Probabilistic knowledge /:
Sarah Moss argues that in addition to full beliefs, credences can constitute knowledge. She introduces the notion of probabilistic content and shows how it plays a central role not only in epistemology, but in the philosophy of mind and language. Just you can believe and assert propositions, you can...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Oxford :
Oxford University Press,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Sarah Moss argues that in addition to full beliefs, credences can constitute knowledge. She introduces the notion of probabilistic content and shows how it plays a central role not only in epistemology, but in the philosophy of mind and language. Just you can believe and assert propositions, you can believe and assert probabilistic contents. |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780192510587 0192510584 9780191861260 019186126X |
Internformat
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| 245 | 1 | 0 | |a Probabilistic knowledge / |c Sarah Moss. |
| 264 | 1 | |a Oxford : |b Oxford University Press, |c 2018. | |
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed February 19, 2018). | |
| 505 | 0 | |a Cover; Probabilistic Knowledge; Copyright; Dedication; Contents; Preface; 1: The case for probabilistic contents; 1.1 Probabilistic beliefs; 1.2 An argument for probabilistic contents of belief; 1.3 The roles played by contents of belief; 1.4 Full beliefs; 1.5 Alternative roles for contents of belief; 2: The case for probabilistic assertion; 2.1 Familiar arguments against propositional contents of assertion; 2.2 Foundational arguments for probabilistic contents of assertion; 2.3 Modeling communication; 2.4 Epistemic modals and indicative conditionals. | |
| 505 | 8 | |a 2.5 A test battery for probabilistic content3: Epistemic modals and probability operators; 3.1 Motivations for my semantics; 3.2 Embedded epistemic vocabulary; 3.3 Challenges for other theories; 3.4 A semantics for epistemic modals and probability operators; 3.5 A semantics for simple sentences; 3.6 The relationship between credence and full belief; 4: Indicative conditionals; 4.1 Probabilities of conditionals as conditional probabilities; 4.2 A semantics for conditionals; 4.3 Why probabilities of conditionals are not conditional probabilities; 4.4 A semantics for other logical operators. | |
| 505 | 8 | |a 4.5 The pragmatics of epistemic vocabulary5: The case for probabilistic knowledge; 5.1 The thesis that probabilistic beliefs can be knowledge; 5.2 Testimony; 5.3 Perception; 5.4 Arguments for probabilistic contents of experience; 5.5 Other sources of knowledge; 5.6 Justified true belief without knowledge; 5.7 Traditional theories of knowledge; 5.8 An alternative mental state?; 5.9 Applications; 6: Factivity; 6.1 Alternatives to probabilistic knowledge?; 6.2 The contents of knowledge ascriptions; 6.3 Frequently asked questions; 6.4 Relativism; 6.5 Objective chance; 7: Skepticism. | |
| 505 | 8 | |a 7.1 A skeptical puzzle7.2 The argument from inconsistency; 7.3 The argument from closure; 7.4 The argument from disjunction; 7.5 The argument from safety; 8: Knowledge and belief; 8.1 The knowledge norm of belief; 8.2 Peer disagreement; 8.3 Applying the knowledge norm of belief; 8.4 Statistical inference; 8.5 Responses to skepticism about perceptual knowledge; 9: Knowledge and action; 9.1 Knowledge norms of action; 9.2 Addressing objections; 9.3 Applying knowledge norms of action; 9.4 Pragmatic encroachment; 9.5 Transformative experience; 10: Knowledge and persons; 10.1 Statistical evidence. | |
| 505 | 8 | |a 10.2 An account of legal proof10.3 Applying knowledge standards of proof; 10.4 Racial and other profiling; 10.5 Applying the rule of consideration; Appendix: A formal semantics for epistemic vocabulary; A.1 Background; A.2 Epistemic modals and probability operators; A.3 Simple sentences; A.4 Indicative conditionals; A.5 Other logical operators; References; Index. | |
| 520 | |a Sarah Moss argues that in addition to full beliefs, credences can constitute knowledge. She introduces the notion of probabilistic content and shows how it plays a central role not only in epistemology, but in the philosophy of mind and language. Just you can believe and assert propositions, you can believe and assert probabilistic contents. | ||
