. 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)

Economic methodologists most often study the relations between models and reality while focusing on the issues of the model's epistemic relevance in terms of its relation to the ‘real world’ and representing the real world in a model. We complement the discussion by bringing the model's constructive mechanisms or self-implementing technologies in play. By this, we mean the elements of the economic model that are aimed at ‘implementing’ it by envisaging the ways to change the reality in order to bring (...) it more in line with the model. We are thus concerned mainly not with the ways to change the model to ‘fit’ the reality, but rather with the model's own armature that is supposed to transform the world along theoretical lines. The case we study is Arrow–Debreu–McKenzie general equilibrium model. In particular, we show the following: gradient methods and stability could be regarded as constructive mechanisms of general equilibrium modeling in the context of market socialism debates; the obsession of general eq.. (shrink)

This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story (...) explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems. (shrink)

This paper discusses the epistemic import of highly abstract and simplified theoretical models using Thomas Schelling’s checkerboard model as an example. We argue that the epistemic contribution of theoretical models can be better understood in the context of a cluster of models relevant to the explanatory task at hand. The central claim of the paper is that theoretical models make better sense in the context of a menu of possible explanations. In order to justify this claim, we introduce a distinction (...) between causal scenarios and causal mechanism schemes. These conceptual tools help us to articulate the basis for modelers’ intuitive confidence that their models make an important epistemic contribution. By focusing on the role of the menu of possible explanations in the evaluation of explanatory hypotheses, it is possible to understand how a causal mechanism scheme can improve our explanatory understanding even in cases where it does not describe the actual cause of a particular phenomenon. (shrink)

Ian Hacking uses the looping effect to describe how classificatory practices in the human sciences interact with the classified people. While arguably this interaction renders the affected human kinds unstable and hence different from natural kinds, realists argue that also some prototypical natural kinds are interactive and human kinds in general are stable enough to support explanations and predictions. I defend a more fine-grained realist interpretation of interactive human kinds by arguing for an explanatory domain account of the looping effect. (...) First, I argue that knowledge of the feedback mechanisms that mediate the looping effect can supplement, and help to identify, the applicability domain over which a kind and its property variations are stably explainable. Second, by applying this account to cross-cultural case studies of psychiatric disorders, I distinguish between congruent feedback mechanisms that explain matches between classifications and kinds, and incongruent feedback mechanisms that explain mismatches. For example, congruent mechanisms maintain Western auditory experiences in schizophrenia, whereas exporting diagnostic labels inflicts incongruence by influencing local experiences. Knowledge of the mechanisms can strengthen explanatory domains, and thereby facilitate classificatory adjustments and possible interventions on psychiatric disorders. (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)

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)

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)

Models not only represent but may also influence their targets in important ways. While models’ abilities to influence outcomes has been studied in the context of economic models, often under the label ‘performativity’, we argue that this phenomenon also pertains to epidemiological models, such as those used for forecasting the trajectory of the Covid-19 pandemic. After identifying three ways in which a model by the Covid-19 Response Team at Imperial College London may have influenced scientific advice, policy, and individual responses, (...) we consider the implications of epidemiological models’ performative capacities. We argue, first, that performativity may impair models’ ability to successfully predict the course of an epidemic; but second, that it may provide an additional sense in which these models can be successful, namely by changing the course of an epidemic. (shrink)

Scientific models are used to predict and understand the target phenomena in the reality. The kind of epistemic relationship between the model and the reality is always regarded by most of the philosophers as a representational one. I argue that, complementary to this representational role, some of the scientific models have a constructive role to play in altering and reconstructing the reality in a physical way. I hold that the idealized model assumptions and elements bestow the constructive force of a (...) model on the reality. By recognizing the physical constructive force of some scientific models, the merit of these models could be judged by how successful they are in the reality construction, rather than by the traditional criterion of model-world representation. (shrink)

