Philosophy Dictionary of ArgumentsHome | |||
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Models, philosophy, logic: A model is obtained when a logical formula provides true statements by inserting objects instead of the free variables. One problem is the exclusion of unintended models. See also model theory._____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. | |||
Author | Concept | Summary/Quotes | Sources |
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Judea Pearl on Models - Dictionary of Arguments
Brockman I 17 Models/Pearl: once we encode our scientific knowledge in a model (which may be qualitative), algorithms exist that examine the model and determine if a given query, be it about an intervention or about a counterfactual, can be estimated from the available data—and, if so, how. >Counterfactuals/Pearl, >Machine learning/Pearl. This capability has dramatically transformed the way scientists are doing science, especially in such data-intensive sciences as sociology and epidemiology, for which causal Brockman I 18 models have become a second language. These disciplines view their linguistic transformation as the Causal Revolution. ((s) For Problems in relation to causality cf. >Causality/Philosophy.) Pearl, Judea.”The Limitations of Opaque Learning Machines.” in: Brockman, John (ed.) 2019. Twenty-Five Ways of Looking at AI. New York: Penguin Press._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition. |
Pearl, Judea Brockman I John Brockman Possible Minds: Twenty-Five Ways of Looking at AI New York 2019 |