AI 中文总结
该研究提出Animarium开源可复现流程，基于ISTAT等公开数据源生成意大利11个城市的合成人口，流程具字节级确定性，发布的人口数据集可用于LLM驱动的模拟。
AI 中文摘要
仅基于公开汇总数据生成11个意大利自治市的合成人口，共包含887937户中的1814317人，所用数据源包括ISTAT人口普查与登记表格、普查分区计数、全国公民地址登记、公共使用调查微观数据以及6个市政开放数据门户，每个数据源均在注册表中进行认证，包含许可证、指纹和声明的可用性。该流程通过四个环节为每个属性赋予声明位置：最多包含9个人口统计属性的最大熵联合模型；从调查受访者处进行23种态度与健康变量的全向量捐赠；定位至普查分区、单岁年龄及地址；以及受各分区普查规模分布约束的家庭。每个下游层级（详细头衔、工作、姓名、传记）均为声明性派生，不添加任何额外信息。该流程具有字节级确定性：从带标签提交重新生成全部11个自治市，可逐位复现每个环节的所有文件，在单台工作站上耗时33分钟。合成人口以数据强制执行的公开模式发布（无姓名、无地址、坐标在普查分区内随机化），可在Animarium（无依赖的网页查看器）中浏览，每个数值均附带其比较信息，每个视图均为可引用的URL，也可作为开放数据集下载。本报告记录了架构、数据源及其认证、发布标签处的可复现性与质量测量、查看器，以及将记录渲染为适用于大语言模型（LLM）驱动模拟的人物角色的叙事层级，该平台的可控性在配套实验中得到展示，验证工作明确超出本报告范围。
英文摘要
Synthetic populations of eleven Italian municipalities (1,814,317 individuals in 887,937 households) generated from published aggregates alone: ISTAT census and register tables, census-section counts, the national civic-address register, public-use survey microdata, and six municipal open-data portals, every source certified in a registry with licence, fingerprint and declared affordances. Four rings give every attribute a declared place: a maximum-entropy joint model of up to nine demographic attributes; whole-vector donation of twenty-three attitudinal and health variables from survey respondents; placement to census section, single year of age and address; and households constrained by the census size distribution per section. Every downstream layer (detailed titles, work, names, biographies) is a declared derivation adding no information. The pipeline is deterministic to the byte: regenerating all eleven municipalities from the tagged commit reproduces every file of every ring bit for bit, in 33 minutes on one workstation. Populations are released in a public regime enforced in the data (no names, no addresses, coordinates randomised within census section), browsable in Animarium, a dependency-free web viewer where every number carries its comparison and every view is a citable URL, and downloadable as an open dataset. The report documents the architecture, the sources and their certification, the reproducibility and quality measurements at the release tag, the viewer, and the narrative layer that renders records into personas for LLM-driven simulation, with the platform's controllability demonstrated in companion experiments, and validation explicitly out of scope.
