Hunting Causes and Using Them argues that causation is not one thing, as commonly assumed, but many. There is a huge variety of causal relations, each with different characterizing features, different methods for discovery and different uses to which it can be put. In this collection of new and previously published essays, Nancy Cartwright provides a critical survey of philosophical and economic literature on causality, with a special focus on the currently fashionable Bayes-nets and invariance methods - and it exposes (...) a huge gap in that literature. Almost every account treats either exclusively how to hunt causes or how to use them. But where is the bridge between? It's no good knowing how to warrant a causal claim if we don't know what we can do with that claim once we have it. This book will interest philosophers, economists and social scientists. (shrink)

This paper argues that even when simple analogue models picture parallel worlds, they generally still serve as isolating tools. But there are serious obstacles that often stop them isolating in just the right way. These are obstacles that face any model that functions as a thought-experiment but they are especially pressing for economic models because of the paucity of economic principles. Because of the paucity of basic principles, economic models are rich in structural assumptions. Without these no interesting conclusions can (...) be drawn. This, however, makes trouble when it comes to exporting conclusions from the model to the world. One uncontroversial constraint on induction from special cases is to beware of extending conclusions to situations that we know are different in relevant respects. In the case of economic models it is clear by inspection that the unrealistic structural assumptions of the model are intensely relevant to the conclusion. Any inductive leap to a real situation seems a bad bet. (shrink)

Robert Sugden argues that robustness analysis cannot play an epistemic role in grounding model-world relationships because the procedure is only a matter of comparing models with each other. We posit that this argument is based on a view of models as being surrogate systems in too literal a sense. In contrast, the epistemic importance of robustness analysis is easy to explicate if modelling is viewed as extended cognition, as inference from assumptions to conclusions. Robustness analysis is about assessing the reliability (...) of our extended inferences, and when our confidence in these inferences changes, so does our confidence in the results. Furthermore, we argue that Sugden’s inductive account relies tacitly on robustness considerations. (shrink)

This paper asks how, in science in general and in economics in particular, theoretical models aid the understanding of real-world phenomena. Using specific models in economics and biology as test cases, it considers three alternative answers: that models are tools for isolating the ‘capacities’ of causal factors in the real world; that modelling is ‘conceptual exploration’ which ultimately contributes to the development of genuinely explanatory theories; and that models are credible counterfactual worlds from which inductive inferences can be made. The (...) paper argues that the ‘credible worlds’ account captures significant aspects of scientific practice, even if many modellers see their work as conceptual exploration. (shrink)

It is argued that one can learn from minimal economic models. Minimal models are models that are not similar to the real world, do not resemble some of its features, and do not adhere to accepted regularities. One learns from a model if constructing and analysing the model affects one’s confidence in hypotheses about the world. Economic models, I argue, are often assessed for their credibility. If a model is judged credible, it is considered to be a relevant possibility. Considering (...) such relevant possibilities may affect one’s confidence in necessity or impossibility hypotheses. Thus, one can learn from minimal economic models. (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)

This article reviews the recent literature on idealization, specifically idealization in the course of scientific modeling. We argue that idealization is not a unified concept and that there are three different types of idealization: Galilean, minimalist, and multiple models, each with its own justification. We explore the extent to which idealization is a permanent feature of scientific representation and discuss its implications for debates about scientific realism.

Although general equilibrium theory originated in the late nineteenth century, modern elaboration and development of the theory began only in the 1930s and 1940s. This book focuses on the version of the theory developed in the second half of the twentieth century, referred to by Lionel McKenzie as the classical general equilibrium theory. McKenzie offers detailed and rigorous treatment of the classical model, giving step-by-step proofs of the basic theorems. In many cases he elaborates on the individual steps to give (...) a fuller understanding of the underlying principles. His goal is to provide readers with a true mastery of the methodology so that they can derive new results that will further enrich their thinking about general equilibrium theory. Special attention is given to the McKenzie model, in which it is not assumed that the number of firms is given but rather that technologies or activities are available to any agents who can supply the resources they require. The McKenzie model is used to establish the turnpike theorems of optimal and competitive capital accumulation. (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)

one takes to be the most salient, any pair could be judged more similar to each other than to the third. Goodman uses this second problem to showthat there can be no context-free similarity metric, either in the trivial case or in a scientifically ...

