En economía, la investigación se divide en dos grandes metodologías: los modelos teórico-matemáticos y los estudios empíricos. Estudiando modelos teóricos y métodos empíricos ) se da cuenta de las limitaciones de ambos métodos. Se concluye que ninguno de estos puede generar explicaciones de cómo en realidad suceden las cosas, sino que solo de cómo posiblemente suceden. La razón es que ambos necesitan un enlace interpretativo que permita extrapolar desde su propio sistema hacia un sistema objetivo. Los modelos tienen dominio general (...) y pueden dar cuenta de mecanismos. Los RCTs, al contrario, son válidos internamente y están conectados al mundo real, pero su dominio es muy específico. Aunque ninguno logre responder preguntas amplias de un fenómeno de interés, pueden complementarse para generar extrapolaciones más confiables sobre un sistema objetivo. Sin embargo, esto solo podrá hacerse si se conocen bien los mecanismos y el contexto en que ocurre una evidencia. Palabras clave: Causalidad, Mecanismos, Capacidades, Epistemología de la Economía, Generalización de Evidencia. (shrink)

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. (shrink)

To succeed, political science usually requires either prediction or contextual historical work. Both of these methods favor explanations that are narrow-scope, applying to only one or a few cases. Because of the difficulty of prediction, the main focus of political science should often be contextual historical work. These epistemological conclusions follow from the ubiquity of causal fragility, under-determination, and noise. They tell against several practices that are widespread in the discipline: wide-scope retrospective testing, such as much large-n statistical work; lack (...) of emphasis on prediction; and resources devoted to ‘pure theory’ divorced from frequent empirical application. I illustrate, via Donatella della Porta’s work on political violence, the important role that is still left for theory. I conclude by assessing the scope for political science to offer policy advice. (shrink)

Should we insist on prediction, i.e. on correctly forecasting the future? Or can we rest content with accommodation, i.e. empirical success only with respect to the past? I apply general considerations about this issue to the case of economics. In particular, I examine various ways in which mere accommodation can be sufficient, in order to see whether those ways apply to economics. Two conclusions result. First, an entanglement thesis: the need for prediction is entangled with the methodological role of orthodox (...) economic theory. Second, a conditional predictivism: if we are not committed to orthodox economic theory, then we should demand prediction rather than accommodation – against most current practice. (shrink)

In the human sciences, experimental research is used to establish causal relationships. However, the extrapolation of these results to the target population can be problematic. To facilitate extrapolation, we propose to use the statistical technique Latent Class Regression Analysis in combination with the analogical reasoning theory for extrapolation. This statistical technique can identify latent classes that differ in the effect of X on Y. In order to extrapolate by means of analogical reasoning, one can characterize the latent classes by a (...) combination of features, and then compare these features to features of the target. (shrink)

According to manipulationist accounts of causal explanation, to explain an event is to show how it could be changed by intervening on its cause. The relevant change must be a ‘serious possibility’ claims Woodward 2003, distinct from mere logical or physical possibility—approximating something I call ‘scientific possibility’. This idea creates significant difficulties: background knowledge is necessary for judgments of possibility. Yet the primary vehicles of explanation in manipulationism are ‘invariant’ generalisations, and these are not well adapted to encoding such knowledge, (...) especially in the social sciences, as some of it is non-causal. Ceteris paribus (CP) laws or generalisations labour under no such difficulty. A survey of research methods such as case and comparative studies, randomised control trials, ethnography, and structural equation modeling, suggests that it would be more difficult and in some instances impossible to try to represent the output of each method in invariant generalisations; and that this is because in each method causal and non-causal background knowledge mesh in a way that cannot easily be accounted for in manipulationist terms. Ceteris paribus-generalisations being superior in this regard, a theory of explanation based on the latter is a better fit for social science. (shrink)

Naturalistic philosophers rely on literature search and review in a number of ways and for different purposes. Yet this article shows how processes of literature search and review are likely to be affected by widespread and systematic biases. A solution to this problem is offered here. Whilst the tradition of systematic reviews of literature from scientific disciplines has been neglected in philosophy, systematic reviews are important tools that minimize bias in literature search and review and allow for greater reproducibility and (...) transparency. If naturalistic philosophers wish to reduce bias in their research, they should then supplement their traditional tools for literature search and review by including systematic methodologies. (shrink)

