AI 中文总结
该研究提出嵌入IAD框架的可视分析沙箱SocialFiVis，结合LLM与PRA引擎模拟多智能体，支持探索社会金融领域反事实政策并解释相关涌现现象。
AI 中文摘要
社会金融（SocialFi）的兴起将在线社区转变为复杂的社会经济系统，在这些空间中，集体决策塑造了以社会资本（如社区信任）和金融健康（如市场流动性）为特征的“数字公地”。治理这类混合生态系统颇具挑战性，因为现实世界的干预成本高昂且不可逆。虽然反事实模拟对于探索替代治理策略至关重要，但现有方法无法捕捉治理规则、个体行为与涌现经济结果之间的非线性相互作用。为系统解析这种复杂性，我们将制度分析与发展（IAD）框架作为理论基础，整合了先前文献与初步专家访谈的见解。基于该框架，我们提出了SocialFiVis，这是一个嵌入IAD的可视分析沙箱，它引入了一个强大模型来量化双轨数字公地，并搭配一个两阶段模拟引擎。该引擎将源自大语言模型（LLM）的智能体角色与机制引导的感知-推理-行动（PRA）运行时相结合，以模拟基于保留消息队列的、异质的、情境感知的智能体。带有可解释推理路径的分层多视图界面使社区运营者能够探索反事实政策，并将系统级结果追溯到个体行为的基本原理。我们通过两个案例研究、一项用户研究和后续访谈对SocialFiVis进行评估，结果表明SocialFiVis支持细粒度的行为归因，并有助于解释诸如社会资本的结构脱钩以及消息传递成员在局部治理冲击下的韧性等涌现现象。
英文摘要
The emergence of social finance (SocialFi) transforms online communities into complex socio-economic systems. Within these spaces, collective decisions shape a "digital commons" characterized by social capital (e.g., community trust) and financial health (e.g., market liquidity). Governing such hybrid ecosystems is challenging because real-world interventions are costly and irreversible. While counterfactual simulation is essential for exploring alternative governance strategies, existing approaches fail to capture the non-linear interplay between governance rules, individual behaviors, and emergent economic outcomes. To systematically unpack this complexity, we operationalize the Institutional Analysis and Development (IAD) framework as our theoretical foundation, synthesizing prior literature with insights from formative expert interviews. Built on this framework, we present SocialFiVis, an IAD-embedded visual analytics sandbox. It introduces a robust model to quantify the dual-track digital commons, coupled with a two-phase simulation engine. This engine combines LLM-derived personas with a mechanism-guided Perception-Reasoning-Action (PRA) runtime to simulate heterogeneous, context-aware agents empirically grounded in the retained messaging cohort. A hierarchical multi-view interface with interpretable reasoning pathways enables community operators to explore counterfactual policies and trace system-level outcomes back to individual behavioral rationales. We evaluate SocialFiVis through two case studies, a user study, and follow-up interviews. Results demonstrate that SocialFiVis supports fine-grained behavioral attribution and helps explain emergent phenomena such as the structural decoupling of social capital and the resilience of messaging members under localized governance shocks.
Comments11 pages, 7 figures, 2 tables. Accepted to IEEE VIS 2026; to appear in IEEE Transactions on Visualization and Computer Graphics
