This study presents a general operator-based mathematical conflict framework that explicitly defines the structural mismatches between raw data and contextual data. The proposed structure treats conflict as a local, directional, and context-sensitive quantity, unifying components such as weighting, scale behavior, and output mapping under a single abstract operator. The framework is defined as a general structure adaptable to different problem classes, without being reduced to a specific learning algorithm or optimization method. Whereas existing approaches treat conflict only as an implicit side effect embedded within the optimization process, the proposed framework handles conflict as an independent, operator-based mathematical object defined at the component level.
This notebook visualizes the geometric behavior of the three conflict operators proposed in the Conflict Framework paper and numerically checks:
- Zero-conflict consistency: ( g(x,x)=0 )
- Antisymmetry: ( g(x,y) = -g(y,x) )
- Grid-based local variation: no abrupt numerical jumps on the sampled positive domain
The notebook is organized into small, purposeful cells so each stage of the experiment is transparent and reproducible.
