Written before the impressive collapse of the socialist system in Eastern Europe, this book offers a quite objective and serious systematic analysis of the Marxian labor theory of value, Marx's main scientific legacy. After reconstructing the 'prototype' of this theory - which is the theory as it was left by Marx himself in Capital - the author proceeds to a careful and detailed analysis of its foundational problems, taking into account Bohm-Bawerk's important criticisms. After introducing advanced contemporary formal tools, the (...) author proceeds to a thorough discussion of the dialectical method, just in order to tackle the foundational problems of the theory. He provides a formally precise and well motivated definition of abstract labor, and then proceeds to prove the existence of a measurement of abstract labor - i.e. the existence of numerical labor-values. Using this result, the author provides rigorous axiomatic foundations for the theory of value and then proves the existence of a Marxian competitive equilibrium, which is tantamount to the proof of the possibility of reproduction for a capitalist economy. The author finishes the book by showing in detail how the problems of the prototype are solved, by reconstructing the Leontief model of the labor theory of value on the new logical bases. Written in a very clear style, in the language of contemporary philosophy of science, the book is of interest to philosophers of science and economists, applied logicians and all those interested in the scientific legacy of Karl Marx. (shrink)

The books’ goal is to answer the question: Do the weaknesses of value-free economics imply the need for a paradigm shift? The author synthesizes criticisms from different perspectives (descriptive and methodological). Special attention is paid to choices over time, because in this area value-free economics has the most problems. In that context, the enriched concept of multiple self is proposed and investigated. However, it is not enough to present the criticisms towards value-free economics. For scientists, a bad paradigm is better (...) than no paradigm. Therefore, the author considers whether value-based economics with normative approaches such as economics of happiness, capability approach, libertarian paternalism, and the concept of multiple self can be the alternative paradigm for value-free economics. This book is essential reading to everyone interested in the current state of economics as a discipline. (shrink)

This open access book provides an exploration of the consequences of the ontological differences between natural and social objects (sometimes described as objects of nature and objects of thought) in the workings of causal and agency relationships. One of its important and possibly original conclusions is that causal and agency relationships do not encompass all of the dependent relationships encountered in social life. The idea that social reality is contingent has been known (and largely undisputed) at least since Wittgenstein’s “On (...) Certainty”, but social science, and most notably economics has continued to operate on the basis of causal and agency theories borrowed or adapted from the natural sciences. This volume contains essays that retain and justify the partial or qualified use of this approach and essays that totally reject any use of causal and agency theory built on determined facts (closed systems).The rejection is based on the possibly original claim that, whereas causation in the objects of the natural sciences reside in their properties, human action is a matter of intentionality. It engages with critical realist theory and re-examines the role of free will in theories of human action in general and economic theory in particular. (shrink)

Abstraction and idealization are the two notions that are most often discussed in the context of assumptions employed in the process of model building. These notions are also routinely used in philosophical debates such as that on the mechanistic account of explanation. Indeed, an objection to the mechanistic account has recently been formulated precisely on these grounds: mechanists cannot account for the common practice of idealizing difference-making factors in models in molecular biology. In this paper I revisit the debate and (...) I argue that the objection does not stand up to scrutiny. This is because it is riddled with a number of conceptual inconsistencies. By attempting to resolve the tensions, I also draw several general lessons regarding the difficulties of applying abstraction and idealization in scientific practice. Finally, I argue that more care is needed only when speaking of abstraction and idealization in a context in which these concepts play an important role in an argument, such as that on mechanistic explanation. (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 essay offers some reflections on Samir Okasha’s new monograph Agents and Goals in Evolution, his style of doing philosophy, and the broader philosophy of nature project of trying to make sense of agency and rationality as natural phenomena.

This paper adds to the philosophical literature on mechanistic explanation by elaborating two related explanatory functions of idealisation in mechanistic models. The first function involves explaining the presence of structural/organizational features of mechanisms by reference to their role as difference-makers for performance requirements. The second involves tracking counterfactual dependency relations between features of mechanisms and features of mechanistic explanandum phenomena. To make these functions salient, we relate our discussion to an exemplar from systems biological research on the mechanism for countering (...) heat shock—the heat shock response system—in Escherichia coli bacteria. This research also reinforces a more general lesson: ontic constraint accounts in the literature on mechanistic explanation provide insufficiently informative normative appraisals of mechanistic models. We close by outlining an alternative view on the explanatory norms governing mechanistic representation. (shrink)

En economía, la investigación se divide en dos grandes metodologías: los modelos teórico-matemáticos y los estudios empíricos. Estudiando modelos teóricos y métodos empíricos ) se da cuenta de las limitaciones de ambos métodos. Se concluye que ninguno de estos puede generar explicaciones de cómo en realidad suceden las cosas, sino que solo de cómo posiblemente suceden. La razón es que ambos necesitan un enlace interpretativo que permita extrapolar desde su propio sistema hacia un sistema objetivo. Los modelos tienen dominio general (...) y pueden dar cuenta de mecanismos. Los RCTs, al contrario, son válidos internamente y están conectados al mundo real, pero su dominio es muy específico. Aunque ninguno logre responder preguntas amplias de un fenómeno de interés, pueden complementarse para generar extrapolaciones más confiables sobre un sistema objetivo. Sin embargo, esto solo podrá hacerse si se conocen bien los mecanismos y el contexto en que ocurre una evidencia. Palabras clave: Causalidad, Mecanismos, Capacidades, Epistemología de la Economía, Generalización de Evidencia. (shrink)

Is there a unique way to de-idealize models? If not, how might the possible ways of reducing the distortion between models and reality differ from each other? Based on an empirical case study conducted in financial markets, this paper discusses how a popular valuation model (the Discounted Cash Flow model) idealizes reality and how the market participants de-idealize it in concrete market situations. In contrast to Cartwright's view that economic models are generally over-constrained, this paper suggests that valuation models are (...) under-constrained. This serves as the reason why the relaxation of simplifying assumptions and concretization do not work as methods of de-idealization. The paper finds that financial market participants de-idealize models using commentary that takes the form of judgment. As a conclusion, a hypothesis is formulated that proposes that the more underdetermined the model is the bigger role narrative and other pragmatic elements play in the process of model application. (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)

Every model leaves out or distorts some factors that are causally connected to its target phenomenon -- the phenomenon that it seeks to predict or explain. If we want to make predictions, and we want to base decisions on those predictions, what is it safe to omit or to simplify, and what ought a causal model to describe fully and correctly? A schematic answer: the factors that matter are those that make a difference to the target phenomenon. There are several (...) ways to understand differencemaking. This paper advances a view as to which is the most relevant to the forecaster and the decision-maker. It turns out that the right notion of differencemaking for thinking about idealization in prediction is also the right notion for thinking about idealization in explanation; this suggests a carefully circumscribed version of Hempel's famous thesis that there is a symmetry between explanation and prediction. (shrink)

Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is (...) related to the explanation of interest. Our analysis reveals that the epistemological relationships are not always dependent on the metaphysical relationships, contrary to what has been assumed by many philosophers of science. Moreover, we identify several importantly different ways that scientific models instantiate these relationships. We argue that our framework provides novel insights concerning the nature of models, explanation, idealization, and understanding. (shrink)

In this paper, I first argue against various attempts to justify idealizations in scientific models that explain by showing that they are harmless and isolable distortions of irrelevant features. In response, I propose a view in which idealized models are characterized as providing holistically distorted representations of their target system. I then suggest an alternative way that idealized modeling can be justified by appealing to universality.

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)

This paper aims (a) to provide characterizations of realism and instrumentalism that are philosophically interesting and applicable to economics; and (b) to defend instrumentalism against realism as a methodological stance in economics. Starting point is the observation that ‘all models are false’, which, or so I argue, is difficult to square with the realist's aim of truth, even if the latter is understood as ‘partial’ or ‘approximate’. The three cheers in favour of instrumentalism are: (1) Once we have usefulness, truth (...) is redundant. (2) There is something disturbing about causal structure. (3) It's better to do what one can than to chase rainbows. (shrink)

The aim of this paper is to develop ideas about robustness analyses. I introduce a form of robustness analysis that I call sufficient parameter robustness, which has been neglected in the literature. I claim that sufficient parameter robustness is different from derivational robustness, the focus of previous research. My purpose is not only to suggest a new taxonomy of robustness, but also to argue that previous authors have concentrated on a narrow sense of robustness analysis, which they have inadequately distinguished (...) from other investigations of models such as sensitivity analysis. (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)