| 650 | 0 | |a Probabilities. |0 http://id.loc.gov/authorities/subjects/sh85107090 | |
| 650 | 0 | |a Logic, Symbolic and mathematical. |0 http://id.loc.gov/authorities/subjects/sh85078115 | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 2 | |a Probability |0 https://id.nlm.nih.gov/mesh/D011336 | |
| 650 | 2 | |a Artificial Intelligence |0 https://id.nlm.nih.gov/mesh/D001185 | |
| 650 | 6 | |a Probabilités. | |
| 650 | 6 | |a Logique symbolique et mathématique. | |
| 650 | 6 | |a Intelligence artificielle. | |
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| author | Moss, Sarah |
| author_GND | http://id.loc.gov/authorities/names/nr2002001357 |
| author_facet | Moss, Sarah |
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| contents | Cover; Probabilistic Knowledge; Copyright; Dedication; Contents; Preface; 1: The case for probabilistic contents; 1.1 Probabilistic beliefs; 1.2 An argument for probabilistic contents of belief; 1.3 The roles played by contents of belief; 1.4 Full beliefs; 1.5 Alternative roles for contents of belief; 2: The case for probabilistic assertion; 2.1 Familiar arguments against propositional contents of assertion; 2.2 Foundational arguments for probabilistic contents of assertion; 2.3 Modeling communication; 2.4 Epistemic modals and indicative conditionals. 2.5 A test battery for probabilistic content3: Epistemic modals and probability operators; 3.1 Motivations for my semantics; 3.2 Embedded epistemic vocabulary; 3.3 Challenges for other theories; 3.4 A semantics for epistemic modals and probability operators; 3.5 A semantics for simple sentences; 3.6 The relationship between credence and full belief; 4: Indicative conditionals; 4.1 Probabilities of conditionals as conditional probabilities; 4.2 A semantics for conditionals; 4.3 Why probabilities of conditionals are not conditional probabilities; 4.4 A semantics for other logical operators. 4.5 The pragmatics of epistemic vocabulary5: The case for probabilistic knowledge; 5.1 The thesis that probabilistic beliefs can be knowledge; 5.2 Testimony; 5.3 Perception; 5.4 Arguments for probabilistic contents of experience; 5.5 Other sources of knowledge; 5.6 Justified true belief without knowledge; 5.7 Traditional theories of knowledge; 5.8 An alternative mental state?; 5.9 Applications; 6: Factivity; 6.1 Alternatives to probabilistic knowledge?; 6.2 The contents of knowledge ascriptions; 6.3 Frequently asked questions; 6.4 Relativism; 6.5 Objective chance; 7: Skepticism. 7.1 A skeptical puzzle7.2 The argument from inconsistency; 7.3 The argument from closure; 7.4 The argument from disjunction; 7.5 The argument from safety; 8: Knowledge and belief; 8.1 The knowledge norm of belief; 8.2 Peer disagreement; 8.3 Applying the knowledge norm of belief; 8.4 Statistical inference; 8.5 Responses to skepticism about perceptual knowledge; 9: Knowledge and action; 9.1 Knowledge norms of action; 9.2 Addressing objections; 9.3 Applying knowledge norms of action; 9.4 Pragmatic encroachment; 9.5 Transformative experience; 10: Knowledge and persons; 10.1 Statistical evidence. 10.2 An account of legal proof10.3 Applying knowledge standards of proof; 10.4 Racial and other profiling; 10.5 Applying the rule of consideration; Appendix: A formal semantics for epistemic vocabulary; A.1 Background; A.2 Epistemic modals and probability operators; A.3 Simple sentences; A.4 Indicative conditionals; A.5 Other logical operators; References; Index. |
| ctrlnum | (OCoLC)1022945279 |
| dewey-full | 519.2 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.2 |
| dewey-search | 519.2 |
| dewey-sort | 3519.2 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| indexdate | 2024-11-27T13:28:13Z |
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| isbn | 9780192510587 0192510584 9780191861260 019186126X |
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| publisher | Oxford University Press, |
| record_format | marc |
