The Projection-Truth Divergence Threshold Theory (PTDTT): A Proposed Integrative Framework for Projection-Truth Discrepancy, Cumulative Processing Load, Threshold Crossing, and Systemic Outcomes
Year: 2026
Type: preprint
Abstract: The Projection-Truth Divergence Threshold Theory (PTDTT) is a proposed framework for analyzing situations in which information classified as true under an explicit verification procedure conflicts with a system's existing projection, expectation, or reference model. Its primary case, the Repeated Truth-Projection Incompatibility Case (RTPI), concerns repeated exposure to such information and the possibility that persistent discrepancy contributes to cumulative processing load. The framework proposes that the resulting response depends not only on discrepancy and repetition, but also on retention, effective processing capacity, model-revision opportunities, and system-specific conditions. The framework separates eight analytical mechanisms: Reference-Model Specification (RSM), Projection-Truth Discrepancy (RER), Recurrent Discrepancy Load (RDL), Effective Processing Capacity (EIN), Threshold Assessment (RAC), Systemic Outcome Differentiation (ROD), Projection Revision (RPF), and Recursive Feedback (RFB). These labels identify roles in the proposed architecture rather than independently established natural laws. A discrete-time mathematical model is specified, with optional continuous-time and multi-agent extensions. The manuscript defines candidate variables, parameter constraints, hypotheses, counterexamples, research designs, and model-comparison requirements. It also distinguishes information truth status from subjective belief and distinguishes threshold crossing from any particular outcome. The central proposal is that, under identifiable conditions, repeated projection-incompatible information may produce cumulative load and heterogeneous systemic responses. Whether this framework provides explanatory advantages over existing models remains an empirical question. No universal threshold, invariant parameter set, or empirically established universal law is claimed. Keywords: projection-truth discrepancy; recurrent processing load; threshold models; system dynamics; cognitive processing; model revision; feedback; falsifiability; multi-agent systems.
Author: Shan, Shamiul Hoque
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225382121