Summary measures of health, such as the quality-adjusted life year and disability-adjusted life year, have long been known to incorporate a number of value choices. In this paper, though, I show that the value choices in the construction of such measures extend far beyond what is generally recognized. In showing this, I hope both to improve the understanding of those measures by epidemiologists, health economists and policy-makers, and also to contribute to the general debate about the extent to which such (...) measures should be adjusted to reflect ethical values. (shrink)

ABSTRACTNormative political epistemologists, such as epistemic democrats, study whether political decision makers can, in principle, be expected to know what they need to know if they are to make wise public policy. Empirical political epistemologists study the content and sources of real-world political actors' knowledge and interpretations of knowledge. In recent years, empirical political epistemologists have taken up the study of the ideas of political actors other than voters, such as bureaucrats and politicians. Normative political epistemologists could follow this lead (...) if they were to focus on the technocratic orientation of nearly all political actors in the West: that is, on their desire to solve social and economic problems. Since most technocratic policy is made by political elites, the reliability of elites' knowledge of the causes of and cures for social and economic problems is a natural topic for normative political epistemology. (shrink)

Two approaches to evidential reasoning compete in the biomedical and social sciences: the experimental and the pragmatist. Whereas experimentalism has received considerable philosophical analysis and support since the times of Bacon and Mill, pragmatism about evidence has been neither articulated nor defended. The overall aim is to fill this gap and develop a theory that articulates the latter. The main ideas of the theory will be illustrated and supported by a case study on the smoking/lung cancer controversy in the 1950s.

Current philosophical accounts of causation suggest that the same causal assertion can have different meanings. Yet, in actual social-scientific practice, the possible meanings of some causal generalisations intended to support policy prescriptions are not always spelled out. In line with a standard referentialist approach to semantics, we propose and elaborate on four questions to systematically elucidate the meaning of causal generalisations. The analysis can be useful to a host of agents, including social scientists, policy-makers, and philosophers aiming at being socially (...) relevant. To illustrate our proposal, we analyse the complexities related to the meaning of causal generalisations in the context of a concrete case of economic research which is explicitly intended to guide public policy, namely, the OECD research on the causes of unemployment. (shrink)

The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, nor of regularity neither invariance, thus breaking down the dominant (...) Human paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models: e.g. Rubin’s model, contingency tables, and multilevel analysis. It is also shown to be latent – yet fundamental – in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability. This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science. (shrink)

This is a comprehensive anthology of works concerning the nature of economics as a science, including classic texts and essays exploring specific branches and schools of economics. Apart from the classics, most of the selections in the third edition are new, as are the introduction and bibliography. No other anthology spans the whole field and offers a comprehensive introduction to questions about economic methodology.

After decades of debates about species concepts, there is broad agreement that species are evolving lineages. However, species classification is still in a state of disorder: different methods of delimitation lead to competing outcomes for the same organisms, and the groups recognised as species are of widely different kinds. This paper considers whether this problem can be resolved by developing a unitary scale for evolutionary independence. Such a scale would show clearly when groups are comparable and allow taxonomists to choose (...) a conventional threshold of independence for species status. Existing measurement approaches to species delimitation are typically shot down by what I call the heterogeneity objection, according to which independently evolving groups are too heterogeneous to be captured by a single scale. I draw a parallel with the measurement of temperature to argue that this objection does not provide sufficient reasons to abandon the measurement approach, and that such an approach may even help to make the vague notion of evolutionary independence more precise. (shrink)

Epistemic democrats are rightly concerned with the quality of outcomes and judge democratic procedures in terms of their ability to ‘track the truth’. However, their impetus to assess ‘rule by experts’ and ‘rule by the people’ as mutually exclusive has led to a meagre treatment of the role of expert knowledge in democracy. Expertise is often presented as a threat to democracy but is also crucial for enlightened political processes. Contemporary political philosophy has so far paid little attention to our (...) reliance on experts and has not sufficiently addressed the question of how expertise can be used to improve the epistemic quality of democratic decision making. We believe this lack of interest is spurred by a too hasty acceptance of arguments dismissive of the political role of experts. The article examines a series of often-cited epistemic objections and concludes that several of them are overstated or misconceived, yet they all reflect real difficulties that need to be addressed. On this background, we tentatively outline a set of mechanisms that can contribute to alleviating the irreducible problem of epistemic asymmetries and ensuring that experts really are experts and use their expertise in the right way. (shrink)

This paper discusses the development of the field of economic methodology during the last few decades emphasizing the early influence of the "shelf" of Popperian philosophy and the division between neoclassical and heterodox economics. It argues that the field of methodology has recently adopted a more naturalistic approach focusing primarily on the "new pluralist" subfields of experimental economics, behavioral economics, neuroeconomics, and related subjects.

