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  1. Simulations, Explication, Compréhension : Essai D’Analyse critiqueSimulations, Explanation, Understanding: An Analytical Overview.Cyrille Imbert - 2017 - Philosophia Scientae 21:49-109.
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  • Social Epistemology and Validation in Agent-Based Social Simulation.David Anzola - 2021 - Philosophy and Technology 34 (4):1333-1361.
    The literature in agent-based social simulation suggests that a model is validated when it is shown to ‘successfully’, ‘adequately’ or ‘satisfactorily’ represent the target phenomenon. The notion of ‘successful’, ‘adequate’ or ‘satisfactory’ representation, however, is both underspecified and difficult to generalise, in part, because practitioners use a multiplicity of criteria to judge representation, some of which are not entirely dependent on the testing of a computational model during validation processes. This article argues that practitioners should address social epistemology to achieve (...)
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  • Capturing the representational and the experimental in the modelling of artificial societies.David Anzola - 2021 - European Journal for Philosophy of Science 11 (3):1-29.
    Even though the philosophy of simulation is intended as a comprehensive reflection about the practice of computer simulation in contemporary science, its output has been disproportionately shaped by research on equation-based simulation in the physical and climate sciences. Hence, the particularities of alternative practices of computer simulation in other scientific domains are not sufficiently accounted for in the current philosophy of simulation literature. This article centres on agent-based social simulation, a relatively established type of simulation in the social sciences, to (...)
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  • Mapping an Expanding Territory: Computer Simulations in Evolutionary Biology.Philippe Huneman - 2014 - History and Philosophy of the Life Sciences 36 (1):60-89.
    The pervasive use of computer simulations in the sciences brings novel epistemological issues discussed in the philosophy of science literature since about a decade. Evolutionary biology strongly relies on such simulations, and in relation to it there exists a research program (Artificial Life) that mainly studies simulations themselves. This paper addresses the specificity of computer simulations in evolutionary biology, in the context (described in Sect. 1) of a set of questions about their scope as explanations, the nature of validation processes (...)
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  • Scientific Understanding: Truth or Dare?Henk W. de Regt - 2015 - Synthese 192 (12):3781-3797.
    It is often claimed—especially by scientific realists—that science provides understanding of the world only if its theories are (at least approximately) true descriptions of reality, in its observable as well as unobservable aspects. This paper critically examines this ‘realist thesis’ concerning understanding. A crucial problem for the realist thesis is that (as study of the history and practice of science reveals) understanding is frequently obtained via theories and models that appear to be highly unrealistic or even completely fictional. So we (...)
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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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  • Scientific Fictions as Rules of Inference.Mauricio Suárez - 2009 - In Fictions in Science: Philosophical Essays on Modeling and Idealization. Routledge. pp. 158--178.
  • Generative Explanation and Individualism in Agent-Based Simulation.Caterina Marchionni & Petri Ylikoski - 2013 - Philosophy of the Social Sciences 43 (3):323-340.
    Social scientists associate agent-based simulation (ABS) models with three ideas about explanation: they provide generative explanations, they are models of mechanisms, and they implement methodological individualism. In light of a philosophical account of explanation, we show that these ideas are not necessarily related and offer an account of the explanatory import of ABS models. We also argue that their bottom-up research strategy should be distinguished from methodological individualism.
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  • Economics as Robustness Analysis.Jaakko Kuorikoski, Aki Lehtinen & Caterina Marchionni - unknown
    All economic models involve abstractions and idealisations. Economic theory itself does not tell which idealizations are truly fatal or harmful for the result and which are not. This is why much of what is seen as theoretical contribution in economics is constituted by deriving familiar results from different modelling assumptions. If a modelling result is robust with respect to particular modelling assumptions, the empirical falsity of these particular assumptions does not provide grounds for criticizing the result. In this paper we (...)
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  • Economic Modelling as Robustness Analysis.Jaakko Kuorikoski, Aki Lehtinen & Caterina Marchionni - 2010 - British Journal for the Philosophy of Science 61 (3):541-567.
    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 (...)
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  • Simulation and the Sense of Understanding.Jaakko Kuorikoski - 2011 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. London: Routledge. pp. 168-187.
    Whether simulation models provide the right kind of understanding comparable to that of analytic models has been and remains a contentious issue. The assessment of understanding provided by simulations is often hampered by a conflation between the sense of understanding and understanding proper. This paper presents a deflationist conception of understanding and argues for the need to replace appeals to the sense of understanding with explicit criteria of explanatory relevance and for rethinking the proper way of conceptualizing the role of (...)
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  • Disagreement in Discipline-Building Processes.David Anzola - 2019 - Synthese 198 (Suppl 25):6201-6224.
    Successful instances of interdisciplinary collaboration can eventually enter a process of disciplinarisation. This article analyses one of those instances: agent-based computational social science, an emerging disciplinary field articulated around the use of computational models to study social phenomena. The discussion centres on how, in knowledge transfer dynamics from traditional disciplinary areas, practitioners parsed several epistemic resources to produce new foundational disciplinary shared commitments, and how disagreements operated as a mechanism of differentiation in their production. Two parsing processes are examined to (...)
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  • Pluralism in Economics: Its Critiques and Their Lessons.Claudius Gräbner & Birte Strunk - 2020 - Journal of Economic Methodology 27 (4):311-329.
