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510.0.#.a: Arts and Humanities Citation Index, Revistes Cientifiques de Ciencies Socials Humanitais (CARHUS Plus), Latinoamericanas en Ciencias Sociales y Humanidades (CLASE), Directory of Open Access Journals (DOAJ), European Reference Index for the Humanities (ERIH PLUS), Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex), SCOPUS, Journal Storage (JSTOR), The Philosopher’s Index, Ulrich’s Periodical Directory

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650.#.4.x: Artes y Humanidades

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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: http://critica.filosoficas.unam.mx/index.php/critica

351.#.#.b: Crítica. Revista Hispanoamericana de Filosofía

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270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

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883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural, UNAM

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial, UNAM

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: http://critica.filosoficas.unam.mx/index.php/critica/article/view/775/747

100.1.#.a: Pérez Ransanz, Ana Rosa

524.#.#.a: Pérez Ransanz, Ana Rosa (1990). Random and Explanation. Some Remarks. Crítica. Revista Hispanoamericana de Filosofía; Vol 22 No 66, 1990; 39-54. Recuperado de https://repositorio.unam.mx/contenidos/4115548

245.1.0.a: Random and Explanation. Some Remarks

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Investigaciones Filosóficas, UNAM

264.#.0.c: 1990

264.#.1.c: 2018-12-13

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-ND 4.0 Internacional, https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es, fecha de asignación de la licencia 2018-12-13, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico alberto@filosoficas.unam.mx

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520.3.#.a: In the first part of this paper I examine the Hempelian model for the probabilistic explanation of particular events: the inductive-statistical model. Here I focus on an examination of the notion of expectability and the implied requirement of high probability. I intend to show that expectability and high probability, in turn, answer to a deep rooted intuition concerning explanation: given an event E, the same sort of circumstances cannot explain E and -E. This intuition is called here “the basic principle”. This basic principle is also the ground for the Hempelian thesis that to explain is always to explain why. I think this thesis is wrong and I propose to distinguish between explaining why and explaining how possibly . I discuss the difficulties confronting the statistical-inductive model in the light of the assumptions here examined and suggest possible solutions. In the second part, I explore those possible solutions, in particular I examine the symmetry principle proposed by Salmon: given an stochastic process, the highly probable results are as well understood as the improbable results. This principle implies the rejection of the basic principle. I claim that the symmetry principle must be restricted to explanations how . Later I discuss the DNP model proposed by Railton and I make some criticisms to it. I try to show that if we make explicit the distinction between explaining why and explaining how in this model, we can restrict clearly the range of applicability of the symmetry principle and the basic principle, I conclude that Railton’s model constitutes an adequate basis to establish the scope and limits of probabilistic explanations of genuinely random events. In the first part of this paper I examine the Hempelian model for the probabilistic explanation of particular events: the inductive-statistical model. Here I focus on an examination of the notion of expectability and the implied requirement of high probability. I intend to show that expectability and high probability, in turn, answer to a deep rooted intuition concerning explanation: given an event E, the same sort of circumstances cannot explain E and -E. This intuition is called here “the basic principle”. This basic principle is also the ground for the Hempelian thesis that to explain is always to explain why. I think this thesis is wrong and I propose to distinguish between explaining why and explaining how possibly . I discuss the difficulties confronting the statistical-inductive model in the light of the assumptions here examined and suggest possible solutions. In the second part, I explore those possible solutions, in particular I examine the symmetry principle proposed by Salmon: given an stochastic process, the highly probable results are as well understood as the improbable results. This principle implies the rejection of the basic principle. I claim that the symmetry principle must be restricted to explanations how . Later I discuss the DNP model proposed by Railton and I make some criticisms to it. I try to show that if we make explicit the distinction between explaining why and explaining how in this model, we can restrict clearly the range of applicability of the symmetry principle and the basic principle, I conclude that Railton’s model constitutes an adequate basis to establish the scope and limits of probabilistic explanations of genuinely random events.

