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

After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple proposed the Raven Paradox. Then, Carnap used the increment of logical probability as the confirmation measure....

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Main Author: Chenguang Lu
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Entropy
Subjects:
Online Access:https://www.mdpi.com/1099-4300/22/4/384
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spelling doaj-ea23499e17b04208903bd9f89c2740082020-11-25T01:37:46ZengMDPI AGEntropy1099-43002020-03-0122438410.3390/e22040384e22040384Channels’ Confirmation and Predictions’ Confirmation: From the Medical Test to the Raven ParadoxChenguang Lu0Intelligence Engineering and Mathematics Institute, Liaoning Technical University, Fuxin 123000, ChinaAfter long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple proposed the Raven Paradox. Then, Carnap used the increment of logical probability as the confirmation measure. So far, many confirmation measures have been proposed. Measure <i>F</i> proposed by Kemeny and Oppenheim among them possesses symmetries and asymmetries proposed by Elles and Fitelson, monotonicity proposed by Greco et al<i>.</i>, and normalizing property suggested by many researchers. Based on the semantic information theory, a measure <i>b</i>* similar to<i> F </i>is derived from the medical test. Like the likelihood ratio, measures <i>b</i>* and<i> F</i> can only indicate the quality of channels or the testing means instead of the quality of probability predictions. Furthermore, it is still not easy to use <i>b</i>*, <i>F</i>, or another measure to clarify the Raven Paradox. For this reason, measure<i> c</i>* similar to the correct rate is derived. Measure <i>c</i>* supports the Nicod Criterion and undermines the Equivalence Condition, and hence, can be used to eliminate the Raven Paradox. An example indicates that measures <i>F</i> and <i>b</i>* are helpful for diagnosing the infection of Novel Coronavirus, whereas most popular confirmation measures are not. Another example reveals that all popular confirmation measures cannot be used to explain that a black raven can confirm &#8220;Ravens are black&#8221; more strongly than a piece of chalk. Measures <i>F</i>, <i>b</i>*, and <i>c</i>* indicate that the existence of fewer counterexamples is more important than more positive examples&#8217; existence, and hence, are compatible with Popper&#8217;s falsification thought.https://www.mdpi.com/1099-4300/22/4/384relative entropycross-entropyuncertain reasoninginductive logicconfirmation measuresemantic informationmedical testraven paradox
collection DOAJ
language English
format Article
sources DOAJ
author Chenguang Lu
spellingShingle Chenguang Lu
Channels’ Confirmation and Predictions’ Confirmation: From the Medical Test to the Raven Paradox
Entropy
relative entropy
cross-entropy
uncertain reasoning
inductive logic
confirmation measure
semantic information
medical test
raven paradox
author_facet Chenguang Lu
author_sort Chenguang Lu
title Channels’ Confirmation and Predictions’ Confirmation: From the Medical Test to the Raven Paradox
title_short Channels’ Confirmation and Predictions’ Confirmation: From the Medical Test to the Raven Paradox
title_full Channels’ Confirmation and Predictions’ Confirmation: From the Medical Test to the Raven Paradox
title_fullStr Channels’ Confirmation and Predictions’ Confirmation: From the Medical Test to the Raven Paradox
title_full_unstemmed Channels’ Confirmation and Predictions’ Confirmation: From the Medical Test to the Raven Paradox
title_sort channels’ confirmation and predictions’ confirmation: from the medical test to the raven paradox
publisher MDPI AG
series Entropy
issn 1099-4300
publishDate 2020-03-01
description After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple proposed the Raven Paradox. Then, Carnap used the increment of logical probability as the confirmation measure. So far, many confirmation measures have been proposed. Measure <i>F</i> proposed by Kemeny and Oppenheim among them possesses symmetries and asymmetries proposed by Elles and Fitelson, monotonicity proposed by Greco et al<i>.</i>, and normalizing property suggested by many researchers. Based on the semantic information theory, a measure <i>b</i>* similar to<i> F </i>is derived from the medical test. Like the likelihood ratio, measures <i>b</i>* and<i> F</i> can only indicate the quality of channels or the testing means instead of the quality of probability predictions. Furthermore, it is still not easy to use <i>b</i>*, <i>F</i>, or another measure to clarify the Raven Paradox. For this reason, measure<i> c</i>* similar to the correct rate is derived. Measure <i>c</i>* supports the Nicod Criterion and undermines the Equivalence Condition, and hence, can be used to eliminate the Raven Paradox. An example indicates that measures <i>F</i> and <i>b</i>* are helpful for diagnosing the infection of Novel Coronavirus, whereas most popular confirmation measures are not. Another example reveals that all popular confirmation measures cannot be used to explain that a black raven can confirm &#8220;Ravens are black&#8221; more strongly than a piece of chalk. Measures <i>F</i>, <i>b</i>*, and <i>c</i>* indicate that the existence of fewer counterexamples is more important than more positive examples&#8217; existence, and hence, are compatible with Popper&#8217;s falsification thought.
topic relative entropy
cross-entropy
uncertain reasoning
inductive logic
confirmation measure
semantic information
medical test
raven paradox
url https://www.mdpi.com/1099-4300/22/4/384
work_keys_str_mv AT chenguanglu channelsconfirmationandpredictionsconfirmationfromthemedicaltesttotheravenparadox
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