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. (shrink)

In this paper I analyze four so-called “principles of expertise”; that is, good epistemic practices that are normatively motivated by the epistemological literature on expert judgment. I highlight some of the problems that the four principles of expertise run into, when we try to implement them in concrete contexts of application (e.g. in science committees). I suggest some possible alternatives and adjustments to the principles, arguing in general that the epistemology of expertise should be informed both by case studies and (...) by the literature on the use of experts in science practice. (shrink)

WE AIM HERE to outline a theory of evidence for use. More specifically we lay foundations for a guide for the use of evidence in predicting policy effectiveness in situ, a more comprehensive guide than current standard offerings, such as the Maryland rules in criminology, the weight of evidence scheme of the International Agency for Research on Cancer (IARC), or the US ‘What Works Clearinghouse’. The guide itself is meant to be well-grounded but at the same time to give practicable (...) advice, that is, advice that can be used by policy-makers not expert in the natural and social sciences, assuming they are well-intentioned and have a reasonable but limited amount of time and resources available for searching out evidence and deliberating. (shrink)

Erik Angner has argued that simultaneous endorsement of the representational theory of measurement and psychometrics leads to inconsistency. His claim rests on an implicit assumption: RTM and psychometrics are full-fledged approaches to measurement. I argue that RTM and psychometrics are only partial approaches that deal with different aspects of measurement, and that therefore simultaneous endorsement of the two is not inconsistent. The argument has implications for the improvement of measurement practices.

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. (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)

Causal assessment is the problem of establishing whether a relation between (variable) X and (variable) Y is causal. This problem, to be sure, is widespread across the sciences. According to accredited positions in the philosophy of causality and in social science methodology, invariance under intervention provides the most reliable test to decide whether X causes Y. This account of invariance (under intervention) has been criticised, among other reasons, because it makes manipulations on the putative causal factor fundamental for the causal (...) methodology; consequently, the argument goes, the account is ill-suited to those contexts where manipulations are not performed, for instance, the social sciences. The article aims to extend the account of invariance (under intervention), in a way that manipulations on the putative causal factors are not methodologically fundamental, and yet invariance remains key for causal assessment both in experimental and non-experimental contexts. (shrink)

Laws of nature take center stage in philosophy of science. Laws are usually believed to stand in a tight conceptual relation to many important key concepts such as causation, explanation, confirmation, determinism, counterfactuals etc. Traditionally, philosophers of science have focused on physical laws, which were taken to be at least true, universal statements that support counterfactual claims. But, although this claim about laws might be true with respect to physics, laws in the special sciences (such as biology, psychology, economics etc.) (...) appear to have—maybe not surprisingly—different features than the laws of physics. Special science laws—for instance, the economic law “Under the condition of perfect competition, an increase of demand of a commodity leads to an increase of price, given that the quantity of the supplied commodity remains constant” and, in biology, Mendel's Laws—are usually taken to “have exceptions”, to be “non-universal” or “to be ceteris paribus laws”. How and whether the laws of physics and the laws of the special sciences differ is one of the crucial questions motivating the debate on ceteris paribus laws. Another major, controversial question concerns the determination of the precise meaning of “ceteris paribus”. Philosophers have attempted to explicate the meaning of ceteris paribus clauses in different ways. The question of meaning is connected to the problem of empirical content, i.e., the question whether ceteris paribus laws have non-trivial and empirically testable content. Since many philosophers have argued that ceteris paribus laws lack empirically testable content, this problem constitutes a major challenge to a theory of ceteris paribus laws. (shrink)

Francesco Guala once wrote that ‘The problem of extrapolation is a minor scandal in the philosophy of science’. This paper agrees with the statement, but for reasons different from Guala’s. The scandal is not, or not any longer, that the problem has been ignored in the philosophy of science. The scandal is that framing the problem as one of external validity encourages poor evidential reasoning. The aim of this paper is to propose an alternative—an alternative which constitutes much better evidential (...) reasoning about target systems of interest, and which makes do without consideration of external validity. (shrink)

