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  1. Tools for Evaluating the Consequences of Prior Knowledge, but No Experiments. On the Role of Computer Simulations in Science.Eckhart Arnold - manuscript
    There is an ongoing debate on whether or to what degree computer simulations can be likened to experiments. Many philosophers are sceptical whether a strict separation between the two categories is possible and deny that the materiality of experiments makes a difference (Morrison 2009, Parker 2009, Winsberg 2010). Some also like to describe computer simulations as a “third way” between experimental and theoretical research (Rohrlich 1990, Axelrod 2003, Kueppers/Lenhard 2005). In this article I defend the view that computer simulations are (...)
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  2. 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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  3. Degrees of Epistemic Opacity.Iñaki San Pedro - manuscript
    The paper analyses in some depth the distinction by Paul Humphreys between "epistemic opacity" —which I refer to as "weak epistemic opacity" here— and "essential epistemic opacity", and defends the idea that epistemic opacity in general can be made sense as coming in degrees. The idea of degrees of epistemic opacity is then exploited to show, in the context of computer simulations, the tight relation between the concept of epistemic opacity and actual scientific (modelling and simulation) practices. As a consequence, (...)
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  4. "Y'all Are Just Too Sensitive": A Computational Ethics Approach to Understanding How Prejudice Against Marginalized Communities Becomes Epistemic Belief.Johannah Sprinz - manuscript
    Members of marginalized communities are often accused of being "too sensitive" when subjected to supposedly harmless acts of microaggression. This paper explores a simulated society consisting of marginalized and non-marginalized agents who interact and may, based on their individually held convictions, commit acts of microaggressions. Agents witnessing a microaggression might condone, ignore or condemn such microaggressions, thus potentially influencing a perpetrator's conviction. A prototype model has been implemented in NetLogo, and possible applications are briefly discussed.
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  5. Why Trust a Simulation? Models, Parameters, and Robustness in Simulation-Infected Experiments.Florian J. Boge - forthcoming - British Journal for the Philosophy of Science.
    Computer simulations are nowadays often directly involved in the generation of experimental results. Given this dependency of experiments on computer simulations, that of simulations on models, and that of the models on free parameters, how do researchers establish trust in their experimental results? Using high-energy physics (HEP) as a case study, I will identify three different types of robustness that I call conceptual, methodological, and parametric robustness, and show how they can sanction this trust. However, as I will also show, (...)
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  6. Computer Simulations, Machine Learning and the Laplacean Demon: Opacity in the Case of High Energy Physics.Florian J. Boge & Paul Grünke - forthcoming - In Andreas Kaminski, Michael Resch & Petra Gehring (eds.), The Science and Art of Simulation II.
    In this paper, we pursue three general aims: (I) We will define a notion of fundamental opacity and ask whether it can be found in High Energy Physics (HEP), given the involvement of machine learning (ML) and computer simulations (CS) therein. (II) We identify two kinds of non-fundamental, contingent opacity associated with CS and ML in HEP respectively, and ask whether, and if so how, they may be overcome. (III) We address the question of whether any kind of opacity, contingent (...)
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  7. Towards a Taxonomy of the Model-Ladenness of Data.Alisa Bokulich - forthcoming - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association.
    Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include data conversion, data correction, data interpolation, (...)
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  8. Combining Qualitative and Quantitative Techniques in the Simulation of Chemical Reaction Mechanisms.Michael Eisenberg - forthcoming - Ai and Simulation: Theory and Applications (Simulation Series Vol. 22, No. 3.). Society for Computer Simulation, San Diego. Ca.
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  9. Antisocial Modelling.Georgi Gardiner - forthcoming - In Alfano Mark, Jeroen De Ridder & Colin Klein (eds.), Social Virtue Epistemology.
    This essay replies to Michael Morreau and Erik J. Olsson’s ‘Learning from Ranters: The Effect of Information Resistance on the Epistemic Quality of Social Network Deliberation’. Morreau and Olsson use simulations to suggest that false ranters—agents who do not update their beliefs and only ever assert false claims—do not diminish the epistemic value of deliberation for other agents and can even be epistemically valuable. They argue conclude that “Our study suggests that including [false] ranters has little or no negative effect (...)