    This paper starts with an evaluation of three common arguments against pluralism in economics: the claim that economics is already pluralist, the argument that if there was the need for gre...
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  • The Helsinki Approach to Economic Methodology, or, How to Espouse the Mainstream?Aki Lehtinen - 2021 - Journal of Economic Methodology 28 (1):79-87.
    This paper provides metaphilosophical reflections on economic methodology by way of discussing the ‘Helsinki approach’. This approach combines the study of the practice of economics with an endeavo...
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  • Modeling Practices in the Social and Human Sciences. An Interdisciplinary Exchange.Mary S. Morgan & Till Grüne-Yanoff - 2013 - Perspectives on Science 21 (2):143-156.
    Philosophers of science studying scientific practice often consider it a methodological requirement that their conceptualization of "model" closely connects with the understanding and use of models by practicing scientists. Occasionally, this connection has been explicitly made (Hutten 1954, Suppes 1961, Morgan and Morrison 1999, Bailer-Jones 2002, Lehtinen and Kuorikoski 2007, Kuorikoski 2007, Morgan 2012a). These studies have been dominated by a focus on the—relatively similar forms of—mathematical models in physics and economics. Yet it has become increasingly evident that the way (...)
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  • Explaining with Simulations: Why Visual Representations Matter.Julie Jebeile - 2018 - Perspectives on Science 26 (2):213-238.
    Mathematical models are often expected to provide not only predictions about the phenomenon that they represent, but also explanations. These explanations are answers to why-questions and particularly answers to why the predicted phenomenon should occur. For instance, models can be used to calculate when the next total solar eclipse will happen, and then to explain why it will take place on July 2, 2019. In this regard we can obtain explanations from a model if we can solve the model equations (...)
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  • Agent-Based Simulation and Sociological Understanding.Petri Ylikoski - 2014 - Perspectives on Science 22 (3):318-335.
    This article discusses agent-based simulation (ABS) as a tool of sociological understanding. I argue that agent-based simulations can play an important role in the expansion of explanatory understanding in the social sciences. The argument is based on an inferential account of understanding (Ylikoski 2009, Ylikoski & Kuorikoski 2010), according to which computer simulations increase our explanatory understanding by expanding our ability to make what-if inferences about social processes and by making these inferences more reliable. The inferential account also suggests a (...)
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  • Simulating Peer Disagreements.Igor Douven - 2010 - Studies in History and Philosophy of Science Part A 41 (2):148-157.
    It has been claimed that epistemic peers, upon discovering that they disagree on some issue, should give up their opposing views and ‘split the difference’. The present paper challenges this claim by showing, with the help of computer simulations, that what the rational response to the discovery of peer disagreement is—whether it is sticking to one’s belief or splitting the difference—depends on factors that are contingent and highly context-sensitive.Keywords: Peer disagreement; Computer simulations; Opinion dynamics; Hegselmann–Krause model; Social epistemology.
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  • The Explanation Paradox.Julian Reiss - 2012 - Journal of Economic Methodology 19 (1):43-62.
    This paper examines mathematical models in economics and observes that three mutually inconsistent hypotheses concerning models and explanation are widely held: (1) economic models are false; (2) economic models are nevertheless explanatory; and (3) only true accounts explain. Commentators have typically resolved the paradox by rejecting either one of these hypotheses. I will argue that none of the proposed resolutions work and conclude that therefore the paradox is genuine and likely to stay.
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  • Teaching Philosophy of Science to Scientists: Why, What and How.Till Grüne-Yanoff - 2014 - European Journal for Philosophy of Science 4 (1):115-134.
    This paper provides arguments to philosophers, scientists, administrators and students for why science students should be instructed in a mandatory, custom-designed, interdisciplinary course in the philosophy of science. The argument begins by diagnosing that most science students are taught only conventional methodology: a fixed set of methods whose justification is rarely addressed. It proceeds by identifying seven benefits that scientists incur from going beyond these conventions and from acquiring abilities to analyse and evaluate justifications of scientific methods. It concludes that (...)
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  • Formal Methods in the Philosophy of Science.Leon Horsten & Igor Douven - 2008 - Studia Logica 89 (2):151-162.
    In this article, we reflect on the use of formal methods in the philosophy of science. These are taken to comprise not just methods from logic broadly conceived, but also from other formal disciplines such as probability theory, game theory, and graph theory. We explain how formal modelling in the philosophy of science can shed light on difficult problems in this domain.
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  • Big Data and Complexity: Is Macroeconomics Heading Toward a New Paradigm?Paola D’Orazio - 2017 - Journal of Economic Methodology 24 (4):410-429.
    The paper discusses the extent to which the availability of unprecedentedly rich data-sets and the need for new approaches – both epistemological and computational – is an emerging issue for Macroeconomics. By adopting an evolutionary approach, we describe the paradigm shifts experienced in the macroeconomic research field and emphasize that the types of data the macroeconomist has to deal with play an important role in the evolutionary process of the development of the discipline. After introducing the current debate over Big (...)
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  • Unification and Mechanistic Detail as Drivers of Model Construction: Models of Networks in Economics and Sociology.Jaakko Kuorikoski & Caterina Marchionni - 2014 - Studies in History and Philosophy of Science Part A 48:97-104.