Democracy research faces a longstanding experimentation bottleneck. Potential institutional innovations remain untested because human-subject studies are slow, expensive, and ethically fraught. This paper argues that digital homunculi, that is, GenAI-powered agents role-playing humans in diverse institutional settings, could offer a way to break through the bottleneck. In contrast to the legacy agent-based modeling, building complexity from transparent simple rules, the digital homunculi methodology aims to extract latent human behavioral knowledge from opaque large language models. To this ends, it designs multi-agent interactions as elicitation devices to trigger in LLMs human-like behavior that can be recorded as synthetic data. However, the validity of synthetic data remains an open question. Success requires that accurate, coherent, transferable models of humans ('little humans' - homunculi) already lurk within GenAI's inscrutable matrices and can be lured out via the social simulation role-play exercise. At the same time, to the extent these attempts are successful, they promise to completely transform the political economy of institutional research from scarcity to abundance. To help mitigate the number of challenges along the way to such success, I propose concrete validation strategies including behavioral back-testing via knowledge cutoffs, and outline infrastructure requirements for rigorous evaluation. The stakes are high: legacy democratic institutions develop at much slower pace than the surrounding technological landscape. If they falter, we lack a repository of tested backup alternatives. Breaking through the experimentation bottleneck must be a priority and digital homunculi may be quickly maturing into a methodology capable of achieving this feat.

翻译：民主研究长期面临实验瓶颈问题。由于人类受试者研究进展缓慢、成本高昂且伦理风险突出，潜在制度创新方案往往难以得到验证。本文提出，数字拟人化——即由生成式人工智能驱动的、在不同制度环境中模拟人类行为的智能体——可能为突破这一瓶颈提供路径。与传统基于智能体建模通过透明简单规则构建复杂性的方法不同，数字拟人化方法论旨在从黑箱式大语言模型中提取潜在的人类行为知识。为此，该方法将多智能体交互设计为诱导机制，以激发大语言模型中类人行为的生成，并将其记录为合成数据。然而，合成数据的有效性仍是悬而未决的问题。该方法成功的前提在于：生成式人工智能的隐晦参数矩阵中必须已存在准确、连贯、可迁移的人类行为模型（即"微型人类"——拟人化智能体），并能通过社会模拟角色扮演实验被有效激发。与此同时，若此类尝试取得成功，将彻底改变制度研究的政治经济学格局——从资源稀缺转向资源富集。为应对实现这一目标过程中的诸多挑战，本文提出具体验证策略，包括基于知识截止点的行为回溯测试，并概述了严格评估所需的基础设施要求。当前形势紧迫：传统民主制度的发展速度远滞后于日新月异的技术环境。若现有制度失效，我们将缺乏经过验证的备用替代方案库。突破实验瓶颈已成为当务之急，而数字拟人化方法正快速发展为可能实现这一突破的关键方法论。