P-A CORE: A Four-State Asymmetric Cognitive Model Explaining Hidden Drivers of Human Decision Distortion

Abstract

Traditional two-state decision models remain widely used in system design due to their structural simplicity; however, they frequently overlook the underlying causal processes by focusing primarily on binary outcomes (Booher, 2003). This research argues that outcomes are the inevitable result of repetitive cognitive patterns that binary frameworks misclassify as noise. This paper introduces the P-A CORE, a four-state asymmetric cognitive model consisting of F (Stable Primary), B (Stable Secondary), P (Rare Peak), and A (Asymmetric Collapse). Unlike existing models, the P-A CORE posits that these states are dynamically interconnected: the baseline stability of F influences the deviations in B, which in turn creates the probabilistic conditions for rare peak performance or a catastrophic asymmetric collapse (A) that fundamentally shifts the operational environment. We demonstrate through conceptual derivation and simulation that human decision-making is characterized by these non-linear transitions rather than static probabilities. By shifting the focus from "what" happened to "why" it occurred through this four-state progression, the model provides a structural basis for identifying hidden drivers of decision distortion. The P-A CORE serves not merely as a descriptive tool but as a predictive framework for risk mitigation, allowing system designers to anticipate and preemptively manage high-impact failures. The central argument remains that no two-state model can describe the fidelity of human cognitive fluctuation required for modern safety-critical systems (Folds, D. J., & Seals, K. B., 2008).

Keywords: P-A CORE, Cognitive Modeling, Human Decision-making, Asymmetric Collapse, Risk Mitigation

DOI: 10.54941/ahfe1007559