Philosophers of science increasingly recognize the importance of idealization: the intentional introduction of distortion into scientific theories. Yet this recognition has not yielded consensus about the nature of idealization. e literature of the past thirty years contains disparate characterizations and justifications, but little evidence of convergence towards a common position.

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)

The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the (...) relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation. (shrink)

During the last two centuries, the way economic science is done has changed radically: it has become a social science based on mathematical models in place of words. This book describes and analyses that change - both historically and philosophically - using a series of case studies to illuminate the nature and the implications of these changes. It is not a technical book; it is written for the intelligent person who wants to understand how economics works from the inside out. (...) This book will be of interest to economists and science studies scholars. But it also aims at a wider readership in the public intellectual sphere, building on the current interest in all things economic and on the recent failure of the so-called economic model, which has shaped our beliefs and the world we live in. (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)

Economics is a culturally and politically powerful and contested discipline, and it has been that way as long as it has existed. For some commentators, economics is the "queen of the social sciences", while others view it as a "dismal science" (and both of these epithets allow for diverse interpretations; see Mäki 2002). Economics is also a discipline that deals with a dynamically complex subject matter and has a tradition of reducing this complexity by using systematic procedures of simplification. Nowadays, (...) these procedures involve for the most part building and using mathematical models (for an overview of the philosophical issues, see Morgan and Knuuttila 2011). In the dominant circles of the discipline, one is not regarded as a serious economist having a professional expert view on any given economic or social issue without having a model about it. Much of the power of the discipline and its characteristic contestations therefore involve models and modelling: the successes and failures of the dismal queen are those of modeling. The issues involved in conomic modeling have been made particularly acute once again by the financial crisis of 2008-2009 and its aftermath: the discipline of economics is among the candidates for the major blame for failure. I will first outline some thoughts about the characteristics disciplinary conventions that guide and constrain modeling in economics. I will then summarize my account of the very ideas of models and modeling. Finally, within the framework of that account, I will highlight some major issues of contestation and sketch the respective notions of potential success and failure in economic modeling with illustrations. These notions are motivated by my subscription to a (flexible and discipline-sensitive) realist philosophy of science (e.g. Mäki 2005). (shrink)

The most common argument against the use of rational choice models outside economics is that they make unrealistic assumptions about individual behavior. We argue that whether the falsity of assumptions matters in a given model depends on which factors are explanatorily relevant. Since the explanatory factors may vary from application to application, effective criticism of economic model building should be based on model-specific arguments showing how the result really depends on the false assumptions. However, some modeling results in imperialistic applications (...) are relatively robust with respect to unrealistic assumptions. Key Words: unrealistic assumptions • economics imperialism • rational choice • as if • robustness. (shrink)

My philosophical intuitions are those of a scientific realist. In addition to being realist in its philosophical outlook, my philosophy of economics also aspires to be realistic in the sense of being descriptively adequate, or at least normatively non-utopian, about economics as a scientific discipline. The special challenge my philosophy of economics must meet is to provide a scientific realist account that is realistic of a discipline that deals with a complex subject matter and operates with highly unrealistic models. Unrealisticness (...) in economic models must not constitute an obstacle to realism about those models. What follows is a selective and somewhat abstract summary of my thinking about economics, outlined from two perspectives: first historical and autobiographical, then systematic and comparative. The first angle helps understand motives and trajectories of ideas against their backgrounds in intellectual history. My story turns out to have both unique and generalizable aspects. The second approach outlines some of the key concepts and arguments as well as their interrelations in my philosophy of economics, with occasional comparisons to other views. More space will be devoted to this second perspective than to the first. (shrink)

Odenbaugh and Alexandrova provide a challenging critique of the epistemic benefits of robustness analysis, singling out for particular criticism the account we articulated in Kuorikoski et al.. Odenbaugh and Alexandrova offer two arguments against the confirmatory value of robustness analysis: robust theorems cannot specify causal mechanisms and models are rarely independent in the way required by robustness analysis. We address Odenbaugh and Alexandrova’s criticisms in order to clarify some of our original arguments and to shed further light on the properties (...) of robustness analysis and its epistemic rationale. (shrink)

Provability, Computability and Reflection.

