19 found
Order:
See also
Julie Jebeile
University of Bern
  1.  17
    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 (...)
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark   4 citations  
  2.  39
    Explaining with Models: The Role of Idealizations.Julie Jebeile & Ashley Graham Kennedy - 2015 - International Studies in the Philosophy of Science 29 (4):383-392.
    Because they contain idealizations, scientific models are often considered to be misrepresentations of their target systems. An important question is therefore how models can explain the behaviours of these systems. Most of the answers to this question are representationalist in nature. Proponents of this view are generally committed to the claim that models are explanatory if they represent their target systems to some degree of accuracy; in other words, they try to determine the conditions under which idealizations can be made (...)
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   7 citations  
  3.  16
    On the Presumed Superiority of Analytical Solutions Over Numerical Methods.Vincent Ardourel & Julie Jebeile - 2017 - European Journal for Philosophy of Science 7 (2):201-220.
    An important task in mathematical sciences is to make quantitative predictions, which is often done via the solution of differential equations. In this paper, we investigate why, to perform this task, scientists sometimes choose to use numerical methods instead of analytical solutions. Via several examples, we argue that the choice for numerical methods can be explained by the fact that, while making quantitative predictions seems at first glance to be facilitated by analytical solutions, this is actually often much easier with (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   5 citations  
  4.  26
    Understanding Climate Change with Statistical Downscaling and Machine Learning.Julie Jebeile, Vincent Lam & Tim Räz - 2020 - Synthese (1-2):1-21.
    Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based ‘black boxes’ that may not provide any understanding. However, in many ways, understanding seems indispensable to appropriately evaluate climate models and to build confidence in climate projections. Relying on two case studies, we compare how machine learning and standard statistical techniques affect our ability to understand the climate system. For that purpose, we put five (...)
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  5.  30
    Values and Objectivity in the Intergovernmental Panel on Climate Change.Julie Jebeile - 2020 - Social Epistemology 34 (5):453-468.
    The assessments issued by the Intergovernmental Panel on Climate Change (IPCC) aim to provide policy-makers with an objective source of information about the various causes of climate change, the projected consequences for the environment and human affairs, and the options for adaptation and mitigation. But what, in this context, is meant by ‘objective’? In practice, in an effort to address internal and external criticisms, the IPCC has regularly revised its methodological procedures; some of these procedures seem to meet the requirements (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  6.  14
    Multi-Model Ensembles in Climate Science: Mathematical Structures and Expert Judgements.Julie Jebeile & Michel Crucifix - 2020 - Studies in History and Philosophy of Science Part A 83:44-52.
    Projections of future climate change cannot rely on a single model. It has become common to rely on multiple simulations generated by Multi-Model Ensembles (MMEs), especially to quantify the uncertainty about what would constitute an adequate model structure. But, as Parker points out (2018), one of the remaining philosophically interesting questions is: “How can ensemble studies be designed so that they probe uncertainty in desired ways?” This paper offers two interpretations of what General Circulation Models (GCMs) are and how MMEs (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   2 citations  
  7.  38
    Empirical Agreement in Model Validation.Julie Jebeile & Anouk Barberousse - 2016 - Studies in History and Philosophy of Science Part A 56:168-174.
    Empirical agreement is often used as an important criterion when assessing the validity of scientific models. However, it is by no means a sufficient criterion as a model can be so adjusted as to fit available data even though it is based on hypotheses whose plausibility is known to be questionable. Our aim in this paper is to investigate into the uses of empirical agreement within the process of model validation.
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   4 citations  
  8.  28
    Computer Simulation, Experiment, and Novelty.Julie Jebeile - 2017 - International Studies in the Philosophy of Science 31 (4):379-395.
    It is often said that computer simulations generate new knowledge about the empirical world in the same way experiments do. My aim is to make sense of such a claim. I first show that the similarities between computer simulations and experiments do not allow them to generate new knowledge but invite the simulationist to interact with simulations in an experimental manner. I contend that, nevertheless, computer simulations and experiments yield new knowledge under the same epistemic circumstances, independently of any features (...)
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  9.  19
    Les simulations sont-elles des expériences numériques?Julie Jebeile - 2016 - Dialogue 55 (1):59-86.
    Some philosophers see an analogy between simulation and experiment. But, once we acknowledge some similarities between computer simulations and experiments, can we conclude from them that simulations generate empirical knowledge, as experiments do? In this paper, I argue that the similarities between simulation and experiment give the scientist at most the illusion that she is conducting an experiment, but cannot seriously ground the analogy. However, it does not follow that experiments are always epistemologically superior to simulations. I analyze the cases (...)
    Direct download (3 more)  
    Translate
     
     
    Export citation  
     
    Bookmark   1 citation  
  10.  6
    Numerical instability and dynamical systems.Vincent Ardourel & Julie Jebeile - 2021 - European Journal for Philosophy of Science 11 (2):1-21.
    In philosophical studies regarding mathematical models of dynamical systems, instability due to sensitive dependence on initial conditions, on the one side, and instability due to sensitive dependence on model structure, on the other, have by now been extensively discussed. Yet there is a third kind of instability, which by contrast has thus far been rather overlooked, that is also a challenge for model predictions about dynamical systems. This is the numerical instability due to the employment of numerical methods involving a (...)
    Direct download (3 more)  
    Translate
     
     
    Export citation  
     
    Bookmark  
  11.  7
    How Do the Validations of Simulations and Experiments Compare?Anouk Barberousse & Julie Jebeile - 2019 - In Claus Beisbart & Nicole J. Saam (eds.), Computer Simulation Validation - Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Springer. pp. 925-942.
    Whereas experiments and computer simulations seem very different at first view because the former, but not the latter, involve interactions with material properties, we argue that this difference is not so important with respect to validation, as far as epistemologyEpistemology is concerned. Major differences remain nevertheless from the methodological point of view. We present and defend this distinction between epistemology and methodology. We illustrate this distinction and related claims by comparing how experiments and simulations are validated in evolutionary studies, a (...)
    Direct download  
     