If a human subject knows they are being measured, this knowledge may affect their attitudes and behaviour to such an extent that it affects the measurement results as well. This broad range of effects is shared under the term ‘reactivity’. Although reactivity is often seen by methodologists as a problem to overcome, in this paper I argue that some quite extreme reactive changes may be legitimate, as long as we are measuring phenomena that are not simple biological regularities. Legitimate reactivity (...) is reactivity which does not undermine the accuracy of a measure; I show that if such reactivity were corrected for, this would unjustifiably ignore the authority of the research subject. Applying this argument to the measurement of depression, I show that under the most commonly accepted models of depression there is room for legitimate reactivity. In the first part of the paper, I provide an inventory of the different types of reactivity that exist in the literature, as well as the different types of phenomena that one could measure. In the second part, I apply my argument to the measurement of depression with the PHQ-9 survey. I argue that depending on what kind of phenomenon we consider depression to be, we will accept different kinds of reactivity. I show that both under the harmful dysfunction model and the practical kinds model, certain reactive changes in measuring depression are best seen as legitimate recharacterizations of the underlying phenomenon, and define what legitimate means in this context. I conclude that in both models, biological aspects constrain characterization, but the models are not so strict that only one concept is acceptable, leaving room for reactivity. (shrink)

Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models concerns what the epistemic goal of toy modelling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this article is to precisely articulate and to defend this (...) claim. In particular, we will distinguish between autonomous and embedded toy models, and then argue that important examples of autonomous toy models are sometimes best interpreted to provide how-possibly understanding, while embedded toy models yield how-actually understanding, if certain conditions are satisfied. _1_ Introduction _2_ Embedded and Autonomous Toy Models _2.1_ Embedded toy models _2.2_ Autonomous toy models _2.3_ Qualification _3_ A Theory of Understanding for Toy Models _3.1_ Preliminaries and requirements _3.2_ The refined simple view _4_ Two Kinds of Understanding with Toy Models _4.1_ Embedded toy models and how-actually understanding _4.2_ Against a how-actually interpretation of all autonomous toy models _4.3_ The how-possibly interpretation of some autonomous toy models _5_ Conclusion. (shrink)

Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models is that it is an unsettled question what the epistemic goal of toy modeling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this paper is to (...) precisely articulate and to defend this claim. In particular, we will distinguish between autonomous and embedded toy models, and, then, argue that important examples of autonomous toy models are sometimes best interpreted to provide how-possibly understanding, while embedded toy models yield how-actually understanding, if certain conditions are satisfied. (shrink)

According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers...

This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and these relations are coordinated by (...) a model commentary. Surrogate models are often about single mechanisms. They are distinguishable from substitute models, which are examined without any concern about their connections with the target. Models as credible worlds are surrogate models that are believed to provide access to their targets on account of their credibility (of which a few senses are distinguished). (shrink)

This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and these relations are coordinated by (...) a model commentary. Surrogate models are often about single mechanisms. They are distinguishable from substitute models, which are examined without any concern about their connections with the target. Models as credible worlds are surrogate models that are believed to provide access to their targets on account of their credibility (of which a few senses are distinguished). (shrink)

The question of whether the idealized models of theoretical economics are explanatory has been the subject of intense philosophical debate. It is sometimes presupposed that either a model provides the actual explanation or it does not provide an explanation at all. Yet, two sets of issues are relevant to the evaluation of model-based explanation: what conditions should a model satisfy in order to count as explanatory and does the model satisfy those conditions. My aim in this paper is to unpack (...) this distinction and show that separating the first set of issues from the second is crucial to an accurate diagnosis of the distinctive challenges that economic models pose. Along the way I sketch a view of model-based explanation in economics that focuses on the role that non-empirical and empirical strategies play in increasing confidence in the adequacy of a given model-based explanation. (shrink)

We develop an account of how mutually inconsistent models of the same target system can provide coherent information about the system. Our account makes use of ideas from the debate surrounding rob...

