Needs, Resources, Goods, and Allocation Mechanisms in a Post-Labor Economy

This paper develops a conceptual and formal framework for evaluating allocation and governance in a possible post-labor economy, where autonomous systems perform a large share of production, research, infrastructure management, and coordination. It treats near-zero marginal costs, recursive machine improvement, and material abundance as conditional scenarios rather than established forecasts. The central question is normative and institutional: how should an economy be evaluated if its primary purpose is to improve human lives rather than maximize output or automation? The framework connects resource constraints, production feasibility, goods, allocation mechanisms, human-needs fulfillment, and structural power. It proposes a multidimensional assessment dashboard that keeps essential-needs fulfillment, subjective well-being, agency, distributional inequality, and intergenerational sustainability distinct. This is consistent with established work on human needs, capabilities, and well-being measurement, but the proposed dashboard is not itself a validated index. Any scalar welfare function is treated as a contestable modeling and ethical choice, not an objective measure of human worth. The paper compares market-based, algorithmic, and hybrid allocation through a worked analytical example and a reproducible synthetic experiment (30 agents per trial, 500 trials, fixed random seed). In this stylized experiment, the needs-weighted optimizer attains the highest value of its specified aggregate objective but leaves the lowest minimum fulfillment; a hybrid minimum-access constraint improves the worst-served outcome and reduces fulfillment inequality. These are model-contingent demonstrations, not empirical estimates, market-equilibrium results, or universal rankings. The paper’s contribution is an integrative research framework, explicit comparison protocol, and reproducible proof of concept—not a claim that post-labor abundance is inevitable or that the framework has already been empirically validated.