We claim that the process of theoretical model refinement in economics is best characterised as robustness analysis: the systematic examination of the robustness of modelling results with respect to particular modelling assumptions. We argue that this practise has epistemic value by extending William Wimsatt's account of robustness analysis as triangulation via independent means of determination. For economists robustness analysis is a crucial methodological strategy because their models are often based on idealisations and abstractions, and it is usually difficult to tell (...) which idealisations are truly harmful. (shrink)

In this article we defend the inferential view of scientific models and idealisation. Models are seen as “inferential prostheses” (instruments for surrogative reasoning) construed by means of an idealisation-concretisation process, which we essentially understand as a kind of counterfactual deformation procedure (also analysed in inferential terms). The value of scientific representation is understood in terms not only of the success of the inferential outcomes arrived at with its help, but also of the heuristic power of representation and their capacity to (...) correct and improve our models. This provides us with an argument against Sugden’s account of credible models: the likelihood or realisticness (their “credibility”) is not always a good measure of their acceptability. As opposed to “credibility” we propose the notion of “enlightening”, which is the capacity of giving us understanding in the sense of an inferential ability. (shrink)

This book provides a systematic exposition of mathematical economics, presenting and surveying existing theories and showing ways in which they can be extended. One of its strongest features is that it emphasises the unifying structure of economic theory in such a way as to provide the reader with the technical tools and methodological approaches necessary for undertaking original research. The author offers explanations and discussion at an accessible and intuitive level providing illustrative examples. He begins the work at an elementary (...) level and progessively takes the reader to the frontier of current research. This second edition brings the reader fully up to date with recent research in the field. (shrink)

Economics is dominated by model building, therefore a comprehension of how such models work is vital to understanding the discipline. This book provides a critical analysis of the economist's favourite tool, and as such will be an enlightening read for some, and an intriguing one for others.

Challenges the widely held view that good models must necessarily be simplifications and hence cannot be true. This is done by distinguishing between whole truth (complete description) and truth (essential description, attained by the method of isolation).

Introducing the ontology of theory choice -/- Economists choose theories and they choose ways of pursuing theories, and they leave others unchosen. Why do economists choose the way they do? How should economists choose? What are the objectives and what are the constraints? What should they be? The questions are both descriptive and prescriptive. -/- There are two broad classes of “criteria of choice” that have been somewhat systematically considered in the recent literature on economic methodology: (1) Empirical criteria. There (...) are several possible ways of incorporating empirical criteria in one's theory of science. The respective methodology of theory assessment may be static or dynamic, it may be deductivist or inductivist, it may include various ideas of what constitutes empirical evidence, and so on. What they all share is the general idea that scientific theories are, or are to be, checked against empirical evidence according to some rules, and that this determines the choice of theory. (2) Social criteria. Again, there are several options. The social criteria may be related to the social interests of scientists or larger social collectives, they may be based on the persuasiveness and tradition-boundedness of theories, they may involve social or moral norms, they may be derived from various costs and benefits of holding a theory in a given research community, and so on. If they involve empirical data, it is the social aspects of the data that matter. What all these views share is that scientific theories are taken to have social attributes (functions, consequences) that play or should play a major role in theory choice. (shrink)

Theoretical biology and economics are remarkably similar in their reliance on mathematical models, which attempt to represent real world systems using many idealized assumptions. They are also similar in placing a great emphasis on derivational robustness of modeling results. Recently philosophers of biology and economics have argued that robustness analysis can be a method for confirmation of claims about causal mechanisms, despite the significant reliance of these models on patently false assumptions. We argue that the power of robustness analysis has (...) been greatly exaggerated. It is best regarded as a method of discovery rather than confirmation. (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)

