It is a commonly expressed sentiment that the science and philosophy of well-being would do well to learn from each other. Typically such calls identify mistakes and bad practices on both sides that would be remedied if scientists picked the right bit of philosophy and philosophers picked the right bit of science. We argue that the differences between philosophers and scientists thinking about well-being are more difficult to reconcile than such calls suggest, and that pluralism is central to this task. (...) Pluralism is a stance that explicitly drives towards accommodating and nurturing the richness and diversity of well-being, both as a concept and as an object of inquiry. We show that well-being science manifests a contingent pluralism at the level of methodology, whereas philosophy of well-being has largely rejected pluralism at the conceptual level. Recently, things have begun to change. Within philosophy, conceptual monism is under attack. But so is methodological pluralism within science. We welcome the first development, and bemoan the second. We argue that a joined-up philosophy and science of well-being should recognise the virtues of both conceptual and methodological pluralism. Philosophers should embrace the methodological justification of pluralism that can be found in the well-being sciences, and scientists should embrace the conceptual reasons to be pluralist that can be found in philosophical debate. (shrink)

On the received view, the Representational Theory of Measurement reduces measurement to the numerical representation of empirical relations. This account of measurement has been widely criticized. In this article, I provide a new interpretation of the Representational Theory of Measurement that sidesteps these debates. I propose to view the Representational Theory of Measurement as a library of theorems that investigate the numerical representability of qualitative relations. Such theorems are useful tools for concept formation that, in turn, is one crucial aspect (...) of measurement for a broad range of cases in linguistics, rational choice, metaphysics, and the social sciences. (shrink)

In this thesis I investigate causal inquiry in the social sciences, drawing on examples from various disciplines and in particular from conflict studies. In a backlash against the pervasiveness of statistical methods, in the last decade certain social scientists have focused on finding the causal mechanisms behind observed correlations. To provide evidence for such mechanisms, researchers increasingly rely on ‘process tracing’, a method which attempts to give evidence for causal relations by specifying the chain of events connecting a putative cause (...) and effect of interest. I will ask whether the causal claims process tracers make are defensible, and where they are not defensible I will ask how we can improve the method. Throughout these investigations, I show that the conclusions of process tracing are constrained both by the causal structure ofthe social world and by social scientists’ aims and values. My central argument is this: all instances of social phenomena have causally relevant differences, which implies that any research design that requires some comparison between cases is limited by how we systematize these phenomena. Moreover, such research cannot rely on stable regularities. Nevertheless, to forego causal conclusions altogether is not the right response to these limitations; by carefully outlining our epistemic assumptions we can make progress in causal inquiry. While I use philosophical theories of causation to comment on the feasibility of a social scientific method, I also do the reverse: by investigating a popular contemporary method in the social sciences, I show to what extent our philosophical theories of causation are workable in practice. Thus, this thesis is both a methodological and a philosophical work. Every chapter discusses both a fundamental philosophical position on the social sciences and a relevant case study from the social sciences. (shrink)

The philosophy of measurement studies the conceptual, ontological, epistemic, and technological conditions that make measurement possible and reliable. A new wave of philosophical scholarship has emerged in the last decade that emphasizes the material and historical dimensions of measurement and the relationships between measurement and theoretical modeling. This essay surveys these developments and contrasts them with earlier work on the semantics of quantity terms and the representational character of measurement. The conclusions highlight four characteristics of the emerging research program in (...) philosophy of measurement: it is epistemological, coherentist, practice oriented, and model based. (shrink)

This work develops an epistemology of measurement, that is, an account of the conditions under which measurement and standardization methods produce knowledge as well as the nature, scope, and limits of this knowledge. I focus on three questions: (i) how is it possible to tell whether an instrument measures the quantity it is intended to? (ii) what do claims to measurement accuracy amount to, and how might such claims be justified? (iii) when is disagreement among instruments a sign of error, (...) and when does it imply that instruments measure different quantities? Based on a series of case studies conducted in collaboration with the US National Institute of Standards and Technology (NIST), I argue for a model-based approach to the epistemology of physical measurement. To measure a physical quantity, I argue, is to estimate the value of a parameter in an idealized model of a physical process. Such estimation involves inference from the final state (‘indication’) of a process to the value range of a parameter (‘outcome’) in light of theoretical and statistical assumptions. Contrary to contemporary philosophical views, measurement outcomes cannot be obtained by mapping the structure of indications. Instead, measurement outcomes as well as claims to accuracy, error and quantity individuation can only be adjudicated relative to a choice of idealized modelling assumptions. (shrink)

The Linda paradox is a key topic in current debates on the rationality of human reasoning and its limitations. We present a novel analysis of this paradox, based on the notion of verisimilitude as studied in the philosophy of science. The comparison with an alternative analysis based on probabilistic confirmation suggests how to overcome some problems of our account by introducing an adequately defined notion of verisimilitudinarian confirmation.

