Economic Governance of Autonomous Agents and Robots: Factor-Origin Accounting and Social Automation Funds

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.