AgoraSim: A Hybrid Agent-Based Modeling Framework

• URL: http://arxiv.org/abs/2607.05999v1
• Date: Tue, 07 Jul 2026 08:37:17 GMT
• Title: AgoraSim: A Hybrid Agent-Based Modeling Framework
• Abstract summary: AgoraSim is a hybrid agent-based modeling framework for scenario-oriented social reaction analysis.<n>It runs ratio-controlled populations that mix LLM, vision-language, custom-endpoint, random, and classical agents.<n>All agents emit a shared structured decision object, enabling common action spaces, interaction protocols, metrics, and audit records.
• Score: 4.857286974497513
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: LLM-agent simulations make natural-language social scenarios easy to instantiate, but their outputs can be overread as predictions and are often difficult to compare with explicit social dynamics. We present AgoraSim, a hybrid agent-based modeling framework for scenario-oriented social reaction analysis. AgoraSim resolves textual or multimodal artifacts into editable ABM configurations, runs ratio-controlled populations that mix LLM, vision-language, custom-endpoint, random, and classical agents, and compares the same scenario against matched classical reference dynamics. All agents emit a shared structured decision object, enabling common action spaces, interaction protocols, metrics, and audit records. Exposed through a local UI, Python SDK/CLI, and REST API, AgoraSim helps users inspect scenario trajectories, compare modeling assumptions, and identify cases that warrant empirical validation.

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