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  1. Homepage Eckhart Arnold.Eckhart Arnold (ed.) - 2001 - Munich: Preprint.
    This is my personal homepage. Find my philosophical papers under "Philosophy".
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  • I modelli in economia.Alessandra Basso & Caterina Marchionni - 2015 - Aphex 11.
    The paper reviews the philosophical literature on the epistemology of modelling in contemporary economics. In particular, it focuses on open questions concerning the epistemic role of models, the validity of inferences from the models to the world, and the legitimacy of their use for purposes of explanation, prediction and intervention.
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  • Punto de vista lógico y no representacionista del razonamiento sustitutivo.Juan Redmond, Rodrigo Lopez-Orellana & Loreto Paniagua - 2021 - Cuadernos Filosóficos / Segunda Época 18.
    In this paper we argue, from an inferential approach, that the inferential role played by a model, during modeling practice, is independent of the notion of representation engaged with the chosen modeling approach. Indeed, we believe that the notion of surrogative reasoning is neither subsidiary nor founded on the notion of representation and that it will only find its foundations in logic itself. Neither the notion of representation is an inferential notion nor FIM is a type of representation-based thinking.
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  • Modeling Intentional Agency: A Neo-Gricean Framework.Matti Sarkia - 2021 - Synthese 199 (3-4):7003-7030.
    This paper analyzes three contrasting strategies for modeling intentional agency in contemporary analytic philosophy of mind and action, and draws parallels between them and similar strategies of scientific model-construction. Gricean modeling involves identifying primitive building blocks of intentional agency, and building up from such building blocks to prototypically agential behaviors. Analogical modeling is based on picking out an exemplary type of intentional agency, which is used as a model for other agential types. Theoretical modeling involves reasoning about intentional agency in (...)
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  • The Fiction View of Models Reloaded.Roman Frigg & James Nguyen - 2016 - The Monist 99 (3):225-242.
    In this paper we explore the constraints that our preferred account of scientific representation places on the ontology of scientific models. Pace the Direct Representation view associated with Arnon Levy and Adam Toon we argue that scientific models should be thought of as imagined systems, and clarify the relationship between imagination and representation.
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  • Learning from Non-Causal Models.Francesco Nappo - forthcoming - Erkenntnis:1-21.
    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 (...)
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  • Computational Functionalism for the Deep Learning Era.Ezequiel López-Rubio - 2018 - Minds and Machines 28 (4):667-688.
    Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of deep learning, namely the extraction of successively (...)
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  • How Models Represent.James Nguyen - 2016 - Dissertation,
    Scientific models are important, if not the sole, units of science. This thesis addresses the following question: in virtue of what do scientific models represent their target systems? In Part i I motivate the question, and lay out some important desiderata that any successful answer must meet. This provides a novel conceptual framework in which to think about the question of scientific representation. I then argue against Callender and Cohen’s attempt to diffuse the question. In Part ii I investigate the (...)
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  • Sustainability and the Infinite Future: A Case Study of a False Modeling Assumption in Environmental Economics.Daniel Steel - 2017 - Erkenntnis 82 (5):1065-1084.
    This essay examines the issue of false assumptions in models via a case study of a prominent economic model of sustainable development, wherein the assumption of an infinite future plays a central role. Two proposals are found to be helpful for this case, one based on the concept of derivational robustness and the other on understanding. Both suggest that the assumption of an infinite future, while arguably legitimate in some applications of the model, is problematic with respect to what I (...)
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  • Learning From Minimal Economic Models.Till Grüne-Yanoff - 2009 - Erkenntnis 70 (1):81-99.
    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 (...)
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  • Appraising Non-Representational Models.Till Grüne-Yanoff - unknown
    Many scientific models are non-representational in that they refer to merely possible processes, background conditions and results. The paper shows how such non-representational models can be appraised, beyond the weak role that they might play as heuristic tools. Using conceptual distinctions from the discussion of how-possibly explanations, six types of models are distinguished by their modal qualities of their background conditions, model processes and model results. For each of these types, an actual model example – drawn from economics, biology, psychology (...)
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  • Not-So-Minimal Models: Between Isolation and Imagination.Lorenzo Casini - 2014 - Philosophy of the Social Sciences 44 (5):646-672.
    What can we learn from “minimal” economic models? I argue that learning from such models is not limited to conceptual explorations—which show how something could be the case—but may extend to explanations of real economic phenomena—which show how something is the case. A model may be minimal qua certain world-linking properties, and yet “not-so-minimal” qua learning, provided it is externally valid. This, in turn, depends on using the right principles for model building and not necessarily “isolating” principles. My argument is (...)