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science , but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of (...) models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosophical problems that do come up in connection with simulations are not specific to simulations and most of them are variants of problems that have been discussed in other contexts before. (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)

Election prediction by means of opinion polling is a rare empirical success story for social science. I examine the details of a prominent case, drawing two lessons of more general interest: Methodology over metaphysics. Traditional metaphysical criteria were not a useful guide to whether successful prediction would be possible; instead, the crucial thing was selecting an effective methodology. Which methodology? Success required sophisticated use of case-specific evidence from opinion polling. The pursuit of explanations via general theory or causal mechanisms, by (...) contrast, turned out to be precisely the wrong path—contrary to much recent philosophy of social science. (shrink)

(No abstract is available for this citation).

Hasok Chang is developing a new form of pragmatic scientific realism that aims to reorient the debate away from truth and towards practice. Central to his project is replacing truth as correspondence with his new notion of ‘operational coherence’, which is introduced as: 1) A success term with probative value to judge and guide epistemic activities. 2) A more useful alternative than truth as correspondence in guiding scientific practice. I argue that, given its current construal as neither necessary nor sufficient (...) for success, operational coherence is too weak and fails to satisfy both 1) and 2). I offer a stronger construal of operational coherence which aims to improve on Chang’s account by tying it to systematic success. This makes operational coherence necessary and sufficient for success. This new account, if successful, rescues 1) but not 2). I then take a step back and try to locate Chang’s pragmatic realism within the broader pragmatist tradition by comparing his views to the founding fathers Peirce, James and Dewey. I also assess to what extent we should consider Chang’s position ‘realist’, arguing that despite the many relativists threads running through it, Chang’s pragmatic realism is deserving of the realist label because its aims to maximize our learning from reality, even if it falls short of what many traditional realist are happy to accept as realism. I finish with comments on the epistemology of science pointing out that there is nothing intrinsic about a practice-based philosophy of science that precludes having both operational coherence and correspondence and highlighting that given a proper understanding these two notions could, in fact, be understood as complementary. I suggest one way this could be done. (shrink)

Policy-makers face an uncertain world. One way of getting a handle on decision-making in such an environment is to rely on evidence. Despite the recent increase in post-fact figures in politics, evidence-based policymaking takes centre stage in policy-setting institutions. Often, however, policy-makers face large volumes of evidence from different sources. Robustness analysis can, prima facie, handle this evidential diversity. Roughly, a hypothesis is supported by robust evidence if the different evidential sources are in agreement. In this thesis, I strengthen the (...) case for the use of robustness analysis in evidence-based policymaking by answering open research questions about this inference technique. First, I argue that existing taxonomies miss a fruitful category of robustness reasoning, that is predictive stability. Second, I claim that derivational robustness analysis – the investigation of whether the results of different models are in agreement – can yield interesting insights even if not the entire relevant model space is covered by available models or if the model results are only partially in agreement. Third, I claim that expert knowledge is necessary to address questions that arise when one applies measurement robustness analysis – the investigation into whether multiple means of measurement yield the same result. Finally, I argue that, in situations where evidence from different measurements is not in agreement, it can be advisable to no longer take all of the evidence into account. This can be done in a rationally defensible way by choosing the most adequate theory or model underlying parts of the evidence set. I discuss examples from climate, medical, and economic policy-making to establish my claims. (shrink)

How can we use models to understand real phenomena if models misrepresent the very phenomena we seek to understand? Some accounts suggest that models may afford understanding by providing causal knowledge about phenomena via how-possibly explanations. However, general equilibrium models, for example, pose a challenge to this solution since their contribution appears to be purely mathematical results. Despite this, practitioners widely acknowledge that it improves our understanding of the world. I argue that the Arrow–Debreu model provides a mathematical how-possibly explanation (...) which establishes claims of mathematical dependence. The account developed reveals how mathematical knowledge can inform claims about the world, allow ‘what-if-things-had-been-different’ inferences, and thus improve our understanding. (shrink)

This paper aims (a) to provide characterizations of realism and instrumentalism that are philosophically interesting and applicable to economics; and (b) to defend instrumentalism against realism as a methodological stance in economics. Starting point is the observation that ‘all models are false’, which, or so I argue, is difficult to square with the realist's aim of truth, even if the latter is understood as ‘partial’ or ‘approximate’. The three cheers in favour of instrumentalism are: (1) Once we have usefulness, truth (...) is redundant. (2) There is something disturbing about causal structure. (3) It's better to do what one can than to chase rainbows. (shrink)

