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  1. Inconsistent idealizations and inferentialism about scientific representation.Peter Tan - 2021 - Studies in History and Philosophy of Science Part A 89 (C):11-18.
    Inferentialists about scientific representation hold that an apparatus’s representing a target system consists in the apparatus allowing “surrogative inferences” about the target. I argue that a serious problem for inferentialism arises from the fact that many scientific theories and models contain internal inconsistencies. Inferentialism, left unamended, implies that inconsistent scientific models have unlimited representational power, since an inconsistency permits any conclusion to be inferred. I consider a number of ways that inferentialists can respond to this challenge before suggesting my own (...)
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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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  • Generative Models.Sim-Hui Tee - 2020 - Erkenntnis 88 (1):23-41.
    Generative models have been proposed as a new type of non-representational scientific models recently. A generative model is characterized with the capacity of producing new models on the basis of the existing one. The current accounts do not explain sufficiently the mechanism of the generative capacity of a generative model. I attempt to accomplish this task in this paper. I outline two antecedent accounts of generative models. I point out that both types of generative models function to generate new homogenous (...)
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  • Argumentative landscapes: the function of models in social epistemology.N. Emrah Aydinonat, Samuli Reijula & Petri Ylikoski - 2021 - 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 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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  • Constructing reality with models.Tee Sim-Hui - 2019 - Synthese 196 (11):4605-4622.
    Scientific models are used to predict and understand the target phenomena in the reality. The kind of epistemic relationship between the model and the reality is always regarded by most of the philosophers as a representational one. I argue that, complementary to this representational role, some of the scientific models have a constructive role to play in altering and reconstructing the reality in a physical way. I hold that the idealized model assumptions and elements bestow the constructive force of a (...)
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  • Incredible Worlds, Credible Results.Jaakko Kuorikoski & Aki Lehtinen - 2009 - Erkenntnis 70 (1):119-131.
    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 (...)
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  • Why We Cannot Learn from Minimal Models.Roberto Fumagalli - 2016 - Erkenntnis 81 (3):433-455.
    Philosophers of science have developed several accounts of how consideration of scientific models can prompt learning about real-world targets. In recent years, various authors advocated the thesis that consideration of so-called minimal models can prompt learning about such targets. In this paper, I draw on the philosophical literature on scientific modelling and on widely cited illustrations from economics and biology to argue that this thesis fails to withstand scrutiny. More specifically, I criticize leading proponents of such thesis for failing to (...)
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  • Why are good theories good? reflections on epistemic values, confirmation, and formal epistemology.Jesús Zamora-Bonilla - 2013 - Synthese 190 (9):1533-1553.
    Franz Huber’s (2008a) attempt to unify inductivist and hypothetico-deductivist intuitions on confirmation by means of a single measure are examined and compared with previous work on the theory of verisimilitude or truthlikeness. The idea of connecting ‘the logic of confirmation’ with ‘the logic of acceptability’ is also critically discussed, and it is argued that ‘acceptability’ takes necessarily into account some pragmatic criteria, and that at least two normative senses of ‘acceptability’ must be distinguished: ‘acceptable’ in the sense of ‘being allowed (...)
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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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  • Scientific Controversies and the Ethics of Arguing and Belief in the Face of Rational Disagreement.Xavier de Donato Rodríguez & Jesús Zamora Bonilla - 2014 - Argumentation 28 (1):39-65.
    Our main aim is to discuss the topic of scientific controversies in the context of a recent issue that has been the centre of attention of many epistemologists though not of argumentation theorists or philosophers of science, namely the ethics of belief in face of rational disagreement. We think that the consideration of scientific examples may be of help in the epistemological debate on rational disagreement, making clear some of the deficiencies of the discussion as it has been produced until (...)
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  • Holistic Idealization: An Artifactual Standpoint.Tarja Knuuttila & Natalia Carrillo - 2022 - Studies in History and Philosophy of Science Part A 91 (C):49-59.
    Idealization is commonly understood as distortion: representing things differently than how they actually are. In this paper, we outline an alternative artifactual approach that does not make misrepresentation central for the analysis of idealization. We examine the contrast between the Hodgkin-Huxley (1952a, b, c) and the Heimburg-Jackson (2005, 2006) models of the nerve impulse from the artifactual perspective, and argue that, since the two models draw upon different epistemic resources and research programs, it is often difficult to tell which features (...)
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  • Models in Economics Are Not (Always) Nomological Machines.Cyril Hédoin - 2014 - Philosophy of the Social Sciences 44 (4):424-459.
    This paper evaluates Nancy Cartwright’s critique of economic models. Cartwright argues that economics fails to build relevant “nomological machines” able to isolate capacities. In this paper, I contend that many economic models are not used as nomological machines. I give some evidence for this claim and build on an inferential and pragmatic approach to economic modeling. Modeling in economics responds to peculiar inferential norms where a “good” model is essentially a model that enhances our knowledge about possible worlds. As a (...)
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  • Scientific representation.Roman Frigg & James Nguyen - 2016 - Stanford Encyclopedia of Philosophy.
    Science provides us with representations of atoms, elementary particles, polymers, populations, genetic trees, economies, rational decisions, aeroplanes, earthquakes, forest fires, irrigation systems, and the world’s climate. It's through these representations that we learn about the world. This entry explores various different accounts of scientific representation, with a particular focus on how scientific models represent their target systems. As philosophers of science are increasingly acknowledging the importance, if not the primacy, of scientific models as representational units of science, it's important to (...)
