Digital banking and online communication have made modern bank runs faster and more networked than the canonical queue-at-the-branch setting. While equilibrium models explain why strategic complementarities generate run risk, they offer limited guidance on how beliefs synchronize and propagate in real time. We develop a process-based agent-based model that makes the information and coordination layer explicit. Banks follow cash-first withdrawal processing with discounted fire-sale liquidation and an endogenous stress index. Depositors are heterogeneous in risk tolerance and in the weight placed on fundamentals versus social information, communicating on a heavy-tailed network calibrated to Twitter activity during March 2023. Depositor behavior is generated by a constrained large language model that maps each agent's information set into a discrete action and an optional post; we validate this policy against laboratory coordination evidence and theoretical benchmarks. Across 4,900 configurations and full LLM simulations, three findings emerge. Within-bank connectivity raises the likelihood and speed of withdrawal cascades holding fundamentals fixed. Cross-bank contagion exhibits a sharp phase transition near spillover rates of 0.10. Depositor overlap and network amplification interact nonlinearly, so channels weak in isolation become powerful in combination. In an SVB, First Republic, and regional bank scenario disciplined by crisis-era data, the model reproduces the observed ordering of failures and predicts substantially higher withdrawal rates among uninsured depositors. The results frame social correlation as a measurable amplifier of run risk alongside balance-sheet fundamentals.

翻译：数字银行与在线通信使得现代银行挤兑比传统分行排队场景更为迅速且网络化。尽管均衡模型解释了战略互补性如何产生挤兑风险，但其对信念如何实时同步与传播的指导有限。本文开发了一个基于过程的智能体模型，显式刻画信息与协调层。银行遵循现金优先的取款处理规则，辅以折价抛售清算机制与内生压力指数。储户在风险承受能力、基本面信息与社会信息权重方面存在异质性，并基于符合2023年3月推特活动特征的重尾网络进行通信。储户行为由受约束的大型语言模型生成，该模型将每个智能体的信息集映射为离散行动及可选发帖；我们通过实验室协调证据与理论基准验证了该策略。通过对4,900种配置的完整LLM模拟，得出三项核心发现：在基本面固定的条件下，银行内部网络连接会提高取款级联发生的概率与速度；银行间传染在溢出率接近0.10时呈现急剧的相变；储户网络重叠与网络放大效应存在非线性交互，导致孤立状态下较弱的传播渠道在组合中变得强效。在基于危机时期数据构建的硅谷银行、第一共和银行及区域性银行情景中，模型复现了观察到的银行倒闭顺序，并预测未投保储户的取款率显著更高。研究结果表明，社会关联性是可量化的挤兑风险放大器，其影响与资产负债表基本面并存。