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  10. Computer Simulations in Science.Eric Winsberg - forthcoming - Stanford Encyclopedia of Philosophy.
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  11. Computer Simulations as Scientific Instruments.Ramón Alvarado - 2022 - Foundations of Science 27 (3):1183-1205.
    Computer simulations have conventionally been understood to be either extensions of formal methods such as mathematical models or as special cases of empirical practices such as experiments. Here, I argue that computer simulations are best understood as instruments. Understanding them as such can better elucidate their actual role as well as their potential epistemic standing in relation to science and other scientific methods, practices and devices.
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  12. Diversity, Trust, and Conformity: A Simulation Study.Sina Fazelpour & Daniel Steel - 2022 - Philosophy of Science 89 (2):209-231.
    Previous simulation models have found positive effects of cognitive diversity on group performance, but have not explored effects of diversity in demographics (e.g., gender, ethnicity). In this paper, we present an agent-based model that captures two empirically supported hypotheses about how demographic diversity can improve group performance. The results of our simulations suggest that, even when social identities are not associated with distinctive task-related cognitive resources, demographic diversity can, in certain circumstances, benefit collective performance by counteracting two types of conformity (...)
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  13. Designing Grant-Review Panels for Better Funding Decisions: Lessons From an Empirically Calibrated Simulation Model.Thomas Feliciani, Michael Morreau, Junwen Luo, Pablo Lucas & Kalpana Shankar - 2022 - Research Policy 51 (4):1-11.
    Objectives To explore how factors relating to grades and grading affect the correctness of choices that grant-review panels make among submitted proposals. To identify interventions in panel design that may be expected to increase the correctness of choices. -/- Method Experimentation with an empirically-calibrated computer simulation model of panel review. Model parameters are set in accordance with procedures at a national science funding agency. Correctness of choices among research proposals is operationalized as agreement with the choices of an elite panel. (...)
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  14. Analogue Quantum Simulation: A New Instrument for Scientific Understanding.Dominik Hangleiter, Jacques Carolan & Karim Thebault - 2022 - Cham: Springer.
    This book presents fresh insights into analogue quantum simulation. It argues that these simulations are a new instrument of science. They require a bespoke philosophical analysis, sensitive to both the similarities to and the differences with conventional scientific practices such as analogical argument, experimentation, and classical simulation. -/- The analysis situates the various forms of analogue quantum simulation on the methodological map of modern science. In doing so, it clarifies the functions that analogue quantum simulation serves in scientific practice. To (...)
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  15. Simulating Marx: Herbert A. Simon's Cognitivist Approach to Dialectical Materialism.Enrico Petracca - 2022 - History of the Human Sciences 35 (2):101-125.
    Starting in the 1950s, computer programs for simulating cognitive processes and intelligent behaviour were the hallmark of Good Old-Fashioned Artificial Intelligence and ‘cognitivist’ cognitive science. This article examines a somewhat neglected case of simulation pursued by one of the founding fathers of simulation methodology, Herbert A. Simon. In the 1970s and 1980s, Simon had repeated contacts with Marxist countries and scientists, in the context of which he advanced the idea that cognitivism could be used as a framework for simulating dialectical (...)
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  16. Théorie des modèles, de la simulation et représentation scientifique chez Mario Bunge.Jean Robillard - 2022 - Mεtascience: Discours Général Scientifique 2:45-73.
    On entend généralement par « théorie des modèles » autant la métamathématique (ou sémantique formelle) que la sémantique des modèles des sciences non formelles. Cet article a pour objet la théorie des modèles scientifiques que Mario Bunge a développée dans Method, Models and Matter (1973). J’y analyse l’intégration théorique qu’opère Bunge des sciences formelles et des sciences expérimentales ou observationnelles, laquelle prend appui sur sa philosophie des sciences. Je la compare sommairement à la théorie des modèles de Gilles-Gaston Granger dans (...)
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  17. Using Simulation in the Assessment of Voting Procedures: An Epistemic Instrumental Approach.Marc Jiménez Rolland, Julio César Macías-Ponce & Luis Fernando Martínez-Álvarez - 2022 - Simulation: Transactions of the Society for Modeling and Simulation International 98 (2):127-144.
