Scientific Understanding and Representation
portes grátis
Scientific Understanding and Representation
Modeling in the Physical Sciences
Khalifa, Kareem; Shech, Elay; Lawler, Insa
Taylor & Francis Ltd
12/2022
20
Dura
Inglês
9781032054957
15 a 20 dias
Descrição não disponível.
1. Introduction, Kareem Khalifa, Insa Lawler, and Elay Shech; Part I: Understanding, Knowledge, and Explanation; 2. Can Scientific Understanding be Reduced to Knowledge?, Henk W. de Regt; 3. Should Friends and Frenemies of Understanding be Friends? Discussing de Regt, Kareem Khalifa; 4. Frenemies or Friends? A Reply to Kareem Khalifa, Henk W. de Regt; 5. Onwards, My Friend! Reply to de Regt, Kareem Khalifa; 6. Factivism in Historical Perspective: Understanding the Gravitational Deflection of Light, Sorin Bangu; 7. Ideal Patterns and Non-Factive Understanding, Mazviita Chirimuuta; 8. Topological Explanations: An Opinionated Appraisal, Daniel Kostic; 9. Explanatory Power: Factive vs. Pragmatic Dimension, Juha Saatsi; Part II: Understanding and Scientific Realism; 10. Understanding the Success of Science, Christopher Pincock; 11. Truth and Reality: How to be a Scientific Realist Without Believing Scientific Theories Should be True, Angela Potochnik; 12. Defensible Scientific Realism: A Reply to Potochnik, Christopher Pincock; 13. Different Ways to be a Realist: A Response to Pincock, Angela Potochnik; 14. Realism About Molecular Structures, Amanda J. Nichols and Myron A. Penner; 15. Anti-Fundamentalist Lessons for Scientific Representation from Scientific Metaphysics, Julia R.S. Bursten; Part III: Understanding, Representation, and Inference; 16. Factivity, Pluralism and the Inferential Account of Scientific Understanding, Jaakko Kuorikoski; 17. Scientific Representation and Understanding: A Communal and Dynamical View, Collin Rice; 18. Representation and Understanding are Constitutively Communal but Not Constitutively Historical, Jaakko Kuorikoski; 19. Which Modal Information and Abilities Are Required for Inferential Understanding?, Collin Rice; 20. Maps, Models, and Representation, James Nguyen and Roman Frigg; 21. DEKI, Denotation, and the Fortuitous Misuse of Maps, Jared Millson and Mark Risjord; 22. DEKI and the Mislocation of Justification: A Response to Millson and Risjord, Roman Frigg and James Nguyen; 23. DEKI and the Justification of Surrogative Inference: A Response, Jared Millson and Mark Risjord; 24. How Values Shape the Machine Learning Opacity Problem, Emily Sullivan; 25. Understanding from Deep Learning Models in Context, Michael Tamir and Elay Shech; 26. Link Uncertainty, Implementation, and ML Opacity: Some Clarifications, Emily Sullivan; 27. Expecting Too Much from Our Machine Learning Models, Elay Shech and Michael Tamir; Part IV: Understanding and Scientific Progress; 28. Understanding the Progress of Science, C. D. McCoy; 29. Scientific Progress Without Justification, Finnur Dellsen; 30. The Significance of Justification for Progress: A Reply to Dellsen, C. D. McCoy; 31. Scientific Progress without Problems, Finnur Dellsen.
