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)

One puzzle concerning highly idealized models is whether they explain. Some suggest they provide so-called ‘how-possibly explanations’. However, this raises an important question about the nature of how-possibly explanations, namely what distinguishes them from ‘normal’, or how-actually, explanations? I provide an account of how-possibly explanations that clarifies their nature in the context of solving the puzzle of model-based explanation. I argue that the modal notions of actuality and possibility provide the relevant dividing lines between how-possibly and how-actually explanations. Whereas how-possibly (...) explanations establish claims of possible explanations, how-actually explanations establish claims of actual ones. Models, in turn, simply provide evidence for these 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)

As the title of this essay suggests, my concern is with the issue of what are economic models. However, the goal of the paper is not to offer an in-depth study on multiple approaches to modelling in economics, but rather to overcome the dichotomical divide between conceptualizing models as isolations and constructions. This is done by introducing the idea of economic models as believable worlds, precisely descriptions of mechanisms that refer to the essentials of the modelled targets. In doing so (...) I make use of the Woodward’s (2002) conceptualization of mechanisms. It is shown that such models do not offer the perfectly true descriptions of the actual world but justified beliefs about the modelled, precisely they aim at maximizing truth and minimizing falsity in a large body of belief about the real world. The analysis throughout the paper is supported by in-depth examination of the Varian’s (1980) model of sales that is here treated as a representative way of reasoning in neoclassical economics. (shrink)

This article examines the issue of whether consideration of so-called minimal models can prompt learning about real-world targets. Using a widely cited example as a test case, it argues against the increasingly popular view that consideration of minimal models can prompt learning about such targets. The article criticizes influential defenses of this view for failing to explicate by virtue of what properties or features minimal models supposedly prompt learning. It then argues that consideration of minimal models cannot prompt learning about (...) real-world targets unless one supplements these models with additional information or presuppositions concerning such targets. (shrink)

Philosophers of science have developed several accounts of how consideration of scientific models can prompt learning about real-world targets. In recent years, various authors advocated the thesis that consideration of so-called minimal models can prompt learning about such targets. In this paper, I draw on the philosophical literature on scientific modelling and on widely cited illustrations from economics and biology to argue that this thesis fails to withstand scrutiny. More specifically, I criticize leading proponents of such thesis for failing to (...) explicate in virtue of what properties or features minimal models supposedly prompt learning and for substantially overstating the epistemic import of minimal models. I then examine and refute several arguments one may put forward to demonstrate that consideration of minimal models can prompt learning about real-world targets. In doing so, I illustrate the implications of my critique for the wider debate on the epistemology of scientific modelling. (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)

Science is replete with falsehoods that epistemically facilitate understanding by virtue of being the very falsehoods they are. In view of this puzzling fact, some have relaxed the truth requirement on understanding. I offer a factive view of understanding that fully accommodates the puzzling fact in four steps: (i) I argue that the question how these falsehoods are related to the phenomenon to be understood and the question how they figure into the content of understanding it are independent. (ii) I (...) argue that the falsehoods do not figure into the understanding’s content by being elements of its periphery or core. (iii) Drawing lessons from case studies, I argue that the falsehoods merely enable understanding. When working with such falsehoods, only the truths we extract from them are elements of the content of our understanding. (iv) I argue that the extraction view is compatible with the thesis that falsehoods can have an epistemic value by virtue of being the very falsehoods they are. (shrink)

Scientists appeal to models when explaining phenomena. Such explanations are often dubbed model explanations or model-based explanations. But what are the precise conditions for ME? Are ME special explanations? In our paper, we first rebut two definitions of ME and specify a more promising one. Based on this analysis, we single out a related conception that is concerned with explanations that are induced from working with a model. We call them ‘model-induced explanations’. Second, we study three paradigmatic cases of alleged (...) ME. We argue that all of them are MIE, upon closer examination. Third, we argue that this undermines the building consensus that model explanations are special explanations that, e.g., challenge the factivity of explanation. Instead, it suggests that what is special about models in science is the epistemology behind how models induce explanations. (shrink)