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  • Model Pluralism.Walter Veit - 2019 - Philosophy of the Social Sciences 50 (2):91-114.
    This paper introduces and defends an account of model-based science that I dub model pluralism. I argue that despite a growing awareness in the philosophy of science literature of the multiplicity, diversity, and richness of models and modeling practices, more radical conclusions follow from this recognition than have previously been inferred. Going against the tendency within the literature to generalize from single models, I explicate and defend the following two core theses: any successful analysis of models must target sets of (...)
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  • The Analytic-Continental Divide in Philosophical Practice: An Empirical Study.Moti Mizrahi & Mike Dickinson - 2021 - Metaphilosophy 52 (5):668-680.
    Philosophy is often divided into two traditions: analytic and continental philosophy. Characterizing the analytic-continental divide, however, is no easy task. Some philosophers explain the divide in terms of the place of argument in these traditions. This raises the following questions: Is analytic philosophy rife with arguments while continental philosophy is devoid of arguments? Or can different types of arguments be found in analytic and continental philosophy? This paper presents the results of an empirical study of a large corpus of philosophical (...)
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  • It’s Not a Game: Accurate Representation with Toy Models.James Nguyen - 2020 - British Journal for the Philosophy of Science 71 (3):1013-1041.
    Drawing on ‘interpretational’ accounts of scientific representation, I argue that the use of so-called ‘toy models’ provides no particular philosophical puzzle. More specifically; I argue that once one gives up the idea that models are accurate representations of their targets only if they are appropriately similar, then simple and highly idealized models can be accurate in the same way that more complex models can be. Their differences turn on trading precision for generality, but, if they are appropriately interpreted, toy models (...)
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  • Non-Causal Understanding with Economic Models: The Case of General Equilibrium.Philippe Verreault-Julien - 2017 - Journal of Economic Methodology 24 (3):297-317.
    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 (...)
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  • How Could Models Possibly Provide How-Possibly Explanations?Philippe Verreault-Julien - 2019 - Studies in History and Philosophy of Science Part A 73:1-12.
    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 (...)
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  • External Representations and Scientific Understanding.Jaakko Kuorikoski & Petri Ylikoski - 2015 - Synthese 192 (12):3817-3837.
    This paper provides an inferentialist account of model-based understanding by combining a counterfactual account of explanation and an inferentialist account of representation with a view of modeling as extended cognition. This account makes it understandable how the manipulation of surrogate systems like models can provide genuinely new empirical understanding about the world. Similarly, the account provides an answer to the question how models, that always incorporate assumptions that are literally untrue of the model target, can still provide factive explanations. Finally, (...)
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  • Models in Science and Engineering: Imagining, Designing and Evaluating Representations.Michael Poznic - 2017 - Dissertation, Delft University of Technology
    The central question of this thesis is how one can learn about particular targets by using models of those targets. A widespread assumption is that models have to be representative models in order to foster knowledge about targets. Thus the thesis begins by examining the concept of representation from an epistemic point of view and supports an account of representation that does not distinguish between representation simpliciter and adequate representation. Representation, understood in the sense of a representative model, is regarded (...)
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  • The Diversity of Rational Choice Theory: A Review Note.Catherine Herfeld - 2020 - Topoi 39 (2):329-347.
    In this paper, I review the literature on rational choice theory to scrutinize a number of criticisms that philosophers have voiced against its usefulness in economics. The paper has three goals: first, I argue that the debates about RCT have been characterized by disunity and confusion about the object under scrutiny, which calls into question the effectiveness of those criticisms. Second, I argue that RCT is not a single and unified choice theory—let alone an empirical theory of human behavior—as some (...)
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  • Current Perspectives in Philosophy of Biology.Joaquin Suarez Ruiz & Rodrigo A. Lopez Orellana - 2019 - Humanities Journal of Valparaiso 14:7-426.
    Current Perspectives in Philosophy of Biology.
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  • An inferential and dynamic approach to modeling and understanding in biology.Rodrigo Lopez-Orellana, Juan Redmond & David Cortés-García - 2019 - Humanities Journal of Valparaiso 14:315-334.
    This paper aims to propose an inferential and dynamic approach to understanding with models in biology. Understanding plays a central role in the practice of modeling. From its links with the other two central elements of scientific research, experimentation, and explanation, we show its epistemic relevance to the case of explanation in biology. Furthermore, by including the notion of understanding, we propose a non-referentialist perspective on scientific models, which is determined by their use.