773.1.#.t: Crítica. Revista Hispanoamericana de Filosofía; Vol 22 No 66 (1990); 39-54

773.1.#.o: http://critica.filosoficas.unam.mx/index.php/critica

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300.#.#.a: Páginas: 39-54

264.#.1.b: Instituto de Investigaciones Filosóficas, UNAM

758.#.#.1: http://critica.filosoficas.unam.mx/index.php/critica

doi: https://doi.org/10.22201/iifs.18704905e.1990.775

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245.1.0.b: Azar y explicación. Algunas observaciones

last_modified: 2021-11-09 23:50:00

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Artículo

Random and Explanation. Some Remarks

Pérez Ransanz, Ana Rosa

Instituto de Investigaciones Filosóficas, UNAM, publicado en Crítica. Revista Hispanoamericana de Filosofía, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Pérez Ransanz, Ana Rosa (1990). Random and Explanation. Some Remarks. Crítica. Revista Hispanoamericana de Filosofía; Vol 22 No 66, 1990; 39-54. Recuperado de https://repositorio.unam.mx/contenidos/4115548

Descripción del recurso

Autor(es)
Pérez Ransanz, Ana Rosa
Tipo
Artículo de Investigación
Área del conocimiento
Artes y Humanidades
Título
Random and Explanation. Some Remarks
Fecha
2018-12-13
Resumen
In the first part of this paper I examine the Hempelian model for the probabilistic explanation of particular events: the inductive-statistical model. Here I focus on an examination of the notion of expectability and the implied requirement of high probability. I intend to show that expectability and high probability, in turn, answer to a deep rooted intuition concerning explanation: given an event E, the same sort of circumstances cannot explain E and -E. This intuition is called here “the basic principle”. This basic principle is also the ground for the Hempelian thesis that to explain is always to explain why. I think this thesis is wrong and I propose to distinguish between explaining why and explaining how possibly . I discuss the difficulties confronting the statistical-inductive model in the light of the assumptions here examined and suggest possible solutions. In the second part, I explore those possible solutions, in particular I examine the symmetry principle proposed by Salmon: given an stochastic process, the highly probable results are as well understood as the improbable results. This principle implies the rejection of the basic principle. I claim that the symmetry principle must be restricted to explanations how . Later I discuss the DNP model proposed by Railton and I make some criticisms to it. I try to show that if we make explicit the distinction between explaining why and explaining how in this model, we can restrict clearly the range of applicability of the symmetry principle and the basic principle, I conclude that Railton’s model constitutes an adequate basis to establish the scope and limits of probabilistic explanations of genuinely random events. In the first part of this paper I examine the Hempelian model for the probabilistic explanation of particular events: the inductive-statistical model. Here I focus on an examination of the notion of expectability and the implied requirement of high probability. I intend to show that expectability and high probability, in turn, answer to a deep rooted intuition concerning explanation: given an event E, the same sort of circumstances cannot explain E and -E. This intuition is called here “the basic principle”. This basic principle is also the ground for the Hempelian thesis that to explain is always to explain why. I think this thesis is wrong and I propose to distinguish between explaining why and explaining how possibly . I discuss the difficulties confronting the statistical-inductive model in the light of the assumptions here examined and suggest possible solutions. In the second part, I explore those possible solutions, in particular I examine the symmetry principle proposed by Salmon: given an stochastic process, the highly probable results are as well understood as the improbable results. This principle implies the rejection of the basic principle. I claim that the symmetry principle must be restricted to explanations how . Later I discuss the DNP model proposed by Railton and I make some criticisms to it. I try to show that if we make explicit the distinction between explaining why and explaining how in this model, we can restrict clearly the range of applicability of the symmetry principle and the basic principle, I conclude that Railton’s model constitutes an adequate basis to establish the scope and limits of probabilistic explanations of genuinely random events.
Idioma
spa
ISSN
ISSN electrónico: 1870-4905; ISSN impreso: 0011-1503

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