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SPECIAL ISSUE: ON THE VERY IDEA OF LOGICAL FORM

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Special Edition: Chalmers on Virtual Reality

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SYMPOSIUM ON JASON STANLEY’S “HOW PROPAGANDA WORKS”

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Book symposium on François Recanati’s Mental Files

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New Perspectives on Quine’s “Word and Object”

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XII Taller d'Investigació en Filosofia

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Petrus Hispanus 2009

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Homage to M. S. Lourenço

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Normativity and Rationality

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Détails du magazine
Format
Magazine
eISSN
2182-2875
Première publication
16 Apr 2017
Période de publication
4 fois par an
Langues
Anglais

Chercher

Volume 9 (2017): Edition 47 (December 2017)

Détails du magazine
Format
Magazine
eISSN
2182-2875
Première publication
16 Apr 2017
Période de publication
4 fois par an
Langues
Anglais

Chercher

10 Articles
access type Accès libre

Causality and Modelling in the Sciences: Introduction

Publié en ligne: 16 Oct 2018
Pages: 423 - 427

Résumé

Abstract

The advantage of examining causality from the perspective of modelling is thus that it puts us naturally closer to the practice of the sciences. This means being able to set up an interdisciplinary dialogue that contrasts and compares modelling practices in different fields, say economics and biology, medicine and statistics, climate change and physics. It also means that it helps philosophers looking for questions that go beyond the narrow ‘what-is-causality’ or ‘what-are-relata’ and thus puts causality right at the centre of a complex crossroad: epistemology/methodology, metaphysics, politics/ethics. This special issue collects nine papers that touch upon various scientific fields, from system biology to medicine to quantum mechanics to economics, and different questions, from explanation and prediction to the role of both true and false assumptions in modelling.

Mots clés

  • Causality
  • modelling
  • causal explanation
  • scientific models
access type Accès libre

Models in Systems Medicine

Publié en ligne: 16 Oct 2018
Pages: 429 - 469

Résumé

Abstract

Systems medicine is a promising new paradigm for discovering associations, causal relationships and mechanisms in medicine. But it faces some tough challenges that arise from the use of big data: in particular, the problem of how to integrate evidence and the problem of how to structure the development of models. I argue that objective Bayesian models offer one way of tackling the evidence integration problem. I also offer a general methodology for structuring the development of models, within which the objective Bayesian approach fits rather naturally.

Mots clés

  • Systems medicine
  • personalised medicine
  • Bayesian models
  • Bayesian epistemology
  • mechanisms
access type Accès libre

Are Model Organisms Theoretical Models?

Publié en ligne: 16 Oct 2018
Pages: 471 - 498

Résumé

Abstract

This article compares the epistemic roles of theoretical models and model organisms in science, and specifically the role of non-human animal models in biomedicine. Much of the previous literature on this topic shares an assumption that animal models and theoretical models have a broadly similar epistemic role—that of indirect representation of a target through the study of a surrogate system. Recently, Levy and Currie (2015) have argued that model organism research and theoretical modelling differ in the justification of model-to-target inferences, such that a unified account based on the widely accepted idea of modelling as indirect representation does not similarly apply to both. I defend a similar conclusion, but argue that the distinction between animal models and theoretical models does not always track a difference in the justification of model-to-target inferences. Case studies of the use of animal models in biomedicine are presented to illustrate this. However, Levy and Currie’s point can be argued for in a different way. I argue for the following distinction. Model organisms (and other concrete models) function as surrogate sources of evidence, from which results are transferred to their targets by empirical extrapolation. By contrast, theoretical modelling does not involve such an inductive step. Rather, theoretical models are used for drawing conclusions from what is already known or assumed about the target system. Codifying assumptions about the causal structure of the target in external representational media (e.g. equations, graphs) allows one to apply explicit inferential rules to reach conclusions that could not be reached with unaided cognition alone (cf. Kuorikoski and Ylikoski 2015).

