In a recent special issue dedicated to Dani Rodrik’s (2015) influential monograph Economics Rules, Grüne-Yanoff and Marchionni (2018) raise a potentially damning problem for Rodrik’s suggestion that progress in economics should be understood and measured laterally, by a continuous expansion of new models. They argue that this could lead to an “embarrassment of riches”, i.e. the rapid expansion of our model library to such an extent that we become unable to choose between the available models, and thus needs to be (...) solved to make ‘model pluralism’ viable. Drawing on Veit’s (2019a) ‘model pluralism’ account, this paper argues that model pluralism as a thesis about the relationship between science and nature undermines the very idea of a general model selection framework for policy making. (shrink)

It is undeniable that much scientific work is model-based. Despite this, the justification for this reliance on models is still controversial. A particular difficulty here is the fact that many scientific models are based on assumptions that do not describe the exact details of many or even any empirical situations very well. This raises the question of why it is that, despite their frequent lack of descriptive accuracy, employing models is scientifically useful.One..

It is argued that Reiss (2012) fails to refute attempts to resolve the paradox of false explanatory models. His article fails to provide an articulate conception of what exactly the presumed paradox is, it suffers from uncontrolled ambiguities and inconsistencies, and it fails to adequately address accounts of economic models that might contribute to reconciling their apparent falsehood and explanatoriness. Some details in my account of how apparently false models may explain are clarified.

In the philosophy of science and epistemology literature, robustness analysis has become an umbrella term that refers to a variety of strategies. One of the main purposes of this paper is to argue that different strategies rely on different criteria for justifications. More specifically, I will claim that: i) robustness analysis differs from de-idealization even though the two concepts have often been conflated in the literature; ii) the comparison of different model frameworks requires different justifications than the comparison of models (...) that differ only for the assumption under test; iii) the replacement of specific assumptions with different ones can encounter specific difficulties in scientific practice. These claims will be supported by a case study in population ecology and a case study in geographical economics. (shrink)

This paper supports the literature which argues that derivational robustness can have epistemic import in highly idealized economic models. The defense is based on a particular example from mathematical economic theory, the dynamic Walrasian general equilibrium model. It is argued that derivational robustness first increased and later decreased the credibility of the Walrasian model. The example demonstrates that derivational robustness correctly describes the practices of a particular group of influential economic theorists and provides support for the arguments of philosophers who (...) have offered a general epistemic justification of such practices. (shrink)

ABSTRACTIn his recent book, Rodrik [. Economics rules. Why economics works, when it fails, and how to tell the difference. Oxford University Press] proposes an account of model pluralism according to which multiple models of the same target are acceptable as long as one model is more useful for one purpose and another is more useful for another purpose. How, then, is the right model for the purpose selected? Rodrik roughly outlines a selection procedure, which we formalize to enhance understanding (...) of his account of model pluralism and to advance the critical discussion. (shrink)

Policymakers increasingly draw on scientific methods, including simulation modeling, to justify their decisions. For these purposes, scientists and policymakers face an extensive choice of modeling strategies. Discussing the example of smallpox epidemiology, this paper distinguishes three types of strategies: Massive Simulation Models (MSMs), Abstract Simulation Models (ASMs) and Macro Equation Models (MEMs). By analyzing some of the main smallpox epidemic models proposed in the last 20 years, it discusses how to justify strategy choice with reference to the core characteristics of (...) these respective strategies. First, I argue that MEMs often suffice for policy purposes, and need to be replaced only if they are insufficiently robust. Such robustness results, however, only requires the use of ASMs, not MSMs. Second, I argue that although MSMs have larger potentials than ASMs in various dimensions, they are also more likely to fail—and that in many cases, this probability of failing outweighs their higher potential. In particular, these dimensions include the representation of the relevant target, the accurate measurement of the relevant parameters, the number of parameters included, the number of mechanisms modeled simultaneously, and the ways of dealing with structural uncertainty. While this in no way excludes the prospect that some MSMs provide good justifications for policy decisions, my arguments caution against a general preference for MSMs over ASMs for policy decision purposes in general and vaccination problems in particular. (shrink)

Many scientific models lack an established representation relation to actual targets and instead refer to merely possible processes, background conditions, and results. This article shows how such models can be appraised. On the basis of the discussion of how-possibly explanations, five types of learning opportunities are distinguished. For each of these types, an example—from economics, biology, psychology, and sociology—is discussed. Contexts and purposes are identified in which the use of a model offers a genuine opportunity to learn. These learning opportunities (...) offer novel justifications for modeling practices that fall between the cracks of standard representationalist appraisals of models. (shrink)

What can we learn from “minimal” economic models? I argue that learning from such models is not limited to conceptual explorations—which show how something could be the case—but may extend to explanations of real economic phenomena—which show how something is the case. A model may be minimal qua certain world-linking properties, and yet “not-so-minimal” qua learning, provided it is externally valid. This, in turn, depends on using the right principles for model building and not necessarily “isolating” principles. My argument is (...) buttressed by a case study from computational economics, namely, two agent-based models of asset pricing. (shrink)

Even though Popper, Lawson and Mäki are realists, the three of them understand by realism something different and support different positions on the use of models in economics. In this article we will compare the three proposals on their conceptions of reality, the function and the nature of economic models and their use to study the social world.