发表机构
AI Center, Faculty of Computer and Information Systems, Islamic University of Madinah; AI V&V Lab, King Fahd University of Petroleum and Minerals; Department of Computer Science, The Islamia University of Bahawalpur（麦地那伊斯兰大学计算机与信息学院人工智能中心; 国王法赫德国油矿大学人工智能验证与验证实验室; 巴哈瓦尔布尔伊斯兰大学计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出 CivicDividendOS 框架，通过要素来源核算和动态社会自动化缴款，解决人机混合经济中的价值归属、财政责任与盈余分配问题，并以城市物流和数字孪生验证。
AI 中文摘要
当代财政架构 overwhelmingly 依赖人类劳动收入和工资预扣来为社会保险和公共基础设施融资。自主软件智能体、生成式基础模型和具身机器人系统的快速普及，使产出增长与人类工作时间脱钩，侵蚀传统税基，同时加剧资本收入集中。现行政策提案，从统一的机器人税到无条件现金转移，均未能解决三个基础性挑战：在混合人机工作流程中归属经济价值、在不创造虚构机器法人格的情况下确立法律财政责任，以及构建一种将技术盈余转化为持久公共财富的公平分配机制。本文提出 CivicDividendOS，一个面向混合人-AI-机器人经济的端到端计算财政框架。该架构引入自主经济活动护照（AEAP），将机器活动锚定到负有责任的法人受益者，并配以要素来源分类账（FOL），用于计算劳动、AI 服务、机器人技术、数据和传统资本之间的因果边际贡献。基于内生替代-增强分类器，系统对自动化增加值征收动态社会自动化缴款（SAC），并为工人技能提升、增强和任务创新提供明确的抵扣额度。累计收入通过人类劳动收入保护盾（HEIS）循环使用，以降低工资税楔，以及公共自动化财富基金（PAWF），其结构性收益用于赋予公民分红、转型保险和人类能力。我们形式化了该数学架构，在城市物流场景中说明其运作，并概述了通过宏观经济政策数字孪生进行评估的方案。
英文摘要
Contemporary fiscal architectures rely overwhelmingly on human labor income and payroll withholdings to finance social insurance and public infrastructure. The rapid diffusion of autonomous software agents, generative foundation models, and embodied robotic systems decouples output growth from human work hours, eroding traditional tax bases while exacerbating capital-income concentration. Prevailing policy proposals, ranging from uniform robot levies to unconditional cash transfers, fail to resolve three foundational challenges: attributing economic value across mixed human-machine workflows, establishing legal fiscal liability without creating fictional machine personhood, and building an equitable distribution mechanism that converts technological surplus into durable public wealth. This paper presents CivicDividendOS, an end-to-end computational fiscal framework for mixed human-AIrobot economies. The architecture introduces an Autonomous Economic Activity Passport (AEAP) that anchors machine activity to accountable corporate beneficiaries, paired with a Factor-Origin Ledger (FOL) that calculates causal marginal contributions across labor, AI services, robotics, data, and conventional capital. Drawing on an endogenous Substitution-Augmentation Classifier, the system levies a dynamic Social Automation Contribution (SAC) on automated value added, offering explicit offset credits for worker upskilling, augmentation, and task innovation. Accrued revenues are recycled through a Human Earned-Income Shield (HEIS) to reduce payroll wedges and a Public Automation Wealth Fund (PAWF) whose structural yields endow citizen dividends, transition insurance, and human capabilities. We formalize the mathematical architecture, illustrate its operation in an urban logistics setting, and outline an evaluation protocol via a macroeconomic policy digital twin.
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