This paper looks at an appeal to the authority of biomedical research that has recently been used by empirical economists to motivate and justify their methods. I argue that those who make this appeal mistake the nature of biomedical research. Randomised trials, which are said to have revolutionised biomedical research, are a central methodology, but according to only one paradigm. There is another paradigm at work in biomedical research, the inferentialist paradigm, in which randomised trials play no special role. I (...) outline the inferentialist alternative in broad strokes, apply it to a recent controversy in econometrics and draw some general conclusions concerning econometric methodology. (shrink)

In this paper, I contend that evidence-focused strategies of science communication may be complemented by possibly more effective rhetorical arguments in current public debates on vaccines. I analyse the case of direct science communication - that is, communication of evidence - and show that it is difficult to effectively communicate evidential standards of science in the presence of well-equipped anti-science movements. Instead, I argue that effective rhetorical tools involve ad hominem strategies, that is, arguments involving claims of expertise. I provide (...) a rationale, and sketch a methodology, for using ad hominem arguments in science communication. (shrink)

In the last decades philosophers of science and social scientists promoted the view that knowledge of mechanisms might help causal inference considerably in the social sciences. Mechanisms, however, can only assist causal inference effectively if scientists have a means to identify them correctly. Some scholars suggested that process-tracing might be a helpful strategy in this respect. Shared criteria to assess its performance, however, are not available yet; furthermore, the criteria proposed so far tie the validity of process-tracing findings to the (...) specific kind of evidence it uses. In this paper I shall propose a criterion to assess process-tracing performance in cases in which favorable epistemic circumstances do not occur and the existing criteria thus fail to apply. The criterion I propose does not double as a condition for validity. Rather, it aims to assess whether the mechanism process-tracing outlines constitutes admissible evidence for the hypothesis at hand. It will be argued that only if this requirement is fulfilled process-tracing can be used as an effective complement for causal inference. (shrink)

Psychometrics and the representational theory of measurement are widely used in social scientific measurement. They are currently pursued largely in isolation from one another. I argue that despite their separation in practice, RTM and psychometrics are complementary approaches, because they can contribute in complementary ways to the establishment of what I argue is a crucial measurement property, namely, representational interpretability. Because RTM and psychometrics are complementary in the establishment of representational interpretability, the current separation of measurement approaches is unfounded. 1Introduction2Two (...) Approaches to Measurement 2.1Representational theory of measurement2.2Psychometrics2.3Representational interpretability3Complementarity, Conceptually 3.1Representational theory of measurement: Conditions of representational interpretability3.2Psychometrics: Evidence of representational interpretability4Complementarity in Action 4.1What is the Rasch model?4.2Rasch and conjoint measurement5Conclusion: Critics and Fruits of Complementarity. (shrink)

Much philosophical attention has been devoted to whether economic models explain, and more generally to how scientific models represent. Yet there is an issue more practically important to economics than either of these, which I label the efficiency question: regardless of how exactly models represent, or of whether their role is explanatory or something else, is current modeling practice an efficient way to achieve these goals – or should research efforts be redirected? In addition to showing how the efficiency question (...) has been relatively neglected, I give two examples of the kind of analysis it requires. (shrink)

Le projet philosophique puise ses sources dans la volonté de proposer une explication rationnelle des phénomènes naturels et culturels qui constituent le monde dans lequel l’être humain prend place. Il a servi de trame culturelle à l’Occident et amené le déploiement d’appareils conceptuels où l’on distingue théorie et pratique. On doit toutefois reconnaître qu’une philosophie théorique peut avoir une portée pratique et l’inverse, puisque toute pratique suppose un ancrage théorique pour légitimer la connaissance et les systèmes normatifs à partir desquels (...) les explications sont avancées. Cela fait dire à certains que la distinction est désormais surannée et qu’une théorie philosophique réunifiée s’impose. Que l’on soit tenant de la tradition ou des nouvelles approches, la philosophie pratique s’impose désormais comme un passage obligé pour réfléchir les problèmes et articuler de nouvelles théories faisant écho à l’état actuel de notre monde. Le présent ouvrage revient sur la notion de philosophie pratique afin d’en faire voir les différentes acceptions et manières de la mobiliser pour réfléchir à des problèmes actuels. (shrink)