| spelling | Moss, Sarah, author. http://id.loc.gov/authorities/names/nr2002001357 Probabilistic knowledge / Sarah Moss. Oxford : Oxford University Press, 2018. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Online resource; title from PDF title page (EBSCO, viewed February 19, 2018). Cover; Probabilistic Knowledge; Copyright; Dedication; Contents; Preface; 1: The case for probabilistic contents; 1.1 Probabilistic beliefs; 1.2 An argument for probabilistic contents of belief; 1.3 The roles played by contents of belief; 1.4 Full beliefs; 1.5 Alternative roles for contents of belief; 2: The case for probabilistic assertion; 2.1 Familiar arguments against propositional contents of assertion; 2.2 Foundational arguments for probabilistic contents of assertion; 2.3 Modeling communication; 2.4 Epistemic modals and indicative conditionals. 2.5 A test battery for probabilistic content3: Epistemic modals and probability operators; 3.1 Motivations for my semantics; 3.2 Embedded epistemic vocabulary; 3.3 Challenges for other theories; 3.4 A semantics for epistemic modals and probability operators; 3.5 A semantics for simple sentences; 3.6 The relationship between credence and full belief; 4: Indicative conditionals; 4.1 Probabilities of conditionals as conditional probabilities; 4.2 A semantics for conditionals; 4.3 Why probabilities of conditionals are not conditional probabilities; 4.4 A semantics for other logical operators. 4.5 The pragmatics of epistemic vocabulary5: The case for probabilistic knowledge; 5.1 The thesis that probabilistic beliefs can be knowledge; 5.2 Testimony; 5.3 Perception; 5.4 Arguments for probabilistic contents of experience; 5.5 Other sources of knowledge; 5.6 Justified true belief without knowledge; 5.7 Traditional theories of knowledge; 5.8 An alternative mental state?; 5.9 Applications; 6: Factivity; 6.1 Alternatives to probabilistic knowledge?; 6.2 The contents of knowledge ascriptions; 6.3 Frequently asked questions; 6.4 Relativism; 6.5 Objective chance; 7: Skepticism. 7.1 A skeptical puzzle7.2 The argument from inconsistency; 7.3 The argument from closure; 7.4 The argument from disjunction; 7.5 The argument from safety; 8: Knowledge and belief; 8.1 The knowledge norm of belief; 8.2 Peer disagreement; 8.3 Applying the knowledge norm of belief; 8.4 Statistical inference; 8.5 Responses to skepticism about perceptual knowledge; 9: Knowledge and action; 9.1 Knowledge norms of action; 9.2 Addressing objections; 9.3 Applying knowledge norms of action; 9.4 Pragmatic encroachment; 9.5 Transformative experience; 10: Knowledge and persons; 10.1 Statistical evidence. 10.2 An account of legal proof10.3 Applying knowledge standards of proof; 10.4 Racial and other profiling; 10.5 Applying the rule of consideration; Appendix: A formal semantics for epistemic vocabulary; A.1 Background; A.2 Epistemic modals and probability operators; A.3 Simple sentences; A.4 Indicative conditionals; A.5 Other logical operators; References; Index. Sarah Moss argues that in addition to full beliefs, credences can constitute knowledge. She introduces the notion of probabilistic content and shows how it plays a central role not only in epistemology, but in the philosophy of mind and language. Just you can believe and assert propositions, you can believe and assert probabilistic contents. Probabilities. http://id.loc.gov/authorities/subjects/sh85107090 Logic, Symbolic and mathematical. http://id.loc.gov/authorities/subjects/sh85078115 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Probability https://id.nlm.nih.gov/mesh/D011336 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Probabilités. Logique symbolique et mathématique. Intelligence artificielle. probability. aat artificial intelligence. aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Artificial intelligence fast Logic, Symbolic and mathematical fast Probabilities fast has work: Probabilistic knowledge (Text) https://id.oclc.org/worldcat/entity/E39PCGpfTx9PyH77JRVMyXcPpP https://id.oclc.org/worldcat/ontology/hasWork Print version : 9780198792154 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1708622 Volltext |
