用AI生成创业者模拟人物，对比真实访谈，验证其在创业验证研究中的有效性。
Synthetic Founders: AI-Generated Social Simulations for Startup Validation Research in Computational Social Science
- 用大语言模型生成虚拟创业者和投资人，与真人访谈对比分析。
- 发现AI模拟能复现真实用户对效率和信任的担忧，但忽略历史创伤等深层问题。
- 适合做创业早期假设探索，补充真实调研的盲区。
我们开展了一项对比性对接实验，将真人初创企业创始人访谈数据与大语言模型（LLM）驱动的虚拟角色数据进行比对，评估人工智能模拟在真实性、差异性和盲点方面的表现。15位早期阶段创业者接受了关于人工智能赋能验证的希望与顾虑访谈，同一方法也应用于由AI生成的创业者与投资人角色。结构化主题合成揭示四类结果：（1）一致主题——承诺型需求信号、黑箱信任障碍与效率提升在两组数据中均被强调；（2）部分重叠——真人关注异常值被平均化及真实客户验证压力，而虚拟角色则突出非理性盲点，并将AI视为心理缓冲；（3）仅人类拥有主题——早期客户互动的亲缘价值与对远大市场的怀疑；（4）仅虚拟角色呈现主题——放大假阳性与创伤盲点，即AI可能因忽视负面历史经验而高估采纳潜力。我们认为，这种对比框架表明，基于LLM的角色构成一种混合社会模拟：语言表达更丰富灵活，但缺乏真实经历与关系后果。它们不替代实证研究，而是作为补充性模拟类型，可拓展假设空间、加速探索性验证，并明确计算社会科学研究中认知真实性的边界。
原文摘要 · Abstract (English)
We present a comparative docking experiment that aligns human-subject interview data with large language model (LLM)-driven synthetic personas to evaluate fidelity, divergence, and blind spots in AI-enabled simulation. Fifteen early-stage startup founders were interviewed about their hopes and concerns regarding AI-powered validation, and the same protocol was replicated with AI-generated founder and investor personas. A structured thematic synthesis revealed four categories of outcomes: (1) Convergent themes - commitment-based demand signals, black-box trust barriers, and efficiency gains were consistently emphasized across both datasets; (2) Partial overlaps - founders worried about outliers being averaged away and the stress of real customer validation, while synthetic personas highlighted irrational blind spots and framed AI as a psychological buffer; (3) Human-only themes - relational and advocacy value from early customer engagement and skepticism toward moonshot markets; and (4) Synthetic-only themes - amplified false positives and trauma blind spots, where AI may overstate adoption potential by missing negative historical experiences. We interpret this comparative framework as evidence that LLM-driven personas constitute a form of hybrid social simulation: more linguistically expressive and adaptable than traditional rule-based agents, yet bounded by the absence of lived history and relational consequence. Rather than replacing empirical studies, we argue they function as a complementary simulation category - capable of extending hypothesis space, accelerating exploratory validation, and clarifying the boundaries of cognitive realism in computational social science.
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