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  • Tools or Toys? On Specific Challenges for Modeling and the Epistemology of Models and Computer Simulations in the Social Sciences.Eckhart Arnold - manuscript
    Mathematical models are a well established tool in most natural sciences. Although models have been neglected by the philosophy of science for a long time, their epistemological status as a link between theory and reality is now fairly well understood. However, regarding the epistemological status of mathematical models in the social sciences, there still exists a considerable unclarity. In my paper I argue that this results from specific challenges that mathematical models and especially computer simulations face in the social sciences. (...)
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  • What’s Wrong with Social Simulations?Eckhart Arnold - 2014 - The Monist 97 (3):359-377.
    This paper tries to answer the question why the epistemic value of so many social simulations is questionable. I consider the epistemic value of a social simulation as questionable if it contributes neither directly nor indirectly to the understanding of empirical reality. To examine this question, two classical social simulations are analyzed with respect to their possible epistemic justification: Schelling’s neighborhood segregation model and Axelrod’s reiterated Prisoner’s Dilemma simulations of the evolution of cooperation. It is argued that Schelling’s simulation is (...)
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  • The Market for Scientific Lemons, and the Marketization of Science.Jesús Zamora Bonilla - 2019 - Theoria : An International Journal for Theory, History and Fundations of Science 34 (1):133-145.
    Scientific research is based on the division of cognitive labour: every scientist has to trust that other colleagues have checked whether the items that are taken as knowledge, and she cannot check by herself, are reliable enough. I apply ideas from the field known as ‘information economics’ to analyse the scientists’ incentives to produce items of knowledge of an ‘adequate’ quality, under the assumption that a big part of what one observes in her empirical research is not available for the (...)
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  • Philosophical Foundations of Neuroeconomics: Economics and the Revolutionary Challenge From Neuroscience.Roberto Fumagalli - 2011 - Dissertation, London School of Economics
    This PhD thesis focuses on the philosophical foundations of Neuroeconomics, an innovative research program which combines findings and modelling tools from economics, psychology and neuroscience to account for human choice behaviour. The proponents of Neuroeconomics often manifest the ambition to foster radical modifications in the accounts of choice behaviour developed by its parent disciplines. This enquiry provides a philosophically informed appraisal of the potential for success and the relevance of neuroeconomic research for economics. My central claim is that neuroeconomists can (...)
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  • Is Credibility a Guide to Possibility? A Challenge for Toy Models in Science.Ylwa Sjölin Wirling - 2021 - Analysis 81 (3):470-478.
    Several philosophers of science claim that scientific toy models afford knowledge of possibility, but answers to the question of why toy models can be expected to competently play this role are scarce. The main line of reply is that toy models support possibility claims insofar as they are credible. I raise a challenge for this credibility-thesis, drawing on a familiar problem for imagination-based modal epistemologies, and argue that it remains unanswered in the current literature. The credibility-thesis has a long way (...)
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  • The New Fiction View of Models.Fiora Salis - 2021 - British Journal for the Philosophy of Science 72 (3):717-742.
    How do models represent reality? There are two conditions that scientific models must satisfy to be representations of real systems, the aboutness condition and the epistemic condition. In this article, I critically assess the two main fictionalist theories of models as representations, the indirect fiction view and the direct fiction view, with respect to these conditions. And I develop a novel proposal, what I call ‘the new fiction view of models’. On this view, models are akin to fictional stories; they (...)
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  • The Epistemology of Modal Modeling.Ylwa Sjölin Wirling & Till Grüne-Yanoff - 2021 - Philosophy Compass 16 (10):e12775.
    Philosophers of science have recently taken care to highlight different modeling practices where scientific models primarily contribute modal information, in the form of for example possibility claims, how-possibly explanations, or counterfactual conditionals. While examples abound, comparatively little attention is being paid to the question of under what conditions, and in virtue of what, models can perform this epistemic function. In this paper, we firstly delineate modal modeling from other modeling practices, and secondly reviewattempts to spell out and explain the epistemic (...)
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  • Argumentative Landscapes: The Function of Models in Social Epistemology.N. Emrah Aydinonat, Samuli Reijula & Petri Ylikoski - forthcoming - Synthese 199 (1-2):369-395.
    We argue that the appraisal of models in social epistemology requires conceiving of them as argumentative devices, taking into account the argumentative context and adopting a family-of-models perspective. We draw up such an account and show how it makes it easier to see the value and limits of the use of models in social epistemology. To illustrate our points, we document and explicate the argumentative role of epistemic landscape models in social epistemology and highlight their limitations. We also claim that (...)
