Channels' Confirmation and Predictions' Confirmation: From the Medical Test to the Raven Paradox

Area of Science:

• Philosophy of Science
• Confirmation Theory
• Logical Probability

Background:

• The verification of universal hypotheses evolved from positivism to confirmation of uncertain premises, facing challenges like the Raven Paradox.
• Various confirmation measures have been proposed, including Carnap's logical probability increment and Kemeny and Oppenheim's measure F.
• Existing measures like F and b* (derived from semantic information theory) assess channel quality but struggle with the Raven Paradox.

Purpose of the Study:

• To introduce and evaluate new confirmation measures, specifically c*, designed to resolve the Raven Paradox.
• To demonstrate the utility of measures F and b* in practical applications like disease diagnosis.
• To analyze the compatibility of proposed measures with established philosophical principles like Popper's falsification.

Main Methods:

• Derivation of measure b* from semantic information theory, analogous to medical testing.
• Development of measure c*, inspired by the correct rate, to address limitations of existing measures.
• Application of measures F, b*, and c* to case studies, including Novel Coronavirus diagnosis and the Raven Paradox.

Main Results:

• Measures F and b* show utility in diagnosing Novel Coronavirus infections, outperforming popular measures.
• Measure c* supports the Nicod Criterion and refutes the Equivalence Condition, effectively resolving the Raven Paradox.
• Measures F, b*, and c* emphasize the importance of minimizing counterexamples over accumulating positive instances, aligning with falsification.

Conclusions:

• Measure c* offers a solution to the long-standing Raven Paradox by revising confirmation criteria.
• Measures F and b* provide valuable tools for diagnostic assessments, highlighting their practical relevance.
• The proposed measures align with Popper's falsificationist approach, emphasizing empirical refutation.