This paper examines two recent approaches to the nature and functioning of economic models: models as isolating representations and models as credible constructions. The isolationist view conceives of economic models as surrogate systems that isolate some of the causal mechanisms or tendencies of their respective target systems, while the constructionist approach treats them rather like pure constructions or fictional entities that nevertheless license different kinds of inferences. I will argue that whereas the isolationist view is still tied to the representationalist (...) understanding of models that takes the model-target dyad as the basic unit of analysis, the constructionist perspective can better accommodate the way we actually acquire knowledge through them. Using the example of Tobin’s ultra-Keynesian model I will show how many of the epistemic characteristics of modelling tend to go unrecognised if too much focus is placed on the model-target dyad. (shrink)

Modelling cannot be characterized as isolating, nor models as isolations. This article presents three arguments to that effect, against Uskali Mäki's account of models. First, while isolation proceeds through a process of manipulation and control, modelling typically does not proceed through such a process. Rather, modellers postulate assumptions, without seeking to justify them by reference to a process of isolation. Second, while isolation identifies an isolation base—a concrete environment it seeks to control and manipulate—modelling typically does not identify such a (...) base. Rather, modellers construct their models without reference to concrete environments, and only later seek to connect their models to concrete situations of the real world. Third, Mäki argues that isolation employs idealization to control for disturbing factors, but does not affect the factors or mechanisms that are supposed to be isolated. However, models typically make idealizing assumptions about the factors and mechanisms that are the focus of investigation. Thus, even the product of modelling often cannot be characterized as isolation. (shrink)

Introduction. Bert HAMMINGA and Neil DE MARCHI: Préface. Bert HAMMINGA and Neil DE MARCHI: Idealization and the Defence of Economics: Notes Toward a History. Part I: General Observations on Idealization in Economics. Kevin D. HOOVER: Six Queries about Idealization in an Empirical Context. Bernard WALLISER: Three Generalization Processes for Economic Models. Steven COOK and David HENDRY: The Theory of Reduction in Econometrics. Maarten C.W. JANSSEN: Economic Models and Their Applications. Adolfo GARCÍA DE LA SIENRA: Idealization and Empirical Adequacy in Economic (...) Theory. Izabella NOWAKOWA and Leszek NOWAK: On Correspondence between Economic Theories. Uskali MÄKI: Isolation, Idealization and Truth in Economics. Part II: Case Studies of Idealization in Economics. Nancy CARTWRIGHT: Mill and Menger: Ideal Elements and Stable Tendencies. Wolfgang BALZER: Exchange Versus Influence: A Case of Idealization. Kees COOLS, Bert HAMMINGA, Theo A.F. KUIPERS: Truth Approximation by Concretization in Capital Structure Theory. Daniel M. HAUSMAN: Paul Samuelson as Dr. Frankenstein: When an Idealization Runs Amuck. Hugo A. KEUZENKAMP: What if an Idealization is Problematic? The Case of the Homogeneity Condition in Consumer Demand. Werner DIEDERICH: Nowak on Explanation and Idealization in Marx's 'Capital'. Gérard JORLAND: Idealization and Transformation. Jack BIRNER: Idealizations and Theory Development in Economics. Some History and Logic of the Logic Discovery. Discussions. Leszek NOWAK: The Idealizational Methodology and Economics. Replies to Diederich, Hoover, Janssen, Jorland and Mäki. (shrink)

It is argued that one can learn from minimal economic models. Minimal models are models that are not similar to the real world, do not resemble some of its features, and do not adhere to accepted regularities. One learns from a model if constructing and analysing the model affects one’s confidence in hypotheses about the world. Economic models, I argue, are often assessed for their credibility. If a model is judged credible, it is considered to be a relevant possibility. Considering (...) such relevant possibilities may affect one’s confidence in necessity or impossibility hypotheses. Thus, one can learn from minimal economic models. (shrink)

It is argued that one can learn from minimal economic models. Minimal models are models that are not similar to the real world, do not resemble some of its features, and do not adhere to accepted regularities. One learns from a model if constructing and analysing the model affects one’s confidence in hypotheses about the world. Economic models, I argue, are often assessed for their credibility. If a model is judged credible, it is considered to be a relevant possibility. Considering (...) such relevant possibilities may affect one’s confidence in necessity or impossibility hypotheses. Thus, one can learn from minimal economic models. (shrink)