AI-Driven Pathways to Human Happiness: Algorithmic Architectures for Thriving Beyond Work in the Age of Humanoid Automation

Abstract

The rise of artificial intelligence and humanoid automation presages a profound shift in socioeconomic structures, necessitating novel computational frameworks for human flourishing beyond traditional employment. This article formalizes algorithmic architectures across three interconnected domains: adaptive resource redistribution systems utilizing gradient descent to optimize universal basic income parameters; personalized learning platforms employing ant colony optimization to navigate educational knowledge graphs; and human-AI collaborative frameworks that preserve creative agency through structured workflows and explainable AI. Each domain is mathematically formalized with rigorous computational models, optimization techniques, and evaluation metrics designed to operationalize human thriving in post-work contexts. Implementation considerations address computational social science benchmarking, real-time adaptation mechanisms, ethical alignment strategies, and integration pathways for large-scale deployment. The formalized algorithmic foundations presented establish a rigorous basis for developing socio-technical systems that enable meaningful human participation, equitable resource distribution, and continuous learning in societies characterized by widespread automation and artificial intelligence.