| spellingShingle | Moss, Sarah Probabilistic knowledge / Cover; Probabilistic Knowledge; Copyright; Dedication; Contents; Preface; 1: The case for probabilistic contents; 1.1 Probabilistic beliefs; 1.2 An argument for probabilistic contents of belief; 1.3 The roles played by contents of belief; 1.4 Full beliefs; 1.5 Alternative roles for contents of belief; 2: The case for probabilistic assertion; 2.1 Familiar arguments against propositional contents of assertion; 2.2 Foundational arguments for probabilistic contents of assertion; 2.3 Modeling communication; 2.4 Epistemic modals and indicative conditionals. 2.5 A test battery for probabilistic content3: Epistemic modals and probability operators; 3.1 Motivations for my semantics; 3.2 Embedded epistemic vocabulary; 3.3 Challenges for other theories; 3.4 A semantics for epistemic modals and probability operators; 3.5 A semantics for simple sentences; 3.6 The relationship between credence and full belief; 4: Indicative conditionals; 4.1 Probabilities of conditionals as conditional probabilities; 4.2 A semantics for conditionals; 4.3 Why probabilities of conditionals are not conditional probabilities; 4.4 A semantics for other logical operators. 4.5 The pragmatics of epistemic vocabulary5: The case for probabilistic knowledge; 5.1 The thesis that probabilistic beliefs can be knowledge; 5.2 Testimony; 5.3 Perception; 5.4 Arguments for probabilistic contents of experience; 5.5 Other sources of knowledge; 5.6 Justified true belief without knowledge; 5.7 Traditional theories of knowledge; 5.8 An alternative mental state?; 5.9 Applications; 6: Factivity; 6.1 Alternatives to probabilistic knowledge?; 6.2 The contents of knowledge ascriptions; 6.3 Frequently asked questions; 6.4 Relativism; 6.5 Objective chance; 7: Skepticism. 7.1 A skeptical puzzle7.2 The argument from inconsistency; 7.3 The argument from closure; 7.4 The argument from disjunction; 7.5 The argument from safety; 8: Knowledge and belief; 8.1 The knowledge norm of belief; 8.2 Peer disagreement; 8.3 Applying the knowledge norm of belief; 8.4 Statistical inference; 8.5 Responses to skepticism about perceptual knowledge; 9: Knowledge and action; 9.1 Knowledge norms of action; 9.2 Addressing objections; 9.3 Applying knowledge norms of action; 9.4 Pragmatic encroachment; 9.5 Transformative experience; 10: Knowledge and persons; 10.1 Statistical evidence. 10.2 An account of legal proof10.3 Applying knowledge standards of proof; 10.4 Racial and other profiling; 10.5 Applying the rule of consideration; Appendix: A formal semantics for epistemic vocabulary; A.1 Background; A.2 Epistemic modals and probability operators; A.3 Simple sentences; A.4 Indicative conditionals; A.5 Other logical operators; References; Index. Probabilities. http://id.loc.gov/authorities/subjects/sh85107090 Logic, Symbolic and mathematical. http://id.loc.gov/authorities/subjects/sh85078115 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Probability https://id.nlm.nih.gov/mesh/D011336 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Probabilités. Logique symbolique et mathématique. Intelligence artificielle. probability. aat artificial intelligence. aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Artificial intelligence fast Logic, Symbolic and mathematical fast Probabilities fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85107090 http://id.loc.gov/authorities/subjects/sh85078115 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D011336 https://id.nlm.nih.gov/mesh/D001185 |
| title | Probabilistic knowledge / |
| title_auth | Probabilistic knowledge / |
| title_exact_search | Probabilistic knowledge / |
| title_full | Probabilistic knowledge / Sarah Moss. |
| title_fullStr | Probabilistic knowledge / Sarah Moss. |
| title_full_unstemmed | Probabilistic knowledge / Sarah Moss. |
| title_short | Probabilistic knowledge / |
| title_sort | probabilistic knowledge |
| topic | Probabilities. http://id.loc.gov/authorities/subjects/sh85107090 Logic, Symbolic and mathematical. http://id.loc.gov/authorities/subjects/sh85078115 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Probability https://id.nlm.nih.gov/mesh/D011336 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Probabilités. Logique symbolique et mathématique. Intelligence artificielle. probability. aat artificial intelligence. aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Artificial intelligence fast Logic, Symbolic and mathematical fast Probabilities fast |
| topic_facet | Probabilities. Logic, Symbolic and mathematical. Artificial intelligence. Probability Artificial Intelligence Probabilités. Logique symbolique et mathématique. Intelligence artificielle. probability. artificial intelligence. MATHEMATICS Applied. MATHEMATICS Probability & Statistics General. Artificial intelligence Logic, Symbolic and mathematical Probabilities |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1708622 |
| work_keys_str_mv | AT mosssarah probabilisticknowledge |