    In this paper, we argue that computer simulations can provide valuable insights into the performance of voting methods on different collective decision problems. This could improve institutional design, even when there is no general theoretical result to support the optimality of a voting method. To support our claim, we first describe a decision problem that has not received much theoretical attention in the literature. We outline different voting methods to address that collective decision problem. Under certain criteria of assessment akin (...)
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  18. 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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  19. Opacity Thought Through: On the Intransparency of Computer Simulations.Claus Beisbart - 2021 - Synthese 199 (3-4):11643-11666.
    Computer simulations are often claimed to be opaque and thus to lack transparency. But what exactly is the opacity of simulations? This paper aims to answer that question by proposing an explication of opacity. Such an explication is needed, I argue, because the pioneering definition of opacity by P. Humphreys and a recent elaboration by Durán and Formanek are too narrow. While it is true that simulations are opaque in that they include too many computations and thus cannot be checked (...)
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  20. Correction To: Polycratic Hierarchies and Networks: What Simulation-Modeling at the LHC Can Teach Us About the Epistemology of Simulation.Florian J. Boge & Christian Zeitnitz - 2021 - Synthese 199 (3):11767-11768.
    With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 26 May 2021 to ©The Author(s) 2021 and the article is forthwith distributed under a Creative Commons Attribution.
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  21. Proof of Concept Research.Steve Elliott - 2021 - Philosophy of Science 88 (2):258-280.
    Researchers often pursue proof of concept research, but criteria for evaluating such research remain poorly specified. This article proposes a general framework for proof of concept research that k...
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  22. Combining Causal Bayes Nets and Cellular Automata: A Hybrid Modelling Approach to Mechanisms.Alexander Gebharter & Daniel Koch - 2021 - British Journal for the Philosophy of Science 72 (3):839-864.
    Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser (2016) pointed out—they have problems with capturing relevant spatial and structural information. In this paper we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes with all (...)
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  23. Exploring Minds: Modes of Modeling and Simulation in Artificial Intelligence.Hajo Greif - 2021 - Perspectives on Science 29 (4):409-435.
    The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the (...)
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  24. Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions.Casey Helgeson, Vivek Srikrishnan, Klaus Keller & Nancy Tuana - 2021 - Philosophy of Science 88 (2):213-233.
    For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many ti...
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  25. Epistemic Issues in Computational Reproducibility: Software as the Elephant in the Room.Alexandre Hocquet & Frédéric Wieber - 2021 - European Journal for Philosophy of Science 11 (2):1-20.
    Computational reproducibility possesses its own dynamics and narratives of crisis. Alongside the difficulties of computing as an ubiquitous yet complex scientific activity, computational reproducibility suffers from a naive expectancy of total reproducibility and a moral imperative to embrace the principles of free software as a non-negotiable epistemic virtue. We argue that the epistemic issues at stake in actual practices of computational reproducibility are best unveiled by focusing on software as a pivotal concept, one that is surprisingly often overlooked in accounts (...)
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  26. The Computational Philosophy: Simulation as a Core Philosophical Method.Conor Mayo-Wilson & Kevin J. S. Zollman - 2021 - Synthese 199 (1-2):3647-3673.
    Modeling and computer simulations, we claim, should be considered core philosophical methods. More precisely, we will defend two theses. First, philosophers should use simulations for many of the same reasons we currently use thought experiments. In fact, simulations are superior to thought experiments in achieving some philosophical goals. Second, devising and coding computational models instill good philosophical habits of mind. Throughout the paper, we respond to the often implicit objection that computer modeling is “not philosophical.”.
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  27. Série Investigações Filosóficas: Textos Selecionados de Filosofia da Ciência II [Philosophical Investigation Series: Selected Texts on Philosophy of Science II].Luana Poliseli (ed.) - 2021 - Pelotas: Editora da Universidade Federal de Pelotas.
    A Série Investigação Filosófica, uma iniciativa do Núcleo de Ensino e Pesquisa em Filosofia do Departamento de Filosofia da UFPel e do Grupo de Pesquisa Investigação Filosófica do Departamento de Filosofia da UNIFAP, sob o selo editorial do NEPFil online e da Editora da Universidade Federal de Pelotas, com auxílio financeiro da John Templeton Foundation, tem por objetivo precípuo a publicação da tradução para a língua portuguesa de textos selecionados a partir de diversas plataformas internacionalmente reconhecidas, tal como a Stanford (...)
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  28. Scenarios as Tools of the Scientific Imagination: The Case of Climate Projections.Michael Poznic & Rafaela Hillerbrand - 2021 - Perspectives on Science 29 (1):36-61.
    Climatologists have recently introduced a distinction between projections as scenario-based model results on the one hand and predictions on the other hand. The interpretation and usage of both terms is, however, not univocal. It is stated that the ambiguities of the interpretations may cause problems in the communication of climate science within the scientific community and to the public realm. This paper suggests an account of scenarios as props in games of make-belive. With this account, we explain the difference between (...)
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  29. The Quest for System-Theoretical Medicine in the COVID-19 Era.Felix Tretter, Olaf Wolkenhauer, Michael Meyer-Hermann, Johannes W. Dietrich, Sara Green, James Marcum & Wolfram Weckwerth - 2021 - Frontiers in Medicine 8:640974.
    Precision medicine and molecular systems medicine (MSM) are highly utilized and successful approaches to improve understanding, diagnosis, and treatment of many diseases from bench-to-bedside. Especially in the COVID-19 pandemic, molecular techniques and biotechnological innovation have proven to be of utmost importance for rapid developments in disease diagnostics and treatment, including DNA and RNA sequencing technology, treatment with drugs and natural products and vaccine development. The COVID-19 crisis, however, has also demonstrated the need for systemic thinking and transdisciplinarity and the limits (...)
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  30. The Epistemic Duties of Philosophers: An Addendum.Philippe van Basshuysen & Lucie White - 2021 - Kennedy Institute of Ethics Journal 31 (4):447-451.
    We were slightly concerned, upon having read Eric Winsberg, Jason Brennan and Chris Surprenant’s reply to our paper “Were Lockdowns Justified? A Return to the Facts and Evidence”, that they may have fundamentally misunderstood the nature of our argument, so we issue the following clarification, along with a comment on our motivations for writing such a piece, for the interested reader.
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  31. Learning Through Simulation.Sara Aronowitz & Tania Lombrozo - 2020 - Philosophers' Imprint 20.
    Mental simulation — such as imagining tilting a glass to figure out the angle at which water would spill — can be a way of coming to know the answer to an internally or externally posed query. Is this form of learning a species of inference or a form of observation? We argue that it is neither: learning through simulation is a genuinely distinct form of learning. On our account, simulation can provide knowledge of the answer to a query even (...)
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  32. How to Infer Explanations From Computer Simulations.Florian J. Boge - 2020 - Studies in History and Philosophy of Science Part A 82:25-33.
    Computer simulations are involved in numerous branches of modern science, and science would not be the same without them. Yet the question of how they can explain real-world processes remains an issue of considerable debate. In this context, a range of authors have highlighted the inferences back to the world that computer simulations allow us to draw. I will first characterize the precise relation between computer and target of a simulation that allows us to draw such inferences. I then argue (...)
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  33. Polycratic Hierarchies and Networks: What Simulation-Modeling at the LHC Can Teach Us About the Epistemology of Simulation.Florian J. Boge & Christian Zeitnitz - 2020 - Synthese 199 (1-2):445-480.
    Large scale experiments at CERN’s Large Hadron Collider rely heavily on computer simulations, a fact that has recently caught philosophers’ attention. CSs obviously require appropriate modeling, and it is a common assumption among philosophers that the relevant models can be ordered into hierarchical structures. Focusing on LHC’s ATLAS experiment, we will establish three central results here: with some distinct modifications, individual components of ATLAS’ overall simulation infrastructure can be ordered into hierarchical structures. Hence, to a good degree of approximation, hierarchical (...)
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  34. Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.
    Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...
