Beyond the Cognitive Horizon: Formalizing the Recursive Systems Causal Decision Model and Its Extension to Recursive Metaphysical Dynamics

This paper introduces the Recursive Systems Causal Decision Model (RSCDM), a theoretical and computational framework for examining how human and AI-augmented decision-makers reason within complex organizational systems. RSCDM integrates system boundaries, internal and external variables, observational limits, interpretation, prior experience, learned behavior, correlation, causation, hidden variables, reciprocal and asymmetrical relationships, hypothesis testing, intervention, artificial intelligence augmentation, feedback, and recursive learning. Two central constructs organize the framework. The Cognitive Horizon describes the point at which relevant system complexity exceeds an observer’s effective capacity to integrate the relationships necessary for decision-making. The Firmament Boundary describes the observer-relative limit separating presently accessible information from information that exists within the focal system but remains hidden, unobserved, inferable, or conceptually unavailable. The paper formally represents RSCDM using mathematical notation and derives propositions concerning boundary relativity, causal inference, informational access, finite cognitive capacity, conditional AI augmentation, reciprocal influence, belief revision, and recursive decision processes. A synthetic validation study involving 240 simulated decision-makers and 1,920 repeated organizational decision trials is included to test whether the proposed framework generates coherent, discriminating, and falsifiable predictions. The simulation examines system complexity, observational access, AI assistance, AI-task fit, relational structure, false causal attribution, and belief revision. The synthetic results are presented as model validation rather than empirical evidence from human participants. The paper then extends RSCDM into Recursive Metaphysical Dynamics (RMD), a broader metatheoretical framework examining nested systems of entities, fields, interactions, constraints, transformations, thresholds, singularities, clusters, emergent wholes, and observer-relative cognitive horizons. RSCDM serves as the bounded and empirically testable layer of this larger theoretical architecture. The central argument is that greater information does not automatically produce greater understanding. Effective reasoning requires the ability to distinguish observation from interpretation, correlation from causation, prediction from explanation, and computational augmentation from justified reliance. The framework is intended to support future empirical research in organizational leadership, human-AI decision-making, causal reasoning, complex systems, cognitive augmentation, and the study of recursive epistemic and metaphysical structures. DOI: 10.5281/zenodo.21879539