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Bianca Cepollaro and Giuliano Torrengo

Abstract

In this paper we discuss two issues addressed by Stanley in How Propaganda Works: the status of slurs (Section 1) and the notion of positive propaganda (Section 2). In particular, in Section 1 we argue contra Stanley that code words like ‘welfare’ are crucially different from slurs in that the association between the lexical item and an additional social meaning is not as systematic as it is for slurs. In this sense, slurs bring about a special kind of propagandistic effect, even if it typically concerns informal contexts rather than public debates. In Section 2, we consider positive propaganda and its relation to emotional effects. For Stanley, positive propaganda relies on the production of emotional effects, feature which risks to erode rational debates even if there is a good purpose behind. Instead, we argue that positive propaganda can work with no appeal to emotions. To this end, we focus on the use of ‘she’ as the default personal pronoun in academic writing and suggest that this measure can count as positive propaganda which rather than eroding rational debates by relying on emotional effects, closely resembles affirmative action aimed at counterbalance a pre-existing form of injustice and inequality.

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Maria Cristina Amoretti

Abstract

In this paper I focus on the connection between some of Stanley’s claims about propaganda and flawed ideologies, and the idea of the social situatedness or perspective-relativity of knowledge. More precisely, I will try to show how Stanley’s reflections on the nature of propaganda and its relationship with flawed ideologies push us towards the empiricists’ characterisation of the social situatedness of knowledge. Not only do these reflections reveal some important weaknesses of standpoint theories (that is, the claim of epistemic asymmetry between advantaged and negatively advantaged groups, and the necessity of actively achieving a standpoint), but they also support the request for the pluralism, rational critique, cooperation, fair discussion and epistemic integration fostered by social empiricism. This means that the broad idea of the social situatedness of knowledge should be defended and further developed along the lines sketched by social empiricism.

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Olúfémi O. Táíwò

Abstract

Jason Stanley’s How Propaganda Works roots the danger of undermining propaganda in an ideology based account of politics, treating individuals’ beliefs and social belief systems as the primary target and mechanism of undermining propaganda. In this paper I suggest a theoretical alternative to the role ideology plays in Stanley’s theories and theories like it, which I call practice first. A practice first account instead treats public behavior as the primary target of propaganda, and analyzes undermining propaganda as altering the incentive structure that sets the terms for public behavior.

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Robert Northcott

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.

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Caterina Marchionni

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.

Open access

Maria Serban

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.

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Riet Van Bork, Lisa D. Wijsen and Mijke Rhemtulla

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.

Open access

Jon Williamson

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.

Open access

Margherita Benzi

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.

Open access

Christian de Ronde

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.