A Mathematical Conflict Framework for Contextual Data Modulation
For researchers in data fusion or context-aware systems, this provides a formal structure for conflict representation, but it remains theoretical without empirical validation.
The paper proposes a generalized mathematical framework to explicitly represent structural discrepancies between raw and contextual data, treating conflict as an independent, operator-based quantity rather than an implicit side effect of optimization.
In this study, a generalized operator-based mathematical conflict framework is presented to explicitly represent structural discrepancies between raw data and contextual data. The proposed structure treats conflict as a local, directional, and context-sensitive quantity, integrating components such as weighting, scale behavior, and output mapping under a unified abstract operator. Without being reduced to a specific learning algorithm or optimization method, the framework is defined as a general structure adaptable to different classes of problems. While existing approaches typically treat conflict merely as an implicit side effect embedded within the optimization process, the proposed framework considers conflict as an independent, operator-based, and component-level mathematical object.