Mots clés

  • Biomedicine
  • modelling
  • animal models
  • theoretical models
  • scientific representation
access type Accès libre

Causal Concepts Guiding Model Specification in Systems Biology

Publié en ligne: 16 Oct 2018
Pages: 499 - 527

Résumé

Abstract

In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry.

Mots clés

  • Causation
  • systems biology
  • causal models
  • cellular networks
  • underdetermination
access type Accès libre

Turing Patterns and Biological Explanation

Publié en ligne: 16 Oct 2018
Pages: 529 - 552

Résumé

Abstract

Turing patterns are a class of minimal mathematical models that have been used to discover and conceptualize certain abstract features of early biological development. This paper examines a range of these minimal models in order to articulate and elaborate a philosophical analysis of their epistemic uses. It is argued that minimal mathematical models aid in structuring the epistemic practices of biology by providing precise descriptions of the quantitative relations between various features of the complex systems, generating novel predictions that can be compared with experimental data, promoting theory exploration, and acting as constitutive parts of empirically adequate explanations of naturally occurring phenomena, such as biological pattern formation. Focusing on the roles that minimal model explanations play in science motivates the adoption of a broader diachronic view of scientific explanation.

Mots clés

  • Mathematical models
  • development
  • patterns
  • biological explanation
access type Accès libre

Defining Metabolic Syndrome: Which Kind of Causality, if any, is Required?

Publié en ligne: 16 Oct 2018
Pages: 553 - 580

Résumé

Abstract

The definition of metabolic syndrome (MetS) has been, and still is, extremely controversial. My purpose is not to give a solution to the associated debate but to argue that the controversy is at least partially due to the different ‘causal content’ of the various definitions: their theoretical validity and practical utility can be evaluated by reconstructing or making explicit the underlying causal structure. I will therefore propose to distinguish the alternative definitions according to the kinds of causal content they carry: (1) definitions grounded on associations, (2) definitions presupposing a causal model built upon statistical associations, and (3) definitions grounded on underlying mechanisms. I suggest that analysing definitions according to their causal content can be helpful in evaluating alternative definitions of some diseases. I want to show how the controversy over MetS suggests a distinction among three kinds of definitions based on how explicitly they characterise the syndrome in causal terms, and on the type of causality involved. I will call ‘type 1 definitions’ those definitions that are purely associative; ‘type 2 definitions’ the definitions based on statistical associations, plus generic medical and causal knowledge; and ‘type 3 definitions’ the definitions based on (hypotheses about) mechanisms. These kinds of definitions, although different, can be related to each other. A definition with more specific causal content may be useful in the evaluation of definitions characterised by a lower degree of causal specificity. Moreover, the identification of the type of causality involved is of help to constitute a good criterion for choosing among different definitions of a pathological entity.

In section (1) I introduce the controversy about MetS, in section (2) I propose some remarks about medical definitions and their ‘causal import’, and in section (3) I suggest that the different attitudes towards the definition of MetS are relevant to evaluate their explicative power.

Mots clés

  • Metabolic syndrome
  • medical definitions
  • causal models
  • mechanistic property clusters
  • natural kinds
access type Accès libre

Toward a Causal Interpretation of the Common Factor Model

Publié en ligne: 16 Oct 2018
Pages: 581 - 601

Résumé

Abstract

Psychological constructs such as personality dimensions or cognitive traits are typically unobserved and are therefore measured by observing so-called indicators of the latent construct (e.g., responses to questionnaire items or observed behavior). The Common Factor Model (CFM) models the relations between the observed indicators and the latent variable. In this article we argue in favor of interpreting the CFM as a causal model rather than merely a statistical model, in which common factors are only descriptions of the indicators. When there is sufficient reason to hypothesize that the underlying causal structure of the data is a common cause structure, a causal interpretation of the CFM has several benefits over a merely statistical interpretation of the model. We argue that (1) a causal interpretation conforms with most research questions in which the goal is to explain the correlations between indicators rather than merely summarizing them; (2) a causal interpretation of the factor model legitimizes the focus on shared, rather than unique variance of the indicators; and (3) a causal interpretation of the factor model legitimizes the assumption of local independence.

