Beyond Isolated Investor: Predicting Startup Success via Roleplay-Based Collective Agents

TL;DR

This paper introduces SimVC-CAS, a multi-agent system that models venture capital decision-making as a group process, significantly improving startup success prediction by capturing collective dynamics and investor network effects.

Contribution

The paper presents a novel multi-agent framework with role-playing investors and GNN-based interaction modeling, enhancing predictive accuracy over traditional single-agent approaches.

Findings

Achieves approximately 25% relative improvement in average precision@10.

Effectively models network-central startups, highlighting network importance.

Provides interpretable insights into investor decision influences.

Abstract

Due to the high value and high failure rates of startups, predicting their success is a critical challenge. Existing approaches typically model startup success from a single decision-maker's perspective, overlooking the collective dynamics that dominate real-world venture capital (VC) decision-making. We propose SimVC-CAS, a collective agent system that simulates VC decisions 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 investor network dynamics. Each agent represents an investor with distinct traits and preferences, enabling heterogeneous evaluations and realistic information exchange over a graph-structured co-investment network. Using both proprietary and public VC data with strict anti-leakage controls, we show that SimVC-CAS significantly improves predictive performance, achieving approximately 25% relative improvement in average precision@10, while exhibiting consistency with real investor decisions. The interaction mechanism is particularly effective for network-central startups, confirming the importance of network in VC decision-making. Analysis of agents' reasoning for decision changes further reveals how network environment influence decision quality, demonstrating the system's interpretability. Our approach may generalize to broader group decision-making scenarios.