DCP Framework: A Constraint-Based Model for Structured Judgment and Layered Interpretation

Read full text
Authors: Chienheng Chen
Cited by: 0

Abstract

English DCP (Decision / Constraint / Condition-based Process) Framework is a conceptual framework for structured judgment under constraints. It provides a way to describe how interpretations, comparisons, and decisions can be formed when information is incomplete, heterogeneous, or context-dependent. Rather than producing definitive answers, the framework makes explicit the conditions, references, assumptions, and uncertainties that shape a judgment. DCP is not an algorithm, software system, or decision engine. It does not prescribe actions, optimize outcomes, or automate decisions. Instead, it serves as a meta-level structure for reasoning, supporting: explicit condition modeling layered interpretation traceable judgment formation uncertainty-aware analysis cross-domain applicability The framework is organized into conceptual layers: Layer 0 — Core Logic: abstract structure of conditional judgment Layer 1 — Interpretive Structures (Xuanling System): reusable reasoning patterns and schemas Layer 2 — Projection Layer: illustrative domain projections and examples (non-binding) This repository represents an evolving conceptual reference rather than a finalized system or implementation. Applications, implementations, or domain adaptations are intentionally left open. Licensing and use This work is released under Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0). You are free to: read, cite, and reference this work share it in unmodified form with attribution You may not: use it for commercial purposes distribute modified or derivative versions Any commercial use or derivative implementation requires separate authorization from the author. Keywords: constraint-based reasoning, structured judgment, layered interpretation, cross-domain mapping

Fields of Study & Subjects

Constraint Satisfaction and Optimization Bayesian Modeling and Causal Inference AI-based Problem Solving and Planning

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
1 January 2026
Language
ZH