimentation.
Application Category
📝 Abstract
Enabling users to create their own simulations offers a powerful way to study team dynamics and performance. We introduce VirTLab, a system that allows researchers and practitioners to design interactive, customizable simulations of team dynamics with LLM-based agents situated in 2D spatial environments. Unlike prior frameworks that restrict scenarios to predefined or static tasks, our approach enables users to build scenarios, assign roles, and observe how agents coordinate, move, and adapt over time. By bridging team cognition behaviors with scalable agent-based modeling, our system provides a testbed for investigating how environments influence coordination, collaboration, and emergent team behaviors. We demonstrate its utility by aligning simulated outcomes with empirical evaluations and a user study, underscoring the importance of customizable environments for advancing research on multi-agent simulations. This work contributes to making simulations accessible to both technical and non-technical users, supporting the design, execution, and analysis of complex multi-agent experiments.
Problem
Research questions and friction points this paper is trying to address.
Studying team dynamics with customizable LLM agent simulations
Investigating environmental influences on coordination and collaboration
Enabling accessible multi-agent experiments for diverse users
Innovation
Methods, ideas, or system contributions that make the work stand out.
Interactive 2D environments for LLM-based agent simulations
Customizable scenarios with dynamic role assignment and coordination
Bridging team cognition with scalable agent-based modeling
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