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  • Model Templates Within and Between Disciplines: From Magnets to Gases – and Socio-Economic Systems.Tarja Knuuttila & Andrea Loettgers - 2016 - European Journal for Philosophy of Science 6 (3):377-400.
    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. (...)
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  • Normative Models and Their Success.Lukas Beck & Marcel Jahn - 2021 - Philosophy of the Social Sciences 51 (2):123-150.
    In this paper, we explore an under-investigated question concerning the class of formal models that aim at providing normative guidance. We call such models normative models. In particular, we examine the question of how normative models can successfully exert normative guidance. First, we highlight the absence of a discussion of this question – which is surprising given the extensive debate about the success conditions of descriptive models – and motivate its importance. Second, we introduce and discuss two potential accounts of (...)
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  • What is a Target System?Alkistis Elliott-Graves - 2020 - Biology and Philosophy 35 (2):1-22.
    Many phenomena in the natural world are complex, so scientists study them through simplified and idealised models. Philosophers of science have sought to explain how these models relate to the world. On most accounts, models do not represent the world directly, but through target systems. However, our knowledge of target systems is incomplete. First, what is the process by which target systems come about? Second, what types of entity are they? I argue that the basic conception of target systems, on (...)
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  • Resolving and Understanding Differences Between Agent-Based Accounts of Scientific Representation.Brandon Boesch - 2019 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 50 (2):195-213.
    Agent-based accounts of scientific representation all agree that the representational relationship is constituted by the actions of scientists. Despite this agreement, there are several differences in how agent-based accounts describe scientific representation. In this essay, I argue that these differences do not undercut the compatibility between the accounts. I make my argument by examining the nature of human agency and demonstrating that scientific, representational actions are multiply describable. I then argue that the differences between the accounts are valuable because they (...)
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  • Thin Versus Thick Accounts of Scientific Representation.Michael Poznic - 2018 - Synthese 195 (8):3433-3451.
    This paper proposes a novel distinction between accounts of scientific representation: it distinguishes thin accounts from thick accounts. Thin accounts focus on the descriptive aspect of representation whereas thick accounts acknowledge the evaluative aspect of representation. Thin accounts focus on the question of what a representation as such is. Thick accounts start from the question of what an adequate representation is. In this paper, I give two arguments in favor of a thick account, the Argument of the Epistemic Aims of (...)
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  • What is the Problem with Model-Based Explanation in Economics?Caterina Marchionni - 2017 - Disputatio 9 (47):603-630.
    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 (...)
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  • Del Círculo de Viena a nuestros días: una historia de enredos. [REVIEW]Agustina Borella - 2019 - Economía: Teoría y Práctica 27:233-238.
    Introducir un recorrido a través de la historia y la teoría propia de la filosofía dela economía es una tarea del todo ambiciosa, sin embargo, Mariusz Maziarz lo lleva adelante en esta obra, que se puede considerar un manual actualizado de la filosofía de la economía y, en particular, de la epistemología de la economía. Aun-que no es un libro de historia del pensamiento económico ni de historia de la filosofía de las ciencias, sino más bien una iniciación ordenada a (...)
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  • Multiple Models, One Explanation.Chiara Lisciandra & Johannes Korbmacher - 2021 - Journal of Economic Methodology 28 (2):186-206.
    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...
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  • Models as ‘Analytical Similes’: On Nicholas Georgescu-Roegen's Contribution to Economic Methodology.Quentin Couix - 2021 - Journal of Economic Methodology 28 (2):165-185.
    This paper investigates the methodology of Nicholas Georgescu-Roegen and his conception of economic models as analytical similes. His approach has received little attention from mathematical econom...
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  • When Experiments Need Models.Donal Khosrowi - 2021 - Philosophy of the Social Sciences 51 (4):400-424.
    This paper argues that an important type of experiment-target inference, extrapolating causal effects, requires models to be successful. Focusing on extrapolation in Evidence-Based Policy, it is ar...
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  • Derivational Robustness, Credible Substitute Systems and Mathematical Economic Models: The Case of Stability Analysis in Walrasian General Equilibrium Theory.D. Hands - 2016 - European Journal for Philosophy of Science 6 (1):31-53.