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Kareem Khalifa;Insa Lawler;Elay Shech;Henk de Regt;Angela Potochnik;Christopher Pincock;Juha Saatsi;Mazviita Chirimuuta;Daniel Kostic;Collin Rice;Jaakko Kuorikoski;Roman Frigg;James Nguyen;Jared Millson;Mark Risjord;Michael Tamir;Emily Sullivan;Casey McCoy;Finnur Dellsen;Amanda Nichols;Myron Penner;scientific explanation;idealized representations;explanatory power;modelling;modeling;verdicality problem;factivity;exactitude;mapping;information flow;machine learning;scientific progress;molecular structures;second quantization;Dl Model;De Regt;Ml Model;Causal Pattern;Pessimistic Meta-induction;Non-epistemic Values;Empirical Adequacy;Ideal Gas Law;Quantum Field Theory;Epistemic Achievements;Approximately True;Epistemic Representation;Deep Learning Models;Unobservable Entities;Epistemic Risk;Topological Explanation;Derivational Correctness;Bjorken Scaling;Counterfactual Dependence;Parton Model;Ontic Structural Realism;Target Phenomena;Peer Disagreement;Representational Content
1. Introduction, Kareem Khalifa, Insa Lawler, and Elay Shech; Part I: Understanding, Knowledge, and Explanation; 2. Can Scientific Understanding be Reduced to Knowledge?, Henk W. de Regt; 3. Should Friends and Frenemies of Understanding be Friends? Discussing de Regt, Kareem Khalifa; 4. Frenemies or Friends? A Reply to Kareem Khalifa, Henk W. de Regt; 5. Onwards, My Friend! Reply to de Regt, Kareem Khalifa; 6. Factivism in Historical Perspective: Understanding the Gravitational Deflection of Light, Sorin Bangu; 7. Ideal Patterns and Non-Factive Understanding, Mazviita Chirimuuta; 8. Topological Explanations: An Opinionated Appraisal, Daniel Kostic; 9. Explanatory Power: Factive vs. Pragmatic Dimension, Juha Saatsi; Part II: Understanding and Scientific Realism; 10. Understanding the Success of Science, Christopher Pincock; 11. Truth and Reality: How to be a Scientific Realist Without Believing Scientific Theories Should be True, Angela Potochnik; 12. Defensible Scientific Realism: A Reply to Potochnik, Christopher Pincock; 13. Different Ways to be a Realist: A Response to Pincock, Angela Potochnik; 14. Realism About Molecular Structures, Amanda J. Nichols and Myron A. Penner; 15. Anti-Fundamentalist Lessons for Scientific Representation from Scientific Metaphysics, Julia R.S. Bursten; Part III: Understanding, Representation, and Inference; 16. Factivity, Pluralism and the Inferential Account of Scientific Understanding, Jaakko Kuorikoski; 17. Scientific Representation and Understanding: A Communal and Dynamical View, Collin Rice; 18. Representation and Understanding are Constitutively Communal but Not Constitutively Historical, Jaakko Kuorikoski; 19. Which Modal Information and Abilities Are Required for Inferential Understanding?, Collin Rice; 20. Maps, Models, and Representation, James Nguyen and Roman Frigg; 21. DEKI, Denotation, and the Fortuitous Misuse of Maps, Jared Millson and Mark Risjord; 22. DEKI and the Mislocation of Justification: A Response to Millson and Risjord, Roman Frigg and James Nguyen; 23. DEKI and the Justification of Surrogative Inference: A Response, Jared Millson and Mark Risjord; 24. How Values Shape the Machine Learning Opacity Problem, Emily Sullivan; 25. Understanding from Deep Learning Models in Context, Michael Tamir and Elay Shech; 26. Link Uncertainty, Implementation, and ML Opacity: Some Clarifications, Emily Sullivan; 27. Expecting Too Much from Our Machine Learning Models, Elay Shech and Michael Tamir; Part IV: Understanding and Scientific Progress; 28. Understanding the Progress of Science, C. D. McCoy; 29. Scientific Progress Without Justification, Finnur Dellsen; 30. The Significance of Justification for Progress: A Reply to Dellsen, C. D. McCoy; 31. Scientific Progress without Problems, Finnur Dellsen.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Kareem Khalifa;Insa Lawler;Elay Shech;Henk de Regt;Angela Potochnik;Christopher Pincock;Juha Saatsi;Mazviita Chirimuuta;Daniel Kostic;Collin Rice;Jaakko Kuorikoski;Roman Frigg;James Nguyen;Jared Millson;Mark Risjord;Michael Tamir;Emily Sullivan;Casey McCoy;Finnur Dellsen;Amanda Nichols;Myron Penner;scientific explanation;idealized representations;explanatory power;modelling;modeling;verdicality problem;factivity;exactitude;mapping;information flow;machine learning;scientific progress;molecular structures;second quantization;Dl Model;De Regt;Ml Model;Causal Pattern;Pessimistic Meta-induction;Non-epistemic Values;Empirical Adequacy;Ideal Gas Law;Quantum Field Theory;Epistemic Achievements;Approximately True;Epistemic Representation;Deep Learning Models;Unobservable Entities;Epistemic Risk;Topological Explanation;Derivational Correctness;Bjorken Scaling;Counterfactual Dependence;Parton Model;Ontic Structural Realism;Target Phenomena;Peer Disagreement;Representational Content
