SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulation
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
Simulation has long supported supply chain management instruction by letting learners observe network behavior and test decision strategies. Recent progress in LLM-driven agents opens new possibilities for richer, more adaptive simulations, but many existing systems still present abstract, opaque data that overwhelms learners and discourages active exploration. We introduce textitSupplyNet, a gamified visual simulation system built on a contextual graph-based LLM multi-agent framework that models interdependent supply chain dynamics and provides responsive feedback through tiered challenges. textitSupplyNet turns the simulation into a manipulable decision space by integrating an interactive network view of system state, a branching timeline for "what-if" exploration and comparison, and a task-oriented analysis console for structured performance breakdowns. Together, these visual components support counterfactual exploration, causal tracing, and comparative reasoning about outcomes. A user study suggests that textitSupplyNet increases engagement and supports users' perceived understanding of supply chain dynamics, highlighting the potential of pairing contextual multi-agent simulation with visualization to advance operational comprehension.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 18 of its 43 references that are in the map. First posted on arXiv 23 Jun 2026.
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18 of 43 references.
Closest ancestors · 3
Same direction, newest first
- 2024InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains16 Jul
- 2023CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents26 Oct
- 2023EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities16 Oct
Other references · 15
- 2022Social Simulacra: Creating Populated Prototypes for Social Computing Systemscited by 300
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- 2025TwinMarket: A Scalable Behavioral and Social Simulation for Financial Marketscited by 46
- 2024PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learningcited by 28
- 2024Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitionscited by 25
- 2025Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answeringcited by 14
- 2023"Guinea Pig Trials" Utilizing GPT: A Novel Smart Agent-Based Modeling Approach for Studying Firm Competition and Collusioncited by 13
- 2024Leveraging Graph Neural Networks and Multi-Agent Reinforcement Learning for Inventory Control in Supply Chainscited by 9
- 2025VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inferencecited by 3
- 2019Gamification in Science: A Study of Requirements in the Context of Reproducible Researchcited by 2
3 more
- 2026VizQStudio: Iterative Visualization Literacy MCQs Design with Simulated Studentscited by 2
- 2022To Trust or to Stockpile: Modeling Human-Simulation Interaction in Supply Chain Shortagescited by 1
- 2026AdaPT: Adaptive Lesson Plan Transformer for Cross-Regional and Differentiated Instructioncited by 1
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