Mots clés

  • Causality
  • reflective model
  • statistical model
access type Accès libre

What is the Problem with Model-based Explanation in Economics?

Publié en ligne: 16 Oct 2018
Pages: 603 - 630

Résumé

Abstract

The question of whether the idealized models of theoretical economics are explanatory has been the subject of intense philosophical debate. It is sometimes presupposed that either a model provides the actual explanation or it does not provide an explanation at all. Yet, two sets of issues are relevant to the evaluation of model-based explanation: what conditions should a model satisfy in order to count as explanatory and does the model satisfy those conditions. My aim in this paper is to unpack this distinction and show that separating the first set of issues from the second is crucial to an accurate diagnosis of the distinctive challenges that economic models pose. Along the way I sketch a view of model-based explanation in economics that focuses on the role that non-empirical and empirical strategies play in increasing confidence in the adequacy of a given model-based explanation.

Mots clés

  • Economic models
  • explanation
  • idealizations
  • Schelling’s model
  • Prisoner’s Dilemma
access type Accès libre

When are Purely Predictive Models Best?

Publié en ligne: 16 Oct 2018
Pages: 631 - 656

Résumé

Abstract

Can purely predictive models be useful in investigating causal systems? I argue “yes”. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to use theory to achieve explanation or insight without empirical success therefore fails, leaving us with the worst of both worlds—neither prediction nor explanation. Best go with empirical success by any means necessary. I support these methodological claims via case studies of two impressive feats of predictive modelling: opinion polling of political elections, and weather forecasting.

Mots clés

  • Prediction
  • explanation
  • weather
  • causation
  • idealization
access type Accès libre

Causality and the Modeling of the Measurement Process in Quantum Theory

Publié en ligne: 16 Oct 2018
Pages: 657 - 690

Résumé

Abstract

In this paper we provide a general account of the causal models which attempt to provide a solution to the famous measurement problem of Quantum Mechanics (QM). We will argue that—leaving aside instrumentalism which restricts the physical meaning of QM to the algorithmic prediction of measurement outcomes—the many interpretations which can be found in the literature can be distinguished through the way they model the measurement process, either in terms of the efficient cause or in terms of the final cause. We will discuss and analyze why both, ‘final cause’ and ‘efficient cause’ models, face severe difficulties to solve the measurement problem. In contradistinction to these schemes we will present a new model based on the immanent cause which, we will argue, provides an intuitive understanding of the measurement process in QM.

Mots clés

  • Causality
  • models
  • explanation
  • measurement problem
  • quantum mechanics
10 Articles
access type Accès libre

Causality and Modelling in the Sciences: Introduction

Publié en ligne: 16 Oct 2018
Pages: 423 - 427

Résumé

Abstract

The advantage of examining causality from the perspective of modelling is thus that it puts us naturally closer to the practice of the sciences. This means being able to set up an interdisciplinary dialogue that contrasts and compares modelling practices in different fields, say economics and biology, medicine and statistics, climate change and physics. It also means that it helps philosophers looking for questions that go beyond the narrow ‘what-is-causality’ or ‘what-are-relata’ and thus puts causality right at the centre of a complex crossroad: epistemology/methodology, metaphysics, politics/ethics. This special issue collects nine papers that touch upon various scientific fields, from system biology to medicine to quantum mechanics to economics, and different questions, from explanation and prediction to the role of both true and false assumptions in modelling.

Mots clés

  • Causality
  • modelling
  • causal explanation
  • scientific models
access type Accès libre

Models in Systems Medicine

Publié en ligne: 16 Oct 2018
Pages: 429 - 469

Résumé

Abstract

Systems medicine is a promising new paradigm for discovering associations, causal relationships and mechanisms in medicine. But it faces some tough challenges that arise from the use of big data: in particular, the problem of how to integrate evidence and the problem of how to structure the development of models. I argue that objective Bayesian models offer one way of tackling the evidence integration problem. I also offer a general methodology for structuring the development of models, within which the objective Bayesian approach fits rather naturally.

Mots clés

  • Systems medicine
  • personalised medicine
  • Bayesian models
  • Bayesian epistemology
  • mechanisms
access type Accès libre

Are Model Organisms Theoretical Models?