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  35. Calculating surprises: a review for a philosophy of computer simulations: Johannes Lenhard: Calculated Surprises. A philosophy of computer simulations. New York: Oxford University Press, 2019, 256pp, 64,12 €.Juan M. Durán - 2020 - Metascience 29 (2):337-340.
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  36. What Can Bouncing Oil Droplets Tell Us About Quantum Mechanics?Peter W. Evans & Karim P. Y. Thébault - 2020 - European Journal for Philosophy of Science 10 (3):1-32.
    A recent series of experiments have demonstrated that a classical fluid mechanical system, constituted by an oil droplet bouncing on a vibrating fluid surface, can be induced to display a number of behaviours previously considered to be distinctly quantum. To explain this correspondence it has been suggested that the fluid mechanical system provides a single-particle classical model of de Broglie’s idiosyncratic ‘double solution’ pilot wave theory of quantum mechanics. In this paper we assess the epistemic function of the bouncing oil (...)
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  37. “ « And the rod starts to swing ». Morphogènes, instabilités et organismes imaginaires dans l’approche de Turing à la biologie » ”.Sara Franceschelli - 2020 - Intellectica 72:191-214.
  38. Philosophical Perspectives on Earth System Modeling: Truth, Adequacy and Understanding.G. Gramelsberger, J. Lenhard & Wendy Parker - 2020 - Journal of Advances in Modeling Earth Systems 12 (1):e2019MS001720.
    We explore three questions about Earth system modeling that are of both scientific and philosophical interest: What kind of understanding can be gained via complex Earth system models? How can the limits of understanding be bypassed or managed? How should the task of evaluating Earth system models be conceptualized?
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  39. The Termination Risks of Simulation Science.Preston Greene - 2020 - Erkenntnis 85 (2):489-509.
    Historically, the hypothesis that our world is a computer simulation has struck many as just another improbable-but-possible “skeptical hypothesis” about the nature of reality. Recently, however, the simulation hypothesis has received significant attention from philosophers, physicists, and the popular press. This is due to the discovery of an epistemic dependency: If we believe that our civilization will one day run many simulations concerning its ancestry, then we should believe that we are probably in an ancestor simulation right now. This essay (...)
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  40. On Robustness in Cosmological Simulations.Marie Gueguen - 2020 - Philosophy of Science 87 (5):1197-1208.
    The Cold Dark Matter model faces many controversies at small scales, as simulations fail to reproduce the observed properties of dark matter halos. Since rival DM models differ on their predic...
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  41. Thought Experiments and the Scientific Imagination.Alice Murphy - 2020 - Dissertation, University of Leeds
    Thought experiments (TEs) are important tools in science, used to both undermine and support theories, and communicate and explain complex phenomena. Their interest within philosophy of science has been dominated by a narrow question: How do TEs increase knowledge? My aim is to push beyond this to consider their broader value in scientific practice. I do this through an investigation into the scientific imagination. Part one explores questions regarding TEs as “experiments in the imagination” via a debate concerning the epistemic (...)
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  42. Model Evaluation: An Adequacy-for-Purpose View.Wendy S. Parker - 2020 - Philosophy of Science 87 (3):457-477.
    According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers...
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  43. Evidence and Knowledge From Computer Simulation.Wendy S. Parker - 2020 - Erkenntnis 87 (4):1521-1538.
    Can computer simulation results be evidence for hypotheses about real-world systems and phenomena? If so, what sort of evidence? Can we gain genuinely new knowledge of the world via simulation? I argue that evidence from computer simulation is aptly characterized as higher-order evidence: it is evidence that other evidence regarding a hypothesis about the world has been collected. Insofar as particular epistemic agents do not have this other evidence, it is possible that they will gain genuinely new knowledge of the (...)
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  44. Expanding Theory Testing in General Relativity: LIGO and Parametrized Theories.Lydia Patton - 2020 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 69:142-53.
    The multiple detections of gravitational waves by LIGO (the Laser Interferometer Gravitational-Wave Observatory), operated by Caltech and MIT, have been acclaimed as confirming Einstein's prediction, a century ago, that gravitational waves propagating as ripples in spacetime would be detected. Yunes and Pretorius (2009) investigate whether LIGO's template-based searches encode fundamental assumptions, especially the assumption that the background theory of general relativity is an accurate description of the phenomena detected in the search. They construct the parametrized post-Einsteinian (ppE) framework in response, (...)
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  45. Models, Parameterization, and Software: Epistemic Opacity in Computational Chemistry.Frédéric Wieber & Alexandre Hocquet - 2020 - Perspectives on Science 28 (5):610-629.
    . Computational chemistry grew in a new era of “desktop modeling,” which coincided with a growing demand for modeling software, especially from the pharmaceutical industry. Parameterization of models in computational chemistry is an arduous enterprise, and we argue that this activity leads, in this specific context, to tensions among scientists regarding the epistemic opacity transparency of parameterized methods and the software implementing them. We relate one flame war from the Computational Chemistry mailing List in order to assess in detail the (...)
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  46. Qualitative Models in Computational Simulative Sciences: Representation, Confirmation, Experimentation.Nicola Angius - 2019 - Minds and Machines 29 (3):397-416.
    The Epistemology Of Computer Simulation has developed as an epistemological and methodological analysis of simulative sciences using quantitative computational models to represent and predict empirical phenomena of interest. In this paper, Executable Cell Biology and Agent-Based Modelling are examined to show how one may take advantage of qualitative computational models to evaluate reachability properties of reactive systems. In contrast to the thesis, advanced by EOCS, that computational models are not adequate representations of the simulated empirical systems, it is shown how (...)
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  47. Validation of Computer Simulations From a Kuhnian Perspective.Eckhart Arnold - 2019 - In Claus Beisbart & Nicole J. Saam (eds.), Computer Simulation Validation - Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Heidelberg, Deutschland: Springer. pp. 203-224.
    While Thomas Kuhn's theory of scientific revolutions does not specifically deal with validation, the validation of simulations can be related in various ways to Kuhn's theory: 1) Computer simulations are sometimes depicted as located between experiments and theoretical reasoning, thus potentially blurring the line between theory and empirical research. Does this require a new kind of research logic that is different from the classical paradigm which clearly distinguishes between theory and empirical observation? I argue that this is not the case. (...)
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  48. What is a Computer Simulation and What Does This Mean for Simulation Validation?Claus Beisbart - 2019 - In Claus Beisbart & Nicole J. Saam (eds.), Computer Simulation Validation - Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Springer. pp. 901-923.
    Many questions about the fundamentals of some area take the form “What is …?” It does not come as a surprise then that, at the dawn of Western philosophy, Socrates asked the questions of what piety, courage, and justice are. Nor is it a wonder that the philosophical preoccupation with computer simulations centered, among other things, about the question of what computer simulations are. Very often, this question has been answered by stating that computer simulation is a species of a (...)
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  49. What is Validation of Computer Simulations? Toward a Clarification of the Concept of Validation and of Related Notions.Claus Beisbart - 2019 - In Claus Beisbart & Nicole J. Saam (eds.), Computer Simulation Validation - Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Cham, Schweiz: Springer. pp. 35-67.
    This chapter clarifies the concept of validation of computer simulations by comparing various definitions that have been proposed for the notion. While the definitions agree in taking validation to be an evaluationEvaluation, they differ on the following questions: What exactly is evaluated—results from a computer simulation, a model, a computer codeCode? What are the standardsStandard of evaluationEvaluation––truthTruth, accuracyAccuracy, and credibilityCredibility or also something else? What type of verdict does validation lead to––that the simulation is such and such good, or that (...)
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  50. Should Validation and Verification Be Separated Strictly?Claus Beisbart - 2019 - In Claus Beisbart & Nicole J. Saam (eds.), Computer Simulation Validation - Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Springer. pp. 1005-1028.
    Verification and validation are methods with which computer simulations are tested. While many practitioners draw a clear line between verification and validation and demand that the former precedes the latter, some philosophers have suggested that the distinction has been over-exaggerated. This chapter clarifies the relationship between verification and validation. Regarding the latter, validation of the conceptual and of the computational modelComputational model are distinguished. I argue that, as a method, verification is clearly different from validation of either of the models. (...)
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