This conceptual theory article develops Algorithmic Bureaucracy as an organizational formation in which administrative decisions acquire authority through the interaction of formal rules, professional judgment, data infrastructures, computational models, technical specialists, managerial priorities, and automated enforcement. No single component is the complete decision-maker, yet the combined arrangement can classify, authorize, reward, deny, punish, preserve, and govern. The article introduces Procedural Fusion, Credentialed Translation, Discretion Migration, Administrative Model Cascades, Decision-Provenance Fracture, Expert–System Mutual Dependence, Quality Metric Capture, Bureaucratic Autonomy Inversion, Institutional Self-Preservation, Professional–Machine Bureaucracy, and the Centerless Authority Threshold. It explains how machine bureaucracy, professional bureaucracy, street-level discretion, system-level administration, and algorithmic systems can become mutually dependent components of a centerless authority structure. Situated within Francisco M. Martinez’s Mythotechnic Fiction, Machine-Institution Fiction, Mythic Authority, and Post-Personal Dark Leadership research program, the article distinguishes legitimate administrative coordination from dark machine institutions. It argues that when rules, experts, and machines jointly exercise authority, responsibility must be organized across the same system that organizes command.
Francisco M. Martinez (Tue,) studied this question.