Publié en ligne: 16 Oct 2018
Pages: 471 - 498

Résumé

Abstract

This article compares the epistemic roles of theoretical models and model organisms in science, and specifically the role of non-human animal models in biomedicine. Much of the previous literature on this topic shares an assumption that animal models and theoretical models have a broadly similar epistemic role—that of indirect representation of a target through the study of a surrogate system. Recently, Levy and Currie (2015) have argued that model organism research and theoretical modelling differ in the justification of model-to-target inferences, such that a unified account based on the widely accepted idea of modelling as indirect representation does not similarly apply to both. I defend a similar conclusion, but argue that the distinction between animal models and theoretical models does not always track a difference in the justification of model-to-target inferences. Case studies of the use of animal models in biomedicine are presented to illustrate this. However, Levy and Currie’s point can be argued for in a different way. I argue for the following distinction. Model organisms (and other concrete models) function as surrogate sources of evidence, from which results are transferred to their targets by empirical extrapolation. By contrast, theoretical modelling does not involve such an inductive step. Rather, theoretical models are used for drawing conclusions from what is already known or assumed about the target system. Codifying assumptions about the causal structure of the target in external representational media (e.g. equations, graphs) allows one to apply explicit inferential rules to reach conclusions that could not be reached with unaided cognition alone (cf. Kuorikoski and Ylikoski 2015).

Mots clés

  • Biomedicine
  • modelling
  • animal models
  • theoretical models
  • scientific representation
access type Accès libre

Causal Concepts Guiding Model Specification in Systems Biology

Publié en ligne: 16 Oct 2018
Pages: 499 - 527

Résumé

Abstract

In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry.

Mots clés

  • Causation
  • systems biology
  • causal models
  • cellular networks
  • underdetermination
access type Accès libre

Turing Patterns and Biological Explanation

Publié en ligne: 16 Oct 2018
Pages: 529 - 552

Résumé

Abstract

Turing patterns are a class of minimal mathematical models that have been used to discover and conceptualize certain abstract features of early biological development. This paper examines a range of these minimal models in order to articulate and elaborate a philosophical analysis of their epistemic uses. It is argued that minimal mathematical models aid in structuring the epistemic practices of biology by providing precise descriptions of the quantitative relations between various features of the complex systems, generating novel predictions that can be compared with experimental data, promoting theory exploration, and acting as constitutive parts of empirically adequate explanations of naturally occurring phenomena, such as biological pattern formation. Focusing on the roles that minimal model explanations play in science motivates the adoption of a broader diachronic view of scientific explanation.

Mots clés

  • Mathematical models
  • development
  • patterns
  • biological explanation
access type Accès libre

Defining Metabolic Syndrome: Which Kind of Causality, if any, is Required?

Publié en ligne: 16 Oct 2018
Pages: 553 - 580

Résumé

Abstract

The definition of metabolic syndrome (MetS) has been, and still is, extremely controversial. My purpose is not to give a solution to the associated debate but to argue that the controversy is at least partially due to the different ‘causal content’ of the various definitions: their theoretical validity and practical utility can be evaluated by reconstructing or making explicit the underlying causal structure. I will therefore propose to distinguish the alternative definitions according to the kinds of causal content they carry: (1) definitions grounded on associations, (2) definitions presupposing a causal model built upon statistical associations, and (3) definitions grounded on underlying mechanisms. I suggest that analysing definitions according to their causal content can be helpful in evaluating alternative definitions of some diseases. I want to show how the controversy over MetS suggests a distinction among three kinds of definitions based on how explicitly they characterise the syndrome in causal terms, and on the type of causality involved. I will call ‘type 1 definitions’ those definitions that are purely associative; ‘type 2 definitions’ the definitions based on statistical associations, plus generic medical and causal knowledge; and ‘type 3 definitions’ the definitions based on (hypotheses about) mechanisms. These kinds of definitions, although different, can be related to each other. A definition with more specific causal content may be useful in the evaluation of definitions characterised by a lower degree of causal specificity. Moreover, the identification of the type of causality involved is of help to constitute a good criterion for choosing among different definitions of a pathological entity.