The observation that concepts used by social scientists are often problematic is not new; they have been described as Ballung concepts, cluster concepts, essentially contested, and reflexive; however, the need to work with these concepts remains. This article addresses the problem of variable choice in the social sciences by exploring and extending Woodward’s recommendations. This article demonstrates why Woodward’s criteria are difficult to apply in the social sciences and proposes an alternative, but complementary, framework for assessing variables.

Christina Lafont has argued that the early Heidegger's reflections on truth and understanding are incompatible with ‘the supposition of a single objective world’. This paper presents her argument, reviews some responses that the existing Heidegger literature suggests, and offers what I argue is a superior response. Building on a deeper exploration of just what the above ‘supposition’ demands, I argue that a crucial assumption that Lafont and Haugeland both accept must be rejected, namely, that different ‘understandings of Being’ can be (...) viewed as offering ‘rival perspectives’ on a common subject-matter. I develop this case by drawing on an alternative account of what a Heideggerian ‘understanding of Being’ might be like. (shrink)

If a human subject knows they are being measured, this knowledge may affect their attitudes and behaviour to such an extent that it affects the measurement results as well. This broad range of effects is shared under the term ‘reactivity’. Although reactivity is often seen by methodologists as a problem to overcome, in this paper I argue that some quite extreme reactive changes may be legitimate, as long as we are measuring phenomena that are not simple biological regularities. Legitimate reactivity (...) is reactivity which does not undermine the accuracy of a measure; I show that if such reactivity were corrected for, this would unjustifiably ignore the authority of the research subject. Applying this argument to the measurement of depression, I show that under the most commonly accepted models of depression there is room for legitimate reactivity. In the first part of the paper, I provide an inventory of the different types of reactivity that exist in the literature, as well as the different types of phenomena that one could measure. In the second part, I apply my argument to the measurement of depression with the PHQ-9 survey. I argue that depending on what kind of phenomenon we consider depression to be, we will accept different kinds of reactivity. I show that both under the harmful dysfunction model and the practical kinds model, certain reactive changes in measuring depression are best seen as legitimate recharacterizations of the underlying phenomenon, and define what legitimate means in this context. I conclude that in both models, biological aspects constrain characterization, but the models are not so strict that only one concept is acceptable, leaving room for reactivity. (shrink)

Many believe that the lack of correlation between happiness and income, first discovered by Richard Easterlin in 1974, entails the conclusion that well-being policies should be made based on happiness measures, rather than income measures. I argue that distinguishing between how well-being is characterized and how that characterization is measured introduces ways of denying the conclusion that policies should be made based on happiness measures. It is possible to avoid the conclusion either by denying that well-being hedonism is true or (...) by denying that happiness measures are a better way of operationalizing hedonism than income measures are. By making these possibilities explicit, we find that less hinges on whether income and happiness are correlated than is often thought. (shrink)

In recent years, it has become commonplace among the Global Burden of Disease study authors to regard the disability-adjusted life year primarily as a descriptive health metric. During the first phase of the GBD, it was widely acknowledged that the DALY had built-in evaluative assumptions. However, from the publication of the 2010 GBD and onwards, two central evaluative practices—time discounting and age-weighting—have been omitted from the DALY model. After this substantial revision, the emerging view now appears to be that the (...) DALY is primarily a descriptive measure. Our aim in this article is to argue that the DALY, despite changes, remains largely evaluative. Our analysis focuses on the understanding of the DALY by comparing the DALY as a measure of disease burden in the two most significant phases of GBD publications, from their beginning to the most recent releases. We identify numerous assumptions underlying the DALY and group them as descriptive or evaluative. We conclude that while the DALY model arguably has become more descriptive, it remains, by necessity, largely evaluative. (shrink)