One can find in the literature two sets of views concerning the relationship between understanding and explanation: that one understands only if 1) one has knowledge of causes and 2) that knowledge is provided by an explanation. Taken together, these tenets characterize what I call the narrow knowledge account of understanding. While the first tenet has recently come under severe attack, the second has been more resistant to change. I argue that we have good reasons to reject it on the (...) basis of theoretical models that provide how-possibly explanations. These models, while they do not explain in the strict sense, afford understanding. In response, I propose an alternative epistemology of understanding, broad KAU, that takes cases of theoretical modelling into account. (shrink)

Science progresses through debate and disagreement, and scientific controversies play a crucial role in the growth of scientific knowledge. However, not all controversies and disagreements are progressive in science. Sometimes, controversies can be pseudoscientific; in fact, bogus controversies, and what seem like genuine scientific disagreements, can be a distortion of science set up by non-scientific actors. Bogus controversies are detrimental to science because they can hinder scientific progress and eventually bias science-based decisions. The first goal of this paper is to (...) elucidate the distinction between bogus and genuine scientific controversies and provide a qualitative methodology, based on the literature on expertise, for distinguishing between the two. We will illustrate six epistemic criteria for distinguishing bogus from genuine scientific debates in science and medicine. This heuristic strategy applies directly to scientific reports, and it relies mostly on the social structure of science. We will then apply the above criteria to a case study: the controversy over statins, which are widely prescribed drugs for reducing the level of cholesterol and preventing cardiovascular disease. (shrink)

The Representational Theory of Measurement is the best known account of the kind of representation measurement requires. However, RTM has been challenged from various angles, with critics claiming e.g. that RTM fails to account for actual measurement practice and that it is ambiguous about the nature of measurable attributes. In this paper I use the critical literature on RTM to formulate Representation Minimalism – a characterization of what measurement-relevant representation requires at the minimum. I argue that Representation Minimalism avoids the (...) main problems with RTM while acknowledging its usefulness as the formal foundation of representation in measurement. (shrink)

Economists reason causally. Like many other scientists, they aim at formulating justified causal claims about their object of study. This thesis contributes to our understanding of how causal reasoning proceeds in economics. By using the research on the causes of unemployment as a case study, three questions are adressed. What are the meanings of causal claims? How can a causal claim be adequately supported by evidence? How are causal beliefs affected by incoming facts? In the process of answering these semantic, (...) epistemic and dynamical questions, the richness of causal reasoning in economics is brought to the foreground. In the part on semantics, it is argued that the meaning of causal generalizations in policy-oriented economics is best captured by using the concept of ‘inferential relation’ rather than the one of ‘referential relation’. This part contributes to develop an inferentialist semantics by distinguishing among types of inferential relations. In the part on epistemology, it is argued that justification in sciences like economics often relies, and ought to rely, on evidential variety―i.e. the combination of evidence from multiple sources. An interpretation of independence among evidential sources is offered: two evidential sources are fully independent if and only if the reasons why they would be unreliable do not overlap. It is then shown that the variety-of-evidence thesis does not hold under extreme circumstances. The part on dynamics focuses on deviant-case study. A storyline is provided for what deviant-case study aims at and how it proceeds in what is characterized as an ‘eclectic science’. (shrink)

. This article analyzes the epistemic contributions of models by distinguishing three roles that they might play: an evidential role, a revealing role and a stimulating role. By using an account of learning based on the philosophical understanding of propositional knowledge as true justified belief, the paper provides the conditions to be fulfilled by a model in order to play a determined role. A case study of an economic model of the labor market—the DMP model—illustrates the usefulness of these conditions (...) in articulating debates over the epistemic contributions of a given model. (shrink)

This thesis attempts to justify a normative role for methodology by sketching a pragmatic way out of the dichotomy between two major strands in economic methodology: empiricism and postmodernism. I discuss several methodological approaches and assess their aptness for theory appraisal in economics. I begin with the most common views on methodology and argue why they are each ill-suited for giving methodological prescriptions to economics. Then, I consider positions that avoid the errors of empiricism and postmodernism. I specifically examine why (...) the two major strands of methodological criticism fail to give helpful methodological advice to economists and sketch out a pragmatic approach that can do this. (shrink)

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