In section (1) I introduce the controversy about MetS, in section (2) I propose some remarks about medical definitions and their ‘causal import’, and in section (3) I suggest that the different attitudes towards the definition of MetS are relevant to evaluate their explicative power.

Mots clés

  • Metabolic syndrome
  • medical definitions
  • causal models
  • mechanistic property clusters
  • natural kinds
access type Accès libre

Toward a Causal Interpretation of the Common Factor Model

Publié en ligne: 16 Oct 2018
Pages: 581 - 601

Résumé

Abstract

Psychological constructs such as personality dimensions or cognitive traits are typically unobserved and are therefore measured by observing so-called indicators of the latent construct (e.g., responses to questionnaire items or observed behavior). The Common Factor Model (CFM) models the relations between the observed indicators and the latent variable. In this article we argue in favor of interpreting the CFM as a causal model rather than merely a statistical model, in which common factors are only descriptions of the indicators. When there is sufficient reason to hypothesize that the underlying causal structure of the data is a common cause structure, a causal interpretation of the CFM has several benefits over a merely statistical interpretation of the model. We argue that (1) a causal interpretation conforms with most research questions in which the goal is to explain the correlations between indicators rather than merely summarizing them; (2) a causal interpretation of the factor model legitimizes the focus on shared, rather than unique variance of the indicators; and (3) a causal interpretation of the factor model legitimizes the assumption of local independence.

Mots clés

  • Causality
  • reflective model
  • statistical model
access type Accès libre

What is the Problem with Model-based Explanation in Economics?

Publié en ligne: 16 Oct 2018
Pages: 603 - 630

Résumé

Abstract

The question of whether the idealized models of theoretical economics are explanatory has been the subject of intense philosophical debate. It is sometimes presupposed that either a model provides the actual explanation or it does not provide an explanation at all. Yet, two sets of issues are relevant to the evaluation of model-based explanation: what conditions should a model satisfy in order to count as explanatory and does the model satisfy those conditions. My aim in this paper is to unpack this distinction and show that separating the first set of issues from the second is crucial to an accurate diagnosis of the distinctive challenges that economic models pose. Along the way I sketch a view of model-based explanation in economics that focuses on the role that non-empirical and empirical strategies play in increasing confidence in the adequacy of a given model-based explanation.

Mots clés

  • Economic models
  • explanation
  • idealizations
  • Schelling’s model
  • Prisoner’s Dilemma
access type Accès libre

When are Purely Predictive Models Best?

Publié en ligne: 16 Oct 2018
Pages: 631 - 656

Résumé

Abstract

Can purely predictive models be useful in investigating causal systems? I argue “yes”. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to use theory to achieve explanation or insight without empirical success therefore fails, leaving us with the worst of both worlds—neither prediction nor explanation. Best go with empirical success by any means necessary. I support these methodological claims via case studies of two impressive feats of predictive modelling: opinion polling of political elections, and weather forecasting.

Mots clés

  • Prediction
  • explanation
  • weather
  • causation
  • idealization
access type Accès libre

Causality and the Modeling of the Measurement Process in Quantum Theory

Publié en ligne: 16 Oct 2018
Pages: 657 - 690

Résumé

Abstract

In this paper we provide a general account of the causal models which attempt to provide a solution to the famous measurement problem of Quantum Mechanics (QM). We will argue that—leaving aside instrumentalism which restricts the physical meaning of QM to the algorithmic prediction of measurement outcomes—the many interpretations which can be found in the literature can be distinguished through the way they model the measurement process, either in terms of the efficient cause or in terms of the final cause. We will discuss and analyze why both, ‘final cause’ and ‘efficient cause’ models, face severe difficulties to solve the measurement problem. In contradistinction to these schemes we will present a new model based on the immanent cause which, we will argue, provides an intuitive understanding of the measurement process in QM.

Mots clés

  • Causality
  • models
  • explanation
  • measurement problem
  • quantum mechanics

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