Decision-Path Inverse Reconstruction: Observed Decision Outcomes as Structural Constraints on Latent Decision Paths

Abstract

This working paper proposes Decision-Path Inverse Reconstruction (DPIR), an inverse analytical operation within the Atlas Insight Method (AIM). DPIR treats an observed decision outcome not only as the endpoint of a judgment process, but also as structural information that constrains the set of decision paths capable of producing that outcome. The paper defines the feasible decision-path set, path sufficiency and insufficiency, minimal condition augmentation, and the Structural Residual. It distinguishes DPIR from direct psychological inference: the framework does not infer a unique hidden emotion or mental state from an outcome. Instead, it identifies the condition classes and decision paths that remain structurally compatible with the observed result, then reduces the candidate set through additional evidence and prospective testing. The paper further positions DPIR in relation to revealed preference theory, process-tracing methods, and inverse reinforcement learning; defines DPIR as an inverse analytical operation over the AIM sequence Awareness -> Insight -> Structure -> Translation -> Integration -> Momentum; and proposes initial falsifiability criteria, research hypotheses, and an experimental validation design.