    This paper supports the literature which argues that derivational robustness can have epistemic import in highly idealized economic models. The defense is based on a particular example from mathematical economic theory, the dynamic Walrasian general equilibrium model. It is argued that derivational robustness first increased and later decreased the credibility of the Walrasian model. The example demonstrates that derivational robustness correctly describes the practices of a particular group of influential economic theorists and provides support for the arguments of philosophers who (...)
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  • How to Use Fitness Landscape Models for the Analysis of Collective Decision-Making: A Case of Theory-Transfer and its Limitations.Peter Marks, Lasse Gerrits & Johannes Marx - 2019 - Biology and Philosophy 34 (1):7.
    There is considerable correspondence between theories and models used in biology and the social sciences. One type of model that is in use in both biology and the social sciences is the fitness landscape model. The properties of the fitness landscape model have been applied rather freely in the social domain. This is partly due to the versatility of the model, but it is also due to the difficulties of transferring a model to another domain. We will demonstrate that in (...)
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  • On the Neural Enrichment of Economic Models: Recasting the Challenge.Roberto Fumagalli - 2017 - Biology and Philosophy 32 (2):201-220.
    In a recent article in this Journal, Fumagalli argues that economists are provisionally justified in resisting prominent calls to integrate neural variables into economic models of choice. In other articles, various authors engage with Fumagalli’s argument and try to substantiate three often-made claims concerning neuroeconomic modelling. First, the benefits derivable from neurally informing some economic models of choice do not involve significant tractability costs. Second, neuroeconomic modelling is best understood within Marr’s three-level of analysis framework for information-processing systems. And third, (...)
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  • The Need for Governmental Inefficiency in Plato’s Republic.Gil Hersch - 2021 - Journal of History of Economic Thought 43 (1):103 - 117.
    In book II of Plato’s Republic, Socrates discusses the cities of necessity and luxury (372d-373a). Discussions of these cities have often focused on citizens desiring more than they need, which creates a demand for luxury. Yet the second part of the equation, which is not usually recognized, is that there must be sufficient supply to meet this demand. The focus of this article is on the importance of supply in the discussion of the first two cities in book II of (...)
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  • Los desafíos del Realismo Crítico Auténtico.Agustina Borella - 2014 - In Tópicos de epistemología. Buenos Aires, CABA, Argentina: pp. 149-162.
    El Realismo Crítico Auténtico (RCA) sostenido por Uskali Mäki se distingue del Realismo Crítico de Lawson. En el marco del Realismo Mínimo se explicitará qué es el RCA y se presentará el Realismo Posible de los modelos económicos en su versión MISS. Se diferenciará el Realismo Mínimo del Realismo Científico Standard. Se desarrollarán las dificultades en torno al RCA.
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  • The Heuristic Defense of Scientific Models: An Incentive-Based Assessment.Armin W. Schulz - 2015 - Perspectives on Science 23 (4):424-442.
    It is undeniable that much scientific work is model-based. Despite this, the justification for this reliance on models is still controversial. A particular difficulty here is the fact that many scientific models are based on assumptions that do not describe the exact details of many or even any empirical situations very well. This raises the question of why it is that, despite their frequent lack of descriptive accuracy, employing models is scientifically useful.One..
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  • The Field: Tasks, Pasts, Futures.Uskali Mäki - 2021 - Journal of Economic Methodology 28 (1):3-13.
    The paper sketches a story about how and why the field of economic methodology / philosophy of economics emerged, and how it has evolved intellectually and ins...
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  • Epistemic Contributions of Models: Conditions for Propositional Learning.François Claveau - 2015 - Perspectives on Science 23 (4):405-423.
    Models are powerful tools that can make us learn. Few contemporary observers of science doubt that, and economists agree; the highest honours of their discipline go to the most influential model builders. Among a long list of modellers who are Nobel laureates, we count Peter A. Diamond, Dale T. Mortensen and Christopher A. Pissarides, who were awarded the prize in 2010 as a recognition of their work in developing a model of the labor market—the DMP model.1While researchers agree that models (...)
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  • What is a Simulation Model?Juan M. Durán - 2020 - Minds and Machines 30 (3):301-323.
    Many philosophical accounts of scientific models fail to distinguish between a simulation model and other forms of models. This failure is unfortunate because there are important differences pertaining to their methodology and epistemology that favor their philosophical understanding. The core claim presented here is that simulation models are rich and complex units of analysis in their own right, that they depart from known forms of scientific models in significant ways, and that a proper understanding of the type of model simulations (...)
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  • Understanding From Machine Learning Models.Emily Sullivan - forthcoming - British Journal for the Philosophy of Science:axz035.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In (...)
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