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)

Much philosophical attention has been devoted to whether economic models explain, and more generally to how scientific models represent. Yet there is an issue more practically important to economics than either of these, which I label the efficiency question: regardless of how exactly models represent, or of whether their role is explanatory or something else, is current modeling practice an efficient way to achieve these goals – or should research efforts be redirected? In addition to showing how the efficiency question (...) has been relatively neglected, I give two examples of the kind of analysis it requires. (shrink)

This paper defends the thesis of learning from non-causal models: viz. that the study of some model can prompt justified changes in one’s confidence in empirical hypotheses about a real-world target in the absence of any known or predicted similarity between model and target with regards to their causal features. Recognizing that we can learn from non-causal models matters not only to our understanding of past scientific achievements, but also to contemporary debates in the philosophy of science. At one end (...) of the philosophical spectrum, my thesis undermines the views of those who, like Cartwright (Erkenntnis 70:45–58, 2009), follow Hesse (Models and Analogies in Science, Notre Dame, University of Notre Dame Press, 1963) in restricting the possibility of learning from models to only those situations where a model identifies some causal factors present in the target. At the other end of the spectrum, my thesis also helps undermine some extremely permissive positions, e.g., Grüne-Yanoff’s (Erkenntnis 70(1):81–99, 2009, Philos Sci 80(5): 850–861, 2013) claim that learning from a model is possible even in the absence of any similarity at all between model and target. The thesis that we can learn from non-causal models offers a cautious middle ground between these two extremes. (shrink)

In the latter half of the 19th century, economic thought in the Germanspeaking world was dominated, both intellectually and academically, by the so-called historical school, from Wilhelm Roscher to Gustav Schmoller and others. In 1871, the Austrian Carl Menger published his Grun&tze der Volkswirtschaftslehre (Menger, 1976 (1871)), customarily referred to as one of the three simultaneous discoveries of marginalist economics-the other two marginalist ‘revolutionaries’ being Jevons in England and Walras in France. Twelve years later, in 1883, Menger published a major (...) methodological treatise entitled Untersuchungen iiber die Methode der Socialwissenschaften und der Politischen Oekonomie insbesondere (Menger, 1963 (1883)). This book included criticisms of some of the historicist principles of doing economics. In the same year, Schmoller, leader of the German historicists, wrote a critical review of Menger’s book (Schmoller, 1883). Menger reacted forcefully with a more straightforwardly polemical small book, Die Irrthiimer des Historismus in der deutschen National6konomie (Menger, 1884). Commentaries by others appeared in later years, but this brief episode amounted to what has thereafter been called the Methodenstreit between Menger and Schmoller. It has been established as perhaps the most famous methodological controversy in the history of the social sciences. (shrink)

If models can be true, where is their truth located? Giere (Explaining science, University of Chicago Press, Chicago, 1998) has suggested an account of theoretical models on which models themselves are not truth-valued. The paper suggests modifying Giere’s account without going all the way to purely pragmatic conceptions of truth—while giving pragmatics a prominent role in modeling and truth-acquisition. The strategy of the paper is to ask: if I want to relocate truth inside models, how do I get it, what (...) else do I need to accept and reject? In particular, what ideas about model and truth do I need? The case used as an illustration is the world’s first economic model, that of von Thünen (1826/1842) on agricultural land use in the highly idealized Isolated State. (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 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)

Idealization is ubiquitous in human cognition, and so is the inclination to be puzzled by it: what to make of ideal gas, infinitely large populations, homo economicus, perfectly just society, known to violate matters of fact? This is apparent in social science theorizing, recent philosophy of science analyzing scientific modeling, and the debate over ideal and non-ideal theory in political philosophy. I will offer a set of concepts and principles to improve transparency about the precise contents of idealizations and their (...) distinct functions. (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)