Representation has been one of the main themes in the recent discussion of models. Several authors have argued for a pragmatic approach to representation that takes users and their interpretations into account. It appears to me, however, that this emphasis on representation places excessive limitations on our view of models and their epistemic value. Models should rather be thought of as epistemic artifacts through which we gain knowledge in diverse ways. Approaching models this way stresses their materiality and media-specificity. Focusing (...) on models as multi-functional artifacts loosens them from any pre-established and fixed representational relationships and leads me to argue for a two-fold approach to representation. (shrink)

Reactivity, or the phenomenon by which subjects tend to modify their behavior in virtue of their being studied upon, is often cited as one of the most important difficulties involved in social scientific experiments, and yet, there is to date a persistent conceptual muddle when dealing with the many dimensions of reactivity. This paper offers a conceptual framework for reactivity that draws on an interventionist approach to causality. The framework allows us to offer an unambiguous definition of reactivity and distinguishes (...) it from placebo effects. Further, it allows us to distinguish between benign and malignant forms of the phenomenon, depending on whether reactivity constitutes a danger to the validity of the causal inferences drawn from experimental data. (shrink)

A series of recent debates in experimental economics have associated demand effects with the artificiality of the experimental setting and have linked it to the problem of external validity. In this paper, we argue that these associations can be misleading, partly because of the ambiguity with which “artificiality” has been defined, but also because demand effects and external validity are related in complex ways. We argue that artificiality may be directly as well as inversely correlated with demand effects. We also (...) distinguish between the demand effects of experimentation and the reactions that they may trigger and that might endanger experimental validity. We conclude that economists should pay more attention to the way in which subjects construe the experimental task and learn to exploit subjects’ reactivity to expectations in their experiments. (shrink)

Traditionally, a scientific model is thought to provide a good scientific explanation to the extent that it satisfies certain scientific goals that are thought to be constitutive of explanation. Problems arise when we realize that individual scientific models cannot simultaneously satisfy all the scientific goals typically associated with explanation. A given model’s ability to satisfy some goals must always come at the expense of satisfying others. This has resulted in philosophical disputes regarding which of these goals are in fact necessary (...) for explanation, and as such which types of models can and cannot provide explanations. Explanatory monists argue that one goal will be explanatory in all contexts, while explanatory pluralists argue that the goal will vary based on pragmatic considerations. In this paper, I argue that such debates are misguided, and that both monists and pluralists are incorrect. Instead of any goal being given explanatory priority over others in a given context, the different goals are all deeply dependent on one another for their explanatory power. Any model that sacrifices some explanatory goals to attain others will always necessarily undermine its own explanatory power in the process. And so when forced to choose between individual scientific models, there can be no explanatory victors. Given that no model can satisfy all the goals typically associated with explanation, no one model in isolation can provide a good scientific explanation. Instead we must appeal to collections of models. Collections of models provide an explanation when they satisfy the web of interconnected goals that justify the explanatory power of one another. (shrink)

Many models in economics are very unrealistic. At the same time, economists put a lot of effort into making their models more realistic. I argue that in many cases, including the Modigliani-Miller irrelevance theorem investigated in this paper, the purpose of this process of concretization is explanatory. When evaluated in combination with their assumptions, a highly unrealistic model may well be true. The purpose of relaxing an unrealistic assumption, then, need not be to move from a false model to a (...) true one. Instead, it may be providing an explanation of some phenomenon by invoking the factor that figures in the assumption. This idea is developed in terms of the contrastive account of explanation. It is argued that economists use highly unrealistic assumptions to determine a contrast that is worth explaining. The process of concretization also motivates new explanatory questions. A high degree of explanatory power, then, may well be due to a high number of unrealistic assumptions. Thus, highly unrealistic models can be powerful explanatory engines. Key Words: concretization • explanation • explanatory power • idealization • model • Modigliani-Miller theorem • unrealistic assumption. (shrink)

This paper discusses why mathematical economists of the early Cold War period favored formal-axiomatic over behavioral choice theories. One reason was that formal-axiomatic theories allowed mathematical economists to improve the conceptual and theoretical foundations of economics and thereby to increase its scientific status. Furthermore, the separation between mathematical economics and other behavioral sciences was not as clear-cut as often argued. While economists did not modify their behavioral assumptions, some acknowledged the empirical shortcomings of their models. The paper reveals the multifaceted (...) nature of rational choice theories reflected in the changing interpretations and roles of the theories in those early years. (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)

Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms "law" and "theory," I distinguish principles, specific conditions, models, (...) hypotheses, and generalizations. I argue that scientists use designated similarities between models and aspects of the world to form both hypotheses and generalizations. (shrink)