Empirical adequacy matters directly - as it does for antirealists - if we aim to get all or most of the observable facts right, or indirectly - as it does for realists - as a symptom that the claims we make about the theoretical facts are right. But why should getting the facts - either theoretical or empirical - right be required of an acceptable theory? Here we endorse two other jobs that good theories are expected to do: helping us (...) with a) understanding and b) managing the world. Both are of equal, often greater, importance than getting a swathe of facts right, and empirical adequacy fares badly in both. It is not needed for doing these jobs and in many cases it gets in the way of doing them efficiently. (shrink)

In this paper I challenge the pernicious aspects of Milton Friedman's methodological outlook that continues to hold sway over mainstream neoclassical economists. I do this by showing how Friedman's own methodological dicta could have been used against him when he famously advanced the expectations critique of the Phillips curve at his presidential address to the American Economic Association. I use this case study to further suggest that psychological and neurophysiological data should not be deemed irrelevant to economic science.

Highly idealized models may serve various epistemic functions, notably explanation, in virtue of representing the world. Inferentialism provides a prima facie compelling characterization of what constitutes the representation relation. In this paper, I argue that what I call factive inferentialism does not provide a satisfactory solution to the puzzle of model-based—factive—explanation. In particular, I show that making explanatory counterfactual inferences is not a sufficient guide for accurate representation, factivity, or realism. I conclude by calling for a more explicit specification of (...) model-world mismatches and properties imputation. (shrink)

The description of how individual models in families of models are related to each other is crucial for the general philosophical understanding of model-based scientific practice. We focus on the Capital Asset Pricing Models (CAPM) family, a cornerstone in financial economics, to provide a descriptive analysis of model relations within a family. We introduce the concepts of theoretical and empirical complementarity to characterise model relations. Our complementarity analysis of model relations has two types of payoff. Specifically regarding the CAPM, our (...) analysis reveals why this model family, which has been empirically contested, has yet remained popular and important: the different models that have been added over time have made new empirically and theoretically complementary contributions to the model family. More generally, our analysis reveals the dynamic character of model-based scientific practice. Characterising relations between models as theoretically and/or empirically complementary yields three hitherto underappreciated lessons: (i) actual modelling purposes are not always primarily epistemic, (ii) a model's individual import is relative, not absolute, and (iii) there is an important interplay between theory and data models. Faithfully characterising scientific modelling in this way facilitates subsequent analysis of models' epistemic import. (shrink)

This article starts by defining instrumentalized knowledge (IK) as the practice of selectively valuing some set of reliable beliefs for the promotion of a more generally false or unreliable worldview. IK is typically exploited by conspiratorial echo chambers, which display systematic distrust and opposition towards mainstream epistemic authorities. We argue that IK is problematic in that it violates core epistemic virtues, and this gives rise to clear and present harms when abused by said echo chambers. Yet, we contend, mainstream epistemic (...) authorities (MEAs) are also complicit in practices resembling IK; we refer to these practices as instrumentalized knowledge* (IK*). IK* differs from IK in that the selective valuing of beliefs corresponds to a ”reliable” worldview, namely, one independently verified by the relevant epistemic experts. We argue that IK*, despite its apparent veracity, is also problematic, as it violates the same epistemic virtues as IK despite its aim of promoting true beliefs. This, we argue, leads it to being counterproductive in its goal of producing knowledge for the sake of the pursuit of truth, thereby raising the question of what distinguishes virtuous from nonvirtuous practices of instrumentalized knowledge. In an attempt to avoid this violation and to distinguish IK* from IK, we investigate whether and how IK* could still be epistemically virtuous. We conclude that IK* can be virtuous if its goal is to produce understanding as opposed to mere knowledge. (shrink)

In a number of very influential publications, Epstein and Hoover (among other authors) have recently argued that a thoroughly micro-foundationalist approach towards economics is unconvincing for metaphysical reasons. However, as we show in this article, this metaphysical/social ontological approach to the debate fails to resolve the status of micro-foundations in the practice of economic modelling. To overcome this, we argue that endogenizing a model—that is, providing micro-foundations for it—correlates with making that model more complex. Specifically, we show that models with (...) more micro-foundations tend to have more parameters or variables. This matters, as there are well-established methodological reasons for preferring models with fewer parameters or variables—ceteris paribus. We therefore conclude that, from a practice-based point of view, micro-foundations are only defensible to the extent that they significantly improve the ability of the relevant model to fit the data of interest. In this way, we arrive at a practice-based, methodological reconceptualization of the debate surrounding the need for micro-foundations in economics. (shrink)

I respond to some challenges raised by my critics. In particular, I argue in favour of six claims. First, against Alexandrova and Northcott, I point out that to deny the explanatoriness of economic models by assuming an ontic (specifically, causal) conception of explanation is to beg the question. Second, against defences of causal realism (by Hausman, Mäki, Rol and Grüne-Yanoff) I point out that they have provided no criterion to distinguish those claims a model makes that can be interpreted realistically (...) (the model's ?causal content? or claims it makes about causal powers or mechanisms) and those the realist can safely ignore. Third, I point out that Hausman's and Rol's claims about robustness plus the empirical observation that economic models are hardly ever robust to the right kinds of specification changes imply that these models do not explain (which is problematic as my original article argued). Fourth, I point out that Sugden's response still leaves an important question unanswered, viz. what makes economic models explanatory. Fifth, I sketch an alternative, instrumentalist account of explanation and argue that it would fit Sugden's bill. Sixth, I point out that under this account of explanation, economic models would come out as non-explanatory, which brings us back to Alexandrova and Northcott's account and its associated difficulties. The ?explanation paradox?, therefore, remains unscathed. (shrink)

This paper defends the viability of de-idealization strategies in economic modeling against recent criticism. De-idealization occurs when an idealized assumption of a theoretical model is replaced with a more realistic one. Recently, some scholars have raised objections against the possibility or fruitfulness of de-idealizing economic models, suggesting that economists do not employ this kind of strategy. We present a detailed case study from the theory of industrial organization, discussing three different models, two of which can be construed as de-idealized versions (...) of the first. We conclude that recent pessimism about de-idealization in economics is largely unfounded, and that de-idealization strategies are not only possible but also widely employed in economics. (shrink)

At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, size (...) of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold. (shrink)