Both metaphysics and cognitive science raise the question of what natural concepts or properties are. A link between the two is notoriously hard to establish. I propose to take natural concepts or properties to be those that are revealed in interaction. The concept of affordances is refined and naturalized to spell out how interacting with objects grounds concepts. I will call this account “Ecological Empiricism”. I argue that the notion of naturalness within this framework turns out to be a gradable (...) – there are more or less natural properties – and dependent on the perspective taken – metaphysically natural properties are different from cognitive natural properties. From a metaphysical point of view, perfect correlations between actions and sensory input are relevant, which are best approximated by scientific measurement. For cognition, simple or basic actions are relevant. Although metaphysical and cognitive naturalness does not coincide according to Ecological Empiricism, it presents a common framework with a uniform conception of naturalness. (shrink)

The development of the science of poverty has largely been driven by the need to define more precisely what poverty is, as well as to provide theoretical and empirical criteria for identifying those who suffer from it. This thesis focuses on a notable response to these and related questions: the conception and measure of poverty by the British sociologist Peter Townsend. Townsend defines poverty as relative deprivation caused by lack of resources. This conception, along with his corresponding cut-off measure, constitutes (...) some of the key components of his theory of poverty. The theory discusses what poverty is, its causes, and how it can be eradicated, and is detailed in Poverty in the United Kingdom: A Survey of Household Resources and Standards of Living (1979), his magnum opus. The primary aim of this thesis is to interpret the theory from Townsend’s adherence to the value-free ideal. He pursued this ideal in response to what he perceived as the pervasive and deleterious influence of moral and political values on measures, theories, and policies on poverty throughout its scientific and political history. I argue that to be free from such influence, Townsend aimed to be as guided by epistemic values as possible, which resulted in a systematic theory of poverty. -/- The thesis is structured as follows: In Chapter 1, I situate Townsend’s conception and measurement of poverty within the context of both the recent history of scientific poverty lines and the most important approaches to measuring the phenomenon. Chapter 2 discusses the systematic nature of the theory, including its role as a middle-range theory that bridges broad-range theories with empirical data. This connection is achieved through several elements: a conception of poverty; hypotheses and anthropological observations related to the elements of the conception; measures to test these hypotheses; an explanation of poverty; and reports on how the research was conducted. Furthermore, Townsend’s theory aligns with Bradburn et al.’s (2017) measurement theory, which asserts that a good measure must include a conception of the relevant phenomenon, a representation, and procedures to gather data. Additionally, all three components must be supported by arguments showing that they fit together properly. -/- In the remaining chapters, I present and discuss the main elements of the theory: his conception of poverty as relative deprivation caused by lack of resources, along with the related hypotheses and observations (Chapter 3); the representations of this conception and the procedures for data collection, processing, as well as ensuring their reliability (Chapter 4); and the explanation of poverty (Chapter 5). I conclude by presenting what Townsend considered the “policy prescriptions” of his theory. Despite its potential shortcomings, I also argue that Townsend’s theory fulfills an important epistemic value: fruitfulness. (shrink)

The article advances a new way of thinking about classifications in general and the classification of mental disorders in particular. By applying insights from measurement practice to the context of classification, I defend a notion of epistemic accuracy that allows one to evaluate and improve classifications by comparing different classifying methods to each other. Progress in classification arises from the mutual development of classification systems and classifying methods. Based on this notion of accuracy, the article illustrates with an example how (...) psychiatric classifications can be improved via circumscribed comparisons of different perspectives on mental disorders, without relying on complete models of their complex aetiology. When applying this strategy, the traditional opposition between symptom-based and causal approaches is of little consequence for making progress in the epistemic accuracy of psychiatric classification. (shrink)

In economics, thought experiments are frequently justified by the difficulty of conducting controlled experiments. They serve several functions, such as establishing causal facts, isolating tendencies, and allowing inferences from models to reality. In this paper, I argue that thought experiments served a further function in economics: facilitating the quantitative definition and measurement of the theoretical concept of utility, thereby bridging the gap between theory and statistical data. I support my argument by a case study, the “hypothetical experiments” of the Norwegian (...) economist Ragnar Frisch (1895-1973). Frisch aimed to eliminate introspection and a subjective concept of utility from economic reasoning. At the same time, he sought behavioral foundations for economic theory that enabled quantitative reasoning. By using thought experiments to justify his set of choice axioms and facilitating the operationalization of utility, Frisch circumvented the problem of observing utility via actual experiments without eliminating the concept of utility from economic theory altogether. As such, these experiments helped Frisch to empirically support the theory’s most important results, such as the laws of demand and supply, without the input of new empirical findings. I suggest that Frisch’s experiments fulfill the main characteristics of thought experiments. (shrink)

Saving the phenomena is shaping the phenomena: recognising that a specific phenomenon exists implies the existence of a mechanism that gives rise to the phenomenon and that the phenomenon has a shape. These two aspects are inextricably connected: determining the best shape is determining the mechanism that produces it. What is best then depends on evaluating the nature of the simplicity of the mechanism.

Contemporary approaches to “non-market accounting” depend critically on methods of “monetization,” i.e., determining prices for goods outside the market. Monetization constitutes a case of economic measurement in a narrow sense that has not yet been analyzed in the literature on measurement in economics. Monetization, I will argue, uses homo economicus—originally created as a model to explain existing prices—as a measuring device, one that generates new prices for goods that are not traded on markets. Homo economicus, though long contested in microeconomics, (...) is thus enjoying a dubious revival in non-market accounting. (shrink)

When discrepancies are discovered between the outcomes of different measurement procedures, two sorts of explanation are open to scientists. Either some of the outcomes are inaccurate or the procedures are not measuring the same quantity. I argue that, due to the possibility of systematic error, the choice between and is underdetermined in principle by any possible evidence. Consequently, foundationalist criteria of quantity individuation are either empty or circular. I propose a coherentist, model-based account of measurement that avoids the underdetermination problem, (...) and use this account to explain how scientists individuate quantities in practice. (shrink)

Supervised machine learning has found its way into ever more areas of scientific inquiry, where the outcomes of supervised machine learning applications are almost universally classified as predictions. I argue that what researchers often present as a mere terminological particularity of the field involves the consequential transformation of tasks as diverse as classification, measurement, or image segmentation into prediction problems. Focusing on the case of machine-learning enabled poverty prediction, I explore how reframing a measurement problem as a prediction task alters (...) the primary epistemic aim of the application. Instead of measuring a property, machine learning developers conceive of their models as predicting a given measurement of this property. I argue that this _predictive reframing_ common to supervised machine learning applications is epistemically and ethically problematic, as it allows developers to externalize concerns critical to the epistemic validity and ethical implications of their model’s inferences. I further hold that the predictive reframing is not a necessary feature of supervised machine learning by offering an alternative conception of machine learning models as measurement models. An interpretation of supervised machine learning applications to measurement tasks as _automatically-calibrated model-based measurements_ internalizes questions of construct validity and ethical desirability critical to the measurement problem these applications are intended to and presented as solving. Thereby, this paper introduces an initial framework for exploring technical, historical, and philosophical research at the intersection of measurement and machine learning. (shrink)