Anna Alexandrova

We examine considerations that enter into design and evaluation of measures in social science, categorizing them into four drivers: epistemic, ethical, pragmatic, and metrological. We call them drivers to highlight their role in guiding researchers’ decisions without determining them. Through an analysis of the World Inequality Report 2022, we reveal tensions among these drivers, illustrating the complex interplay between the various demands a measure must satisfy. Our analysis highlights the need for case-by-case compromises to address these tensions, as optimizing one driver often comes at the expense of another. We explore the extent to which these compromises shape measurement practice and the principles that guide researchers in balancing them. While existing literature on measurement assumes that tensions can be resolved with good practice and use, we argue that developing a good measure requires balancing multiple demands, recognising that it might be impossible to meet all of them simultaneously.

This introduction to the special issue 'Are Social Sciences Special?' proposes the concept of S-debates, or debates over the specialness of the social sciences. We argue that these debates arose regularly and cyclically since the institutionalisation of social sciences in 19th and 20th centuries and used a repertoire of arguments that can be traced back to the German-speaking debate covered by Martin Kusch in the article in this issue. While philosophers have typically tackled these debates as participants, no intervention ever led to a consensus. This indicates the need for a sociology of science approach that examines why these debates arise, their cultural and political stakes, and what the participants gain from arguing for or against the specialness of social sciences. Awareness of why S-debates ebb and flow and why they may be irresolvable helps us understand their role in contemporary social sciences and yields lessons for those wishing to revive them

What sort of inquiry is social science? This question used to preoccupy philosophers, but fell off their agenda due to a stalemate between so-called naturalists, who took the ideal to benatural science, and exceptionalists, who allied social sciences with the humanities. I show that both positions commit the error of contrastivism, namely, defining social science in contrast to these two traditions, which inevitably ends up caricaturing them. Using recent advances in philosophy, I formulate constructivism about social sciences, a view that denies an essence to this inquiry and grounds it in the needs of communities to understand and improve themselves.

Thick concepts, namely those concepts that describe and evaluate simultaneously, present a challenge to science. Since science does not have a monopoly on value judgments, what is responsible research involving such concepts? Using measurement of wellbeing as an example, we first present the options open to researchers wishing to study phenomena denoted by such concepts. We argue that while it is possible to treat these concepts as technical terms, or to make the relevant value judgment in-house, the responsible thing to do, especially in the context of public policy, is to make this value judgment through a legitimate political process that includes all the stakeholders of this research. We then develop a participatory model of measurement based on the ideal of co-production. To show that this model is feasible and realistic, we illustrate it with a case study of co-production of a concept of thriving conducted by the authors in collaboration with a UK anti-poverty charity Turn2us.

New types of artificial intelligence (AI), from cognitive assistants to social robots, are challenging meaningful comparison with other kinds of intelligence. How can such intelligent systems be catalogued, evaluated, and contrasted, with representations and projections that offer meaningful insights? To catalyse the research in AI and the future of cognition, we present the motivation, requirements and possibilities for an atlas of intelligence: an integrated framework and collaborative open repository for collecting and exhibiting information of all kinds of intelligence, including humans, non-human animals, AI systems, hybrids and collectives thereof. After presenting this initiative, we review related efforts and present the requirements of such a framework. We survey existing visualisations and representations, and discuss which criteria of inclusion should be used to configure an atlas of intelligence.

Thick concepts, namely those concepts that describe and evaluate simultaneously, present a challenge to science. Since science does not have a monopoly on value judgments, what is responsible research involving such concepts? Using measurement of wellbeing as an example, we first present the options open to researchers wishing to study phenomena denoted by such concepts. We argue that while it is possible to treat these concepts as technical terms, or to make the relevant value judgment in-house, the responsible thing to do, especially in the context of public policy, is to make this value judgment through a legitimate political process that includes all the stakeholders of this research. We then develop a participatory model of measurement based on the ideal of co-production. To show that this model is feasible and realistic, we illustrate it with a case study of co-production of a concept of thriving conducted by the authors in collaboration with a UK anti-poverty charity Turn2us.

When it originated in the late 19th century, psychometrics was a field with both a scientific and a social mission: psychometrics provided new methods for research into individual differences, and at the same time, these psychometric instruments were considered a means to create a new social order. In contrast, contemporary psychometrics - due to its highly technical nature and its limited involvement in substantive psychological research - has created the impression of being a value-free discipline. In this article, we develop a contrasting characterization of contemporary psychometrics as a value-laden discipline. We expose four such values: that individual differences are quantitative (rather than qualitative), that measurement should be objective in a specific sense, that test items should be fair, and that utility of a model is more important than its truth. Our goal is not to criticize psychometrics for employing these values, but rather to bring them into the open and to show that these values are not inevitable and are in need of systematic evaluation.

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.

What is it to be mentally healthy? In the ongoing movement to promote mental health, to reduce stigma, and to establish parity between mental and physical health, there is a clear enthusiasm about this concept and a recognition of its value in human life. However, it is often unclear what mental health means in all these efforts and whether there is a single concept underlying them. Sometimes, the initiatives for the sake of mental health are aimed just at reducing mental illness, thus implicitly identifying mental health with the absence of diagnosable psychiatric disease. More ambitiously, there are high-profile proposals to adopt a positive definition, identifying mental health with psychic or even overall well-being. We argue against both: a definition of mental health as mere absence of mental illness is too thin, too undemanding, and too closely linked to psychiatric value judgments, while the definition in terms of well-being is too demanding and potentially oppressive. As a compromise, we sketch out a middle position. On this view, mental health is a primary good, that is, the psychological preconditions of pursuing any conception of the good life, including well-being, without being identical to well-being.

We make the case that the Prisoner’s Dilemma, notwithstanding its fame and the quantity of intellectual resources devoted to it, has largely failed to explain any phenomena of social scientific or biological interest. In the heart of the paper we examine in detail a famous purported example of Prisoner’s Dilemma empirical success, namely Axelrod’s analysis of WWI trench warfare, and argue that this success is greatly overstated. Further, we explain why this negative verdict is likely true generally and not just in our case study. We also address some possible defenses of the Prisoner’s Dilemma.

Theoretical biology and economics are remarkably similar in their reliance on mathematical models, which attempt to represent real world systems using many idealized assumptions. They are also similar in placing a great emphasis on derivational robustness of modeling results. Recently philosophers of biology and economics have argued that robustness analysis can be a method for confirmation of claims about causal mechanisms, despite the significant reliance of these models on patently false assumptions. We argue that the power of robustness analysis has been greatly exaggerated. It is best regarded as a method of discovery rather than confirmation