Anna Alexandrova

Making Models Count

What sort of claims do scientific models make and how do these claims then underwrite empirical successes such as explanations and reliable policy interventions? In this paper I propose answers to these questions for the class of models used throughout the social and biological sciences, namely idealized deductive ones with a causal interpretation. I argue that the two main existing accounts misrepresent how these models are actually used, and propose a new account.

NO REVOLUTION NECESSARY: NEURAL MECHANISMS FOR ECONOMICS

We argue that neuroeconomics should be a mechanistic science. We defend this view as preferable both to a revolutionary perspective, according to which classical economics is eliminated in favour of neuroeconomics, and to a classical economic perspective, according to which economics is insulated from facts about psychology and neuroscience. We argue that, like other mechanistic sciences, neuroeconomics will earn its keep to the extent that it either reconfigures how economists think about decision-making or how neuroscientists think about brain mechanisms underlying behaviour. We discuss some ways that the search for mechanisms can bring about such top-down and bottom-up revision, and we consider some examples from the recent neuroeconomics literature of how varieties of progress of this sort might be achieved.

Well-Being as an Object of Science

The burgeoning science of well-being makes no secret of being value laden: improvement of well-being is its explicit goal. But in order to achieve this goal its concepts and claims need to be value adequate; that is, they need, among other things, to adequately capture well-being. In this article I consider two ways of securing this adequacy—first, by relying on philosophical theory of prudential value and, second, by the psychometric approach. I argue that neither is fully adequate and explore an alternative. This alternative requires thorough changes in the way philosophers theorize about well-being.

When Analytic Narratives Explain

Rational choice modeling originating in economics is sweeping across many areas of social science. This paper examines a popular methodological proposal for integrating formal models from game theory with more traditional narrative explanations of historical phenomena, known as “analytic narratives”. Under what conditions are we justified in thinking that an analytic narrative provides a good explanation? In this paper I criticize the existing criteria and provide a set of my own. Along the way, I address the critique of analytic narratives by Jon Elster.

It's just a feeling: why economic models do not explain

Julian Reiss correctly identified a trilemma about economic models: we cannot maintain that they are false, but nevertheless explain and that only true accounts explain. In this reply we give reasons to reject the second premise – that economic models explain. Intuitions to the contrary should be distrusted.

Doing Well in the Circumstances

Judgments of well-being across different circumstances and spheres of life exhibit a staggering diversity. Depending on the situation, we use different standards of well-being and even treat it as being constituted by different things. This is true of scientific studies as well as of everyday life. How should we interpret this diversity? I consider three ways of doing so: first, denying the legitimacy of this diversity, second, treating well-being as semantically invariant but differentially realizable, and, third, adopting contextualist semantics for well-being expressions. I reject the first option on the grounds that it is unable to make sense of much of everyday and scientific linguistic practices, and also because it makes the category of well-being insignificant or even otiose for practical purposes. We should thus pick between the second and the third options. I argue that contextualism about well-being is more plausible and faces fewer objections than the differential realization view. I conclude with a discussion of other features of contextualism: it does not imply that well-being is relative to individual taste, it need not result in eliminativism about well-being, nor in scepticism about a general theory of well-being. It does not commit us to an “anything goes” approach, nor does it threaten anarchy and miscommunication.

Is Construct Validation Valid

What makes a measure of well-being valid? The dominant approach today, construct validation, uses psychometrics to ensure that questionnaires behave in accordance with background knowledge. Our first claim is interpretive—construct validation obeys a coherentist logic that seeks to balance diverse sources of evidence about the construct in question. Our second claim is critical—while in theory this logic is defensible, in practice it does not secure valid measures. We argue that the practice of construct validation in well-being research is theory avoidant, favoring a narrow focus on statistical tests while largely ignoring relevant philosophical considerations.

Can the Science of Well-Being Be Objective?

Well–being, health and freedom are some of the many phenomena of interest to science whose definitions rely on a normative standard. Empirical generalizations about them thus present a special case of value-ladenness. I propose the notion of a ‘mixed claim’ to denote such generalizations. Against the prevailing wisdom, I argue that we should not seek to eliminate them from science. Rather, we need to develop principles for their legitimate use. Philosophers of science have already reconciled values with objectivity in several ways, but none of the existing proposals are suitable for mixed claims. Using the example of the science of well-being, I articulate a conception of objectivity for this science and for mixed claims in general. 1 Introduction 2 What Are Mixed Claims? 3 Mixed Claims Are Different   3.1 Values as reasons to pursue science   3.2 Values as agenda-setters   3.3 Values as ethical constraints on research protocols   3.4 Values as arbiters between underdetermined theories   3.5 Values as determinants of standards of confirmation   3.6 Values as sources of wishful thinking and fraud 4 Mixed Claims Should Stay   4.1 Against Nagel 5 The Dangers of Mixed Claims 6 The Existing Accounts of Objectivity   6.1 The perils of impartiality 7 Objectivity for Mixed Claims 8 Three Rules   8.1 Unearth the value presuppositions in methods and measures   8.2 Check if value presuppositions are invariant to disagreements   8.3 Consult the relevant parties 9 Conclusion 1 Introduction 2 What Are Mixed Claims? 3 Mixed Claims Are Different   3.1 Values as reasons to pursue science   3.2 Values as agenda-setters   3.3 Values as ethical constraints on research protocols   3.4 Values as arbiters between underdetermined theories   3.5 Values as determinants of standards of confirmation   3.6 Values as sources of wishful thinking and fraud   3.1 Values as reasons to pursue science   3.2 Values as agenda-setters   3.3 Values as ethical constraints on research protocols   3.4 Values as arbiters between underdetermined theories   3.5 Values as determinants of standards of confirmation   3.6 Values as sources of wishful thinking and fraud 4 Mixed Claims Should Stay   4.1 Against Nagel   4.1 Against Nagel 5 The Dangers of Mixed Claims 6 The Existing Accounts of Objectivity   6.1 The perils of impartiality   6.1 The perils of impartiality 7 Objectivity for Mixed Claims 8 Three Rules   8.1 Unearth the value presuppositions in methods and measures   8.2 Check if value presuppositions are invariant to disagreements   8.3 Consult the relevant parties   8.1 Unearth the value presuppositions in methods and measures   8.2 Check if value presuppositions are invariant to disagreements   8.3 Consult the relevant parties 9 Conclusion

Mapping Intelligence: Requirements and Possibilities

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.