Framework develops decision architecture principles including selectors and claim calibration for various fields, suggesting implications for data-driven decisions.
Key Points
This framework aims to establish a mathematical approach to understand the role of numerical selectors in decision systems.
Develops a mathematical framework representing decision architecture and selectors.
Defines material and hidden selectors along with concepts like provenance graphs and architecture sensitivity.
Proposes formal propositions to address interactions and stability of selectors.
Establishes that both explicit and hidden selectors have equal provenance burden.
Demonstrates that threshold decisions may be unstable despite precise values.
Shows that operational reproducibility does not guarantee architecture independence.