    Export citation  
     
    Bookmark  
  12.  12
    Expert Reports by Large Multidisciplinary Groups: The Case of the International Panel on Climate Change.Isabelle Drouet, Daniel Andler, Anouk Barberousse & Julie Jebeile - 2021 - Synthese.
    Recent years have seen a notable increase in the production of scientific expertise by large multidisciplinary groups. The issue we address is how reports may be written by such groups in spite of their size and of formidable obstacles: complexity of subject matter, uncertainty, and scientific disagreement. Our focus is on the International Panel on Climate Change, unquestionably the best-known case of such collective scientific expertise. What we show is that the organization of work within the IPCC aims to make (...)
    Direct download (4 more)  
     
    Export citation  
     
    Bookmark  
  13.  18
    Collaborative Practice, Epistemic Dependence and Opacity: The Case of Space Telescope Data Processing.Julie Jebeile - 2018 - Philosophia Scientiae 22:59-78.
    Wagenknecht a récemment introduit une distinction conceptuelle entre dépendance épistémique translucide et dépendance épistémique opaque, dans le but de mieux rendre compte de la diversité des relations de dépendance épistémique au sein des pratiques collaboratives de recherche. Dans la continuité de son travail, mon but est d’expliciter les différents types d’expertise requis lorsque sont employés instruments et ordinateurs dans la production de connaissance, et d’identifier des sources potentielles d’opacité. Mon analyse s’appuie sur un cas contemporain de création de connaissance scientifique, (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  14. Idealizations in Empirical Modeling.Julie Jebeile - 2017 - In Martin Carrier & Johannes Lenhard (eds.), Mathematics as a Tool. Tracing New Roles of Mathematics in the Sciences. Springer Verlag.
    In empirical modeling, mathematics has an important utility in transforming descriptive representations of target system into calculation devices, thus creating useful scientific models. The transformation may be considered as the action of tools. In this paper, I assume that model idealizations could be such tools. I then examine whether these idealizations have characteristic properties of tools, i.e., whether they are being adapted to the objects to which they are applied, and whether they are to some extent generic.
    Direct download  
     
    Export citation  
     
    Bookmark  
  15.  10
    Model spread and progress in climate modelling.Julie Jebeile & Anouk Barberousse - 2021 - European Journal for Philosophy of Science 11 (3):1-19.
    Convergence of model projections is often considered by climate scientists to be an important objective in so far as it may indicate the robustness of the models’ core hypotheses. Consequently, the range of climate projections from a multi-model ensemble, called “model spread”, is often expected to reduce as climate research moves forward. However, the successive Assessment Reports of the Intergovernmental Panel on Climate Change indicate no reduction in model spread, whereas it is indisputable that climate science has made improvements in (...)
    Direct download (2 more)  
    Translate
     
     
    Export citation  
     
    Bookmark  
  16.  5
    The Kac Ring or the Art of Making Idealisations.Julie Jebeile - 2020 - Foundations of Physics 50 (10):1152-1170.
    In 1959, mathematician Mark Kac introduced a model, called the Kac ring, in order to elucidate the classical solution of Boltzmann to the problem of macroscopic irreversibility. However, the model is far from being a realistic representation of something. How can it be of any help here? In philosophy of science, it is often argued that models can provide explanations of the phenomenon they are said to approximate, in virtue of the truth they contain, and in spite of the idealisations (...)
    Direct download (2 more)  
    Translate
     
     
    Export citation  
     
    Bookmark  
  17.  8
    The Nuclear Power Plant: Our New “Tower of Babel”?Julie Jebeile - 2014 - In Johanna Jauernig & Christoph Lütge (eds.), Business Ethics and Risk Management. Springer. pp. 129--143.
    On July 5, 2012 the Investigation Committee on the Accident at the Fukushima Nuclear Power Stations of the Tokyo Electric Power Company (TEPCO) issued a final, damning report. Its conclusions show that the human group – constituted by the employees of TEPCO and the control organism – had partial and imperfect epistemic control on the nuclear power plant and its environment. They also testify to a group inertia in decision-making and action. Could it have been otherwise? Is not a collective (...)
    Direct download  
     
    Export citation  
     
    Bookmark  
  18.  15
    Verification and Validation of Simulations Against Holism.Julie Jebeile & Vincent Ardourel - 2019 - Minds and Machines 29 (1):149-168.
    It has been argued that the Duhem problem is renewed with computational models since model assumptions having a representational aim and computational assumptions cannot be tested in isolation. In particular, while the Verification and Validation methodology is supposed to prevent such holism, Winsberg argues that verification and validation cannot be separated in practice. Morrison replies that Winsberg overstates the entanglement between the steps. The paper aims at arbitrating these two positions, by stressing their respective validity in relation to domains of (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  19.  56
    Value Management and Model Pluralism in Climate Science.Julie Jebeile & Michel Crucifix - 2021 - Studies in History and Philosophy of Science Part A 88 (August 2021):120-127.
    Non-epistemic values pervade climate modelling, as is now well documented and widely discussed in the philosophy of climate science. Recently, Parker and Winsberg have drawn attention to what can be termed “epistemic inequality”: this is the risk that climate models might more accurately represent the future climates of the geographical regions prioritised by the values of the modellers. In this paper, we promote value management as a way of overcoming epistemic inequality. We argue that value management can be seriously considered (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark