François Claveau

This article maintains that an important class of scientific generalizations should be reinterpreted: they have typically been understood as ceteris paribus laws, but are, in fact, generics. Four arguments are presented to support this thesis. One argument is that the interpretation in terms of ceteris paribus laws is a historical accident. The other three arguments draw on similarities between these generalizations and archetypal generics: they come with similar inferential commitments, they share a syntactic form, and the existing theories to make sense of them are alike. Once these generalizations are properly understood as generics, the recent cognitive approach to generics can be extended to the study of the relevant sciences. The last section indicates ways in which this extension is fruitful for the two strands of research that we combine: the philosophy of science literature on generalizations and the semantics literature on generics.

The variety-of-evidence thesis has been criticized by Bovens and Hartmann. This article points to two limitations of their Bayesian model: the conceptualization of unreliable evidential sources as randomizing and the restriction to comparing full independence to full dependence. It is shown that the variety-of-evidence thesis is rehabilitated when unreliable sources are reconceptualized as systematically biased. However, it turns out that allowing for degrees of independence leads to a qualification of the variety-of-evidence thesis: as Bovens and Hartmann claimed, more independence does not always imply stronger confirmation.

What is the relation between monetary policy and inequalities in income and wealth? This question has received insufficient attention, especially in light of the unconventional policies introduced since the 2008 financial crisis. The article analyzes three ways in which the concern central banks show for inequalities in their official statements remains incomplete and underdeveloped. First, central banks tend to care about inequality for instrumental reasons only. When they do assign intrinsic value to containing inequalities, they shy away from trade-offs with the standard objectives of monetary policy that such a position entails. Second, central banks play down the causal impact monetary policy has on inequalities. When they do acknowledge it, they defend their actions by claiming that it is an unintended side effect, that it is temporary, and/or that any alternative policy would fare even worse. The article appeals to the doctrine of double effect to criticize these arguments. Third, even if one accepts that inequalities should be contained and that today’s monetary policies exacerbate them, is it both desirable and feasible to make containing inequalities part of the mandate of central banks? The article analyzes and rejects three attempts on the part of central banks to answer this question negatively.

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

This article examines two theses formulated by Russo and Williamson in their study of causal inference in the health sciences. The two theses are assessed against evidence from a specific case in the social sciences, i.e., research on the institutional determinants of the aggregate unemployment rate. The first Russo–Williamson Thesis is that a causal claim can only be established when it is jointly supported by difference-making and mechanistic evidence. This thesis is shown not to hold. While researchers in my case study draw extensively on both types of evidence, one causal claim out of the three analyzed is established even though it is exclusively supported by mechanistic evidence. The second Russo–Williamson Thesis is that standard accounts of causality fail to handle the dualist epistemology highlighted in the first Thesis. I argue that a counterfactual-manipulationist account of causality—which is endorsed by many philosophers as well as many social scientists—can perfectly make sense of the typical strategy in my case study to draw on both difference-making and mechanistic evidence; it is just an instance of the common strategy of increasing evidential variety

. 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.

Diversity of evidence is widely claimed to be crucial for evidence amalgamation to have distinctive epistemic merits. Bayesian epistemologists capture this idea in the variety-of-evidence thesis: ceteris paribus, the strength of confirmation of a hypothesis by an evidential set increases with the diversity of the evidential elements in that set. Yet, formal exploration of this thesis has shown that it fails to be generally true. This article demonstrates that the thesis fails in even more circumstances than recent results would lead us to expect. Most importantly, it can fail whatever the chance that the evidential sources are unreliable. Our results hold for two types of degrees of variety: reliability independence and testable aspect independence. We conclude that the variety-of-evidence thesis can, at best, be interpreted as an exception-prone rule of thumb.

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’.

The most common way of analysing the meaning of causal generalisations relies on referentialist semantics. In this article, we instead develop an analysis based on inferentialist semantics. According to this approach, the meaning of a causal generalisation is constituted by the web of inferential connections in which the generalisation participates. We distinguish and discuss five classes of inferential connections that constitute the meaning of causal generalisations produced in policy-oriented economic research. The usefulness of our account is illustrated with the analysis of generalisations about unemployment put forward by the Organisation for Economic Co-operation and Development in its highly influential 1994 OECD Jobs Study. The article ends with a discussion of some crucial philosophical questions about the use of inferentialism in the analysis of causal generalisations.

Diversity of evidence is widely claimed to be crucial for evidence amalgamation to have distinctive epistemic merits. Bayesian epistemologists capture this idea in the variety-of-evidence thesis: ceteris paribus, the strength of confirmation of a hypothesis by an evidential set increases with the diversity of the evidential elements in that set. Yet, formal exploration of this thesis has shown that it fails to be generally true. This article demonstrates that the thesis fails in even more circumstances than recent results would lead us to expect. Most importantly, it can fail whatever the chance that the evidential sources are unreliable. Our results hold for two types of degrees of variety: reliability independence and testable aspect independence. We conclude that the variety-of-evidence thesis can, at best, be interpreted as an exception-prone rule of thumb.

This article elaborates a conceptual framework to systematize the epistemic evaluation of social systems. This framework can be used to structure an evaluation or to characterize and assess existing ones. The article then uses the framework to assess four representative evaluations of think tanks. This meta-evaluation exemplifies how the framework can play its structuring role. It also leads us to general conclusions about the existing evaluations of think tanks. Most importantly, by focusing on the organizational level, existing evaluations miss factors that are situated at the network and ecosystemic levels and that significantly determine how well think tanks serve society in producing and disseminating knowledge relevant to public policy. This conclusion suggests the need for epistemic evaluations of think tank ecosystems.

An individual's preferences are interdependent when they can be influenced by the behaviour of other agents. This paper analyzes the internal dynamics of an approach in contemporary economics allowing for interdependent preferences, the extended utility approach, which presents itself as a mild reform of neoclassical economics. I contend that this approach succeeds in broadening the policy perspectives of mainstream economics by challenging neoclassical policy stances. However, this success comes with a limitation: the EUA is unable to supply new consensual policy stances as alternatives to the challenged ones. The reason for this limitation is that the EUA opens the possibility of a wide variety of specifications for the utility function, and policy conclusions are sensitive to the details of these specifications.

Why do policymakers and economists within a policymaking institution choose to throw away a model and to develop an alternative one? Why do they choose to stick to an existing model? This article contributes to the literature on the history and philosophy of modelling by answering these questions. It delves into the dynamics of persistence, change, and building practices of macroeconomic modelling, using the case of forecasting models at the Bank of England (1974–2014). Based on archives and interviews, we document the multiple factors at play in model building and model change. We identify three sets of factors: the agency of modellers, institutional factors, and the material factor. Our investigation shows the diversity of explanations behind the decision to change a model: each time, model replacement resulted from a different combination of the three types of factors.

In this chapter, we discuss social network analysis as a method for the history of economics. We argue that social network analysis is not primarily a method of data representation but foremost a method of discovery and confirmation. It is as such a promising method that should be added to the toolbox of the historian of economics. We furthermore argue that, to be meaningfully applied in history, social network analysis must be complemented with historical knowledge gained by other means and often by more traditional, mostly qualitative, methods. It should therefore be viewed as a method that complements rather than replaces more established approaches in the history of economics.