Due to the high value and high failure rate of startups, predicting their success has become a critical challenge across interdisciplinary research. Existing approaches typically model success prediction from the perspective of a single decision-maker, overlooking the collective dynamics of investor groups that dominate real-world venture capital (VC) decisions. In this paper, we propose SimVC-CAS, a novel collective agent system that simulates VC decision-making as a multi-agent interaction process. By designing role-playing agents and a GNN-based supervised interaction module, we reformulate startup financing prediction as a group decision-making task, capturing both enterprise fundamentals and the behavioral dynamics of potential investor networks. Each agent embodies an investor with unique traits and preferences, enabling heterogeneous evaluation and realistic information exchange through a graph-structured co-investment network. Using real-world data from PitchBook and under strict data leakage controls, we show that SimVC-CAS significantly improves predictive accuracy while providing interpretable, multiperspective reasoning, for example, approximately 25% relative improvement with respect to average precision@10. SimVC-CAS also sheds light on other complex group decision scenarios.

翻译：由于初创企业的高价值与高失败率，预测其成功已成为跨学科研究中的关键挑战。现有方法通常从单一决策者的视角建模成功预测，忽视了主导现实世界风险投资决策的投资者群体的集体动态。本文提出 SimVC-CAS，一种新颖的集体代理系统，将风险投资决策模拟为多代理交互过程。通过设计角色扮演代理和一个基于图神经网络的监督交互模块，我们将初创企业融资预测重构为群体决策任务，同时捕捉企业基本面和潜在投资者网络的行为动态。每个代理代表具有独特特质和偏好的投资者，通过图结构的共同投资网络实现异质性评估和真实信息交换。使用 PitchBook 的真实数据并在严格的数据泄露控制下，我们证明 SimVC-CAS 显著提高了预测准确性，同时提供可解释的多视角推理，例如在平均精确率@10 指标上实现约 25% 的相对提升。SimVC-CAS 也为其他复杂群体决策场景提供了启示。