• JEBEILE, Julie. Modéliser pour comprendre. Avant et après le tournant numérique dans les sciences, from the thesis dissertation, forthcoming, CNRS éditions, collection ALPHA.

Articles in international peer-reviewed journals and collective volumes

  • BARBEROUSSE, Anouk and JEBEILE, Julie. How do the validations of simulations and experiments compare?, in Beisbart, C. and Saam, N. J. (eds.) Computer Simulation Validation – Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives, Springer series: Simulation Foundations, Methods and Applications, 2019
  • JEBEILE, Julie. Computer simulation, experiment, and novelty, International Studies in
 the Philosophy of Science, 2018, accepted and forthcoming     (abstract)
    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 they may share.
  • JEBEILE, Julie. Collaborative scientific practice, epistemic dependence and opacity: the case of space telescope data processing, Philosophia Scientiae, Kimé, special issue “Études de cas en épistémologie sociale. Argumentation, délibération publique et pratiques collaboratives”, edited by Pierre Willaime and Olivier Ouzilou, 2018, no.22(2), pp. 59–78, 20 p.
    Wagenknecht recently introduced a conceptual (yet non-exhausting) distinction between translucent and opaque epistemic dependence in order to better describe the diversity of the relations of epistemic dependence experienced by scientists in collaborative research practice. In line with her analysis, I will further elaborate the different kinds of expertise that are specific to instrument- and computer-assisted practices, and the potential sources of opacity. For that, I focus on a contemporary case of scientific knowledge creation, i.e., space telescope data processing.
  • JEBEILE, Julie. Explaining with simulations. Why visual representations matter, Perspectives on Science, 2018, vol. 26, no. 2, March-April, pp. 213-238, 26 p. preprint    
    Computer simulations are often expected to provide explanations about target phenomena. However there is a gap between the simulation outputs and the underlying model, which prevents users finding the relevant explanatory components within the model. I contend that visual representations which adequately display the simulation outputs can nevertheless be used to get explanations. In order to do so, I elaborate on the way graphs and pictures can help one to explain the behavior of a flow past a cylinder. I then specify the reasons that make more generally visual representations particularly suitable for explanatory tasks in a computer-assisted context.
  • ARDOUREL, Vincent and JEBEILE, Julie. On the presumed superiority of analytical solutions over numerical methods, European Journal for the Philosophy of Science, 2017, issue 7, pp. 201-220, 20p. preprint   
    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 with analytical solutions, this is actually oftenmuch easier with numerical methods. Thus we challenge the widely presumed superiority of analytical solutions over numerical methods.
  • JEBEILE, Julie. Idealizations in empirical modeling, in Lenhard, J. and Carrier, M.  (eds.)  Mathematics as a tool, Boston Studies in the Philosophy of Science, 2017, pp. 213-232, 20p. preprint     (abstract)
    In empirical modeling, mathematics has an important utility in transforming descriptive representations of target system(s) into calculation , thus creating useful . 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 the usual expected properties of tools, i.e. being adapted to the objects on which they apply, and being to some extent generic.
  • JEBEILE, Julie. Centrale nucléaire : notre nouvelle Tour de Babel?, in Guay, A. and Ruphy, S. (eds.) Science, philosophie société, IVe congrès de la SPS, Presses universitaires de France-Comté, collection Sciences : concepts et problèmes, 2017, pp. 143-158, 16p.
  • JEBEILE, Julie. Les simulations sont-elles des expériences numériques ?, Dialogue: Canadian Philosophical Review/Revue canadienne de philosophie, volume 55, issue 01, 2016, pp. 59-86, 28p.    
    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 when simulations and experiments equally yield new empirical knowledge.
  • JEBEILE, Julie and BARBEROUSSE, Anouk. Empirical agreement in model validation, Studies in History and Philosophy of ScienceStudies in History and Philosophy of Science Part A, Volume 56, April 2016, pp 168–174, 7p. preprint    
    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.
  • JEBEILE, Julie and KENNEDY, Ashley. Explaining with models: the role of idealizations, International Studies in the Philosophy of Science, 2015, volume 29, number 4, pp. 383–392, 10p. preprint   
    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 behaviors 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 without jeopardizing the representational function of models. In this paper we first outline several forms of this representationalist view. We then argue that this view, in each of these forms, omits an important role of idealizations: that of facilitating the identification of the explanatory components within a model. Via examination of a case study from contemporary astrophysics, we show that one way in which idealizations can do this is by creating a comparison case which serves to highlight the relevant features of the target system. 
  • JEBEILE, Julie. Nuclear Power Plant: our New Tower of Babel? in C. Luetge and J. Jauernig (eds.), Business Ethics and Risk Management, Ethical Economy,Volume 43, Springer Science + Business Media Dordrecht, 2014, pp 129-143, 15p. preprint    
    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 decisionmaking and action. Could it have been otherwise? Is not a collective of human beings, even prepared in the best way against nuclear risk, de facto prone to epistemic imperfection and a kind of inertia? In this article, I focus on the group of engineers who, in research and design offices, design nuclear power plants and model possible nuclear accidents in order to calculate the probability of their occurrence, predict their consequences, and determine the appropriate countermeasures against them. I argue that this group is prone to epistemic imperfection, even when it is highly prepared for adverse nuclear events.
  • JEBEILE, Julie. Le tournant computationnel dans les sciences : la fin d’une philosophie de la connaissance, in M. Silberstein and F. Varenne (eds.) Modéliser & simuler. Epistémologies et pratiques de la modélisation et de la simulation, tome 1, Editions Matériologiques, 2013, pp.171-189, 19p.

Papers under review and work in progress

Several papers (six) are going through peer-reviewing process; other papers (three) are currently in progress.