The paper seeks to offer [1] an explication of a concept of economics imperialism, focusing on its epistemic aspects; and [2] criteria for its normative assessment. In regard to [1], the defining notion is that of explanatory unification across disciplinary boundaries. As to [2], three kinds of constraints are proposed. An ontological constraint requires an increased degree of ontological unification in contrast to mere derivational unification. An axiological constraint derives from variation in the perceived relative significance of the facts explained. (...) An epistemological constraint requires strong fallibilism acknowledging a particularly severe epistemic uncertainty and proscribing against over-confident arrogance. (shrink)

Science depends on judgments of the bearing of evidence on theory. Scientists must judge whether an observation or the result of an experiment supports, disconfirms, or is simply irrelevant to a given hypothesis. Similarly, scientists may judge that, given all the available evidence, a hypothesis ought to be accepted as correct or nearly so, rejected as false, or neither. Occasionally, these evidential judgments can be made on deductive grounds. If an experimental result strictly contradicts a hypothesis, then the truth of (...) the data deductively entails the falsity of the hypothesis. In the great majority of cases, however, the connection between evidence and hypothesis is non-demonstrative, or inductive. In particular, this is so whenever a general hypothesis is inferred to be correct on the basis of the available data, since the truth of the data will not deductively entail the truth of the hypothesis. It always remains possible that the hypothesis is false even though the data are correct. (shrink)

This paper provides a conceptual framework that allows for distinguishing between different kinds of generalisation and applicability. It is argued that generalising models may bring epistemic benefits. They do so if they show that restrictive and unrealistic assumptions do not threaten the credibility of results derived from models. There are two different notions of applicability, generic and specific, which give rise to three different kinds of generalizations. Only generalising a result brings epistemic benefits concerning the truth of model components or (...) results. Abstracting the model and applying the model into new systems are not intrinsically epistemically beneficial in this way. The Dixit-Stiglitz model of monopolistic competition is used as an illustration. (shrink)

This paper contributes to the philosophical accounts of generalisation in formal modelling by introducing a conceptual framework that allows for recognising generalisations that are epistemically beneficial in the sense of contributing to the truth of a model result or component. The framework is useful for modellers themselves because it is shown how to recognise different kinds of generalisation on the basis of changes in model descriptions. Since epistemically beneficial generalisations usually de-idealise the model, the paper proposes a reformulation of the (...) well-known distinction between abstraction and idealisation. A reformulated notion of abstraction is needed because the extant accounts yield wrong judgments when model-modifications introduce implicit assumptions. (shrink)

Like other mathematically intensive sciences, economics is becoming increasingly computerized. Despite the extent of the computation, however, there is very little true simulation. Simple computation is a form of theory articulation, whereas true simulation is analogous to an experimental procedure. Successful computation is faithful to an underlying mathematical model, whereas successful simulation directly mimics a process or a system. The computer is seen as a legitimate tool in economics only when traditional analytical solutions cannot be derived, i.e., only as a (...) purely computational aid. We argue that true simulation is seldom practiced because it does not fit the conception of understanding inherent in mainstream economics. According to this conception, understanding is constituted by analytical derivation from a set of fundamental economic axioms. We articulate this conception using the concept of economists' perfect model. Since the deductive links between the assumptions and the consequences are not transparent in ‘bottom‐up’ generative microsimulations, microsimulations cannot correspond to the perfect model and economists do not therefore consider them viable candidates for generating theories that enhance economic understanding. (shrink)

Although there has been much recent discussion on mechanisms in philosophy of science and social theory, no shared understanding of the crucial concept itself has emerged. In this paper, a distinction between two core concepts of mechanism is made on the basis that the concepts correspond to two different research strategies: the concept of mechanism as a componential causal system is associated with the heuristic of functional decomposition and spatial localization and the concept of mechanism as an abstract form of (...) interaction is associated with the strategy of abstraction and simple models. The causal facts assumed and the theoretical consequences entailed by an explanation with a given mechanism differ according to which concept of mechanism is in use. Research strategies associated with mechanism concepts also involve characteristic biases that should be taken into account when using them, especially in new areas of application. (shrink)

Descriptive abstraction means omission of information from descriptions of phenomena. In this paper, I introduce a distinction between vertical and horizontal descriptive abstraction. Vertical abstracts away levels of mechanism or organization, while horizontal abstracts away details within one level of organization. The distinction is implicit in parts of the literature, but it has received insufficient attention and gone mainly unnoticed. I suggest that the distinction can be used to clarify how computational descriptions are formed in some variants of the mechanistic (...) account of physical computation. Furthermore, I suggest that, if this suggestion is adopted, it can be used to resolve what I call abstraction, hierarchy, and generality problems raised against mechanistic account of physical computation. According to the abstraction problem, the mechanistic account of physical computation is conceptually confused in claiming that physical systems process computational, abstract properties. An existing solution distinguishes between descriptive and metaphysical abstraction, suggesting that the abstraction problem unnecessarily postulates metaphysically abstract entities. The solution has been criticized for leading to what I call hierarchy and generality problems: it results in two separate hierarchies, one physical and one computational, making it problematic both to account for the generality of computational descriptions and to specify how the two hierarchies are related to each other. Adopting the vertical-horizontal distinction and the view that computational descriptions are achieved by horizontal abstraction allows one to account for the generality of computational descriptions, and to form a single hierarchy in which there are no separate hierarchies in need of integration. (shrink)

The so-called integration problem concerning mechanistic and computational explanation asks how they are related to each other. One approach is that a computational explanation is a species of mechanistic explanation. According to this view, computational or mathematical descriptions are mechanism sketches or macroscopic descriptions that include computationally relevant and exclude computationally irrelevant physical properties. Some suggest that this results in a so-called single hierarchy view of physical computation, where computational or mathematical properties sit together in the same mechanistic hierarchy with (...) the implementational properties. This view can be contrasted with a separate hierarchy view, according to which computational and physical descriptions have their own hierarchies which are related to each other via a bridging implementation relation. The single hierarchy view has been criticized for downplaying the explanatory value of computational explanations and not being hospitable to multiple realization of cognitive processes. In this paper, I argue that the aforementioned criticisms fail, and there might be a deeper problem with the single hierarchy view, which is that the single hierarchy view might collapse into a separate hierarchy view. The kind of abstraction used by the single hierarchy view does not seem to grant mathematical or computational descriptions but only more stripped physical or implementational descriptions. (shrink)

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)

One striking feature of the contemporary modelling practice is its interdisciplinary nature. The same equation forms, and mathematical and computational methods, are used across different disciplines, as well as within the same discipline. Are there, then, differences between intra- and interdisciplinary transfer, and can the comparison between the two provide more insight on the challenges of interdisciplinary theoretical work? We will study the development and various uses of the Ising model within physics, contrasting them to its applications to socio-economic systems. (...) While the renormalization group methods justify the transfer of the Ising model within physics – by ascribing them to the same universality class – its application to socio-economic phenomena has no such theoretical grounding. As a result, the insights gained by modelling socio-economic phenomena by the Ising model may remain limited. (shrink)

The epistemic value of models has traditionally been approached from a representational perspective. This paper argues that the artifactual approach evades the problem of accounting for representation and better accommodates the modal dimension of modeling. From an artifactual perspective, models are viewed as erotetic vehicles constrained by their construction and available representational tools. The modal dimension of modeling is approached through two case studies. The first portrays mathematical modeling in economics, while the other discusses the modeling practice of synthetic biology, (...) which exploits and combines models in various modes and media. Neither model intends to represent any actual target system. Rather, they are constructed to study possible mechanisms through the construction of a model system with built-in dependencies. (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)

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)

This paper distinguishes between causal isolation robustness analysis and independent determination robustness analysis and suggests that the triangulation of the results of different epistemic means or activities serves different functions in them. Circadian clock research is presented as a case of causal isolation robustness analysis: in this field researchers made use of the notion of robustness to isolate the assumed mechanism behind the circadian rhythm. However, in contrast to the earlier philosophical case studies on causal isolation robustness analysis (Weisberg and (...) Reisman in Philos Sci 75:106–131, 2008 ; Kuorikoski et al. in Br J Philos Sci 61:541–567, 2010 ), robustness analysis in the circadian clock research did not remain in the level of mathematical modeling, but it combined it with experimentation on model organisms and a new type of model, a synthetic model. (shrink)

RESUMEN: En el presente trabajo se hará una crítica al enfoque de máquinas nomológicas desarrollado por Nancy Cartwright para el caso de las ciencias sociales. Se argumentará que los fenómenos sociales no responden a una lógica de "capacidades", sino de "árboles de posibilidades" o "resultados de final abierto". La estabilidad o invarianza, si bien es posible en el reino de lo social, parece ser más la excepción que la regla. Esto tiene consecuencias importantes para los propósitos de intervención y control. (...) De acuerdo con las corrientes manipulabilistas tradicionales la invarianza es una herramienta fundamental para cumplir cualquier propósito intervencionista, ya que permite prever con un buen grado de confianza el resultado de una manipulación. Sin embargo, en este trabajo se intentará mostrar que, a pesar de que no sea del todo plausible obtener un conocimiento invariante en el reino de lo social, las intervenciones pueden ser igualmente eficaces. Para ello, será fundamental reemplazar la noción "ex-ante" de intervención por una que involucre continuidad o sistematicidad. También será clave tener en cuenta aquellas cláusulas o condiciones especificadas dentro de los modelos, ya que estos últimos cumplen el rol de ser anteproyectos para la generación de resultados. ABSTRACT: In the present paper, a critique of Nancy Cartwright's nomological machines approach in the social sciences will be made. It is argued that social phenomena do not correspond to the logic of "capacities", but to the logic of "possibility trees" or "open-ended results". Stability or invariance, although possible in the social realm, seem to be more the exception than the rule. This has important implications for the purposes of intervention and control. According to traditional manipulabilistic accounts, invariance is essential for achieving any interventionist purpose, because it allows predicting the result of a manipulation with a high degree of confidence. However, in this paper it will be shown that even though the obtaining of invariant knowledge is not entirely plausible in the social realm, interventions may be effective. To do this, it will be necessary to replace the "ex-ante" notion of intervention with another notion that involves continuity. It will also be crucial to take into account the clauses or conditions specified in models, since such models play the role of being blueprints for generating results. (shrink)

La filosofía estándar de la economía presupone que en el dominio de los fenómenos económicos subyacen regularidades estables, las cuales pueden explicarse mediante el funcionamiento de mecanismos o de máquinas socioeconómicas. Asimismo, se considera que una vez puestos en funcionamiento, su comportamiento no necesita de subsecuentes intervenciones. Esto implica asumir que los procesos socioeconómicos tienen una naturaleza semejante a los de las ciencias naturales. No obstante, dichas regularidades son por lo general examinadas a la luz de algún modelo económico, por (...) lo cual pierden muchas veces contacto con el mundo real. Este trabajo tiene como objetivo poner en el centro del análisis otro objeto de estudio: los procesos económicos basados en expectativas. Se trata de procesos en los cuales la acción humana funciona como nexo causal entre las variables económicas y en los que dicha acción es producto de una formación previa de expectativas, las cuales son sensibles a la información del contexto. Se mostrará que en esta clase de procesos la intervención sistemática sobre las condiciones de fondo y sobre las expectativas es fundamental si lo que se busca es alcanzar un objetivo deseado o lograr un proceso estable. Esta intervención requerirá no solo del conocimiento proporcionado por los modelos, sino también de un conocimiento extrateorético o interdisciplinario. Mainstream philosophy of economics assumes that steady regularities underlie the domain of economic phenomena which can be explained by the operation of mechanisms or socioeconomic machines. It is also considered that once operating those mechanisms don't need further interventions. This implies the assumption that the nature of socioeconomic processes is similar to the one of the natural sciences. Nevertheless, such regularities are usually examined in the light of some economic model, which often ends up losing contact with the real world. The present paper aims at putting in the center of the analysis another object of study: the economic processes based on expectations. In these processes the human action operates as a causal link between economic variables; such an action being the result of a previous building of expectations which are sensitive to the information of the context. It will be shown that the systematic intervention on the background conditions and expectations is fundamental in this kind of processes if what is sought is fulfilling a desired goal or managing a stable process. That intervention will not only require the knowledge provided by models but also an extra-theoretical or interdisciplinary knowledge. (shrink)

1. A multidisciplinary group of scholars within the International Commission on Stratigraphy – known as the Anthropocene Working Group – recently recommended the Anthropocene as a new geological ep...

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)