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  • Credible Worlds, Capacities and Mechanisms.Robert Sugden - 2009 - Erkenntnis 70 (1):3-27.
    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 (...)
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  • Understanding with theoretical models.Petri Ylikoski & N. Emrah Aydinonat - 2014 - Journal of Economic Methodology 21 (1):19-36.
    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 (...)
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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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  • Are climate models credible worlds? Prospects and limitations of possibilistic climate prediction.Gregor Betz - 2015 - European Journal for Philosophy of Science 5 (2):191-215.
    Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would allow one to (...)
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  • Isolating Representations Versus Credible Constructions? Economic Modelling in Theory and Practice.Tarja Knuuttila - 2009 - Erkenntnis 70 (1):59-80.
    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 (...)
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  • Derivational robustness, credible substitute systems and mathematical economic models: the case of stability analysis in Walrasian general equilibrium theory.D. Wade 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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  • ‘Situational Analysis’ and Economics: an attempt at clarification.Alfonso Palacio-Vera - 2019 - Economics and Philosophy 35 (3):479-498.
    Popper’s ‘Situational Analysis’ (SA) constitutes his methodological proposal for the social sciences. We claim that the two hallmarks of SA are: (i) that scientists assume they possess a ‘wider’ view of the problem-situation than actors do, and (ii) use the model as an ideal ‘benchmark’ scenario to identify thedeviationof actors’ actual behaviour from the former. We argue that SA is not a generalization of the neoclassical theory of individual behaviour but captures instead the methodology adopted by modern behavioural economists. Last, (...)
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  • (1 other version)Inferentialism, degrees of commitment, and ampliative reasoning.Jesús Zamora Bonilla, Xavier de Donato Rodríguez & Javier González de Prado Salas - 2017 - Synthese 198 (Suppl 4):909-927.
    Our purpose in this paper is to contribute to a practice-based characterization of scientific inference. We want to explore whether Brandom’s pragmatist–inferentialist framework can suitably accommodate several types of ampliative inference common in scientific reasoning and explanation (probabilistic reasoning, abduction and idealisation). First, we argue that Brandom’s view of induction in terms of merely permissive inferences is inadequate; in order to overcome the shortcoming of Brandom’s proposal, we put forward an alternative conception of inductive, probabilistic reasoning by appeal to the (...)
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  • Collective Actors without Collective Minds: An Inferentialist Approach.Javier González de Prado Salas & Jesús Zamora-Bonilla - 2015 - Philosophy of the Social Sciences 45 (1):3-25.
    We present an inferentialist account of collective rationality and intentionality, according to which beliefs and other intentional states are understood in terms of the normative statuses attributed to, and undertaken by, the participants of a discursive practice—namely, their discursive or practical commitments and entitlements. Although these statuses are instituted by the performances and attitudes of the agents, they are not identified with any physical or psychological entity, process or relation. Therefore, we argue that inferentialism allows us to talk of collective (...)
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  • (1 other version)Inferentialism, degrees of commitment, and ampliative reasoning.Rodríguez Xavier de Donato, Bonilla Jesús Zamora & Javier González De Prado Salas - 2017 - Synthese 198 (Suppl 4):909-927.
    Our purpose in this paper is to contribute to a practice-based characterization of scientific inference. We want to explore whether Brandom’s pragmatist–inferentialist framework can suitably accommodate several types of ampliative inference common in scientific reasoning and explanation (probabilistic reasoning, abduction and idealisation). First, we argue that Brandom’s view of induction in terms of merely permissive inferences is inadequate; in order to overcome the shortcoming of Brandom’s proposal, we put forward an alternative conception of inductive, probabilistic reasoning by appeal to the (...)
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  • Conceptual Constructive Models and Abstraction-as-Aggregation.Sim-Hui Tee - 2021 - Philosophia 49 (2):819-837.
    Conceptual constructive models are a type of scientific model that can be used to construct or reshape the target phenomenon conceptually. Though it has received scant attention from the philosophers, it raises an intriguing issue of how a conceptual constructive model can construct the target phenomenon in a conceptual way. Proponents of the conception of conceptual constructive models are not being explicit about the application of the constructive force of a model in the target construction. It is far from clear (...)
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  • Abstraction as an Autonomous Process in Scientific Modeling.Sim-Hui Tee - 2020 - Philosophia 48 (2):789-801.
    ion is one of the important processes in scientific modeling. It has always been implied that abstraction is an agent-centric activity that involves the cognitive processes of scientists in model building. I contend that there is an autonomous aspect of abstraction in many modeling activities. I argue that the autonomous process of abstraction is continuous with the agent-centric abstraction but capable of evolving independently from the modeler’s abstraction activity.
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  • The Representational Semantic Conception.Mauricio Suárez & Francesca Pero - 2019 - Philosophy of Science 86 (2):344-365.
    This paper argues for a representational semantic conception of scientific theories, which respects the bare claim of any semantic view, namely that theories can be characterised as sets of models. RSC must be sharply distinguished from structural versions that assume a further identity of ‘models’ and ‘structures’, which we reject. The practice-turn in the recent philosophical literature suggests instead that modelling must be understood in a deflationary spirit, in terms of the diverse representational practices in the sciences. These insights are (...)
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