DIArc Foundational Note v0.1 — Minimum Claim Edition

Abstract

Abstract The rapid development of artificial intelligence has significantly increased the availability of information, analytical capability, and machine-assisted reasoning. However, greater access to information does not necessarily produce better decisions. In many organizational contexts, the emerging bottleneck is no longer information acquisition, but the human and organizational capacity to determine what information is sufficient, when analysis should stop, when a decision should be made, and how outcomes should improve future judgment. This Foundational Note introduces Decision Intelligence Architecture (DIArc) as an architectural framework for Human–AI collaborative decision systems. DIArc is based on a central proposition: in the AI era, competitive advantage increasingly depends not on maximizing information, but on maximizing the rate at which high-quality decisions generate learning and improve judgment, under explicit constraints on information consumption and decision cycles. The architecture is organized into four theoretical layers. First, the Capability Inversion Hypothesis describes a structural shift in which information, knowledge, and analysis become increasingly abundant while judgment, commitment, execution, and learning become comparatively scarce capabilities. Second, Identity-driven Information Consumption (IDIC) describes a decision failure mechanism in which continued information consumption may serve identity reinforcement rather than decision improvement. Third, the Decision Constraint Architecture, comprising Decision Information Budget (DIB) and Decision Cycle Budget (DCB), introduces explicit constraints on information consumption and analytical iteration. Fourth, High-quality Decision Velocity (HQDV) describes the performance objective of accelerating completed high-quality decision loops, while Judgment Evolution Rate (JER) represents the longer-term evolutionary objective of improving judgment through outcome-based learning. This note constitutes the initial public disclosure of the DIArc architecture and establishes its theoretical baseline for subsequent research and branch concepts.