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  1. A Verisimilitude Framework for Inductive Inference, with an Application to Phylogenetics.Vassend Olav Benjamin - unknown
    Bayesianism and likelihoodism are two of the most important frameworks philosophers of science use to analyse scientific methodology. However, both frameworks face a serious objection: much scientific inquiry takes place in highly idealized frameworks where all the hypotheses are known to be false. Yet, both Bayesianism and likelihoodism seem to be based on the assumption that the goal of scientific inquiry is always truth rather than closeness to the truth. Here, I argue in favor of a verisimilitude framework for inductive (...)
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  • The Evidence for the Accelerating Universe: Endorsement and Robust Consistency.Genco Guralp - 2020 - European Journal for Philosophy of Science 10 (2):1-52.
    The 2011 Nobel Prize in Physics was awarded to researchers from the Supernova Cosmology Project and the High-z Supernova Search Team for the discovery of the accelerating expansion of the universe. In this paper, I provide a historical analysis of the supernova cosmology evidence put forward by these teams for the accelerating universe, in terms of an iterative model of scientific progress developed by Hasok Chang in the context of his study of the development of measurement standards. I argue, using (...)
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  • 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 (...)
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  • Derivational Robustness and Indirect Confirmation.Aki Lehtinen - 2018 - Erkenntnis 83 (3):539-576.
    Derivational robustness may increase the degree to which various pieces of evidence indirectly confirm a robust result. There are two ways in which this increase may come about. First, if one can show that a result is robust, and that the various individual models used to derive it also have other confirmed results, these other results may indirectly confirm the robust result. Confirmation derives from the fact that data not known to bear on a result are shown to be relevant (...)
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  • Two Senses of Experimental Robustness: Result Robustness and Procedure Robustness.Koray Karaca - forthcoming - British Journal for the Philosophy of Science:000-000.
    In the philosophical literature concerning scientific experimentation, the notion of robustness has been solely discussed in relation to experimental results. In this paper, I propose a novel sense of experimental robustness that applies to experimental procedures. I call the foregoing sense of robustness procedure robustness and characterize it as the capacity of an experimental procedure to maintain its intended function invariant during the experimental process despite possible variations in its inputs. I argue that PR is a precondition for what I (...)
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  • Unification Beyond Justification: A Strategy for Theory Development.Molly Kao - 2017 - Synthese:1-16.
    This paper considers the importance of unification in the context of developing scientific theories. I argue that unifying hypotheses are not valuable simply because they are supported by multiple lines of evidence. Instead, they can be valuable because they guide experimental research in different domains in such a way that the results from those experiments inform the scope of the theory being developed. I support this characterization by appealing to the early development of quantum theory. I then draw some comparisons (...)
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  • The Volterra Principle Generalized.Tim Räz - 2017 - Philosophy of Science 84 (4):737-760.
    Michael Weisberg and Kenneth Reisman argue that the Volterra Principle can be derived from multiple predator-prey models and that, therefore, the Volterra Principle is a prime example for robustness analysis. In the current article, I give new results regarding the Volterra Principle, extending Weisberg’s and Reisman’s work, and I discuss the consequences of these results for robustness analysis. I argue that we do not end up with multiple, independent models but rather with one general model. I identify the kind of (...)
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  • When Expert Disagreement Supports the Consensus.Finnur Dellsén - 2018 - Australasian Journal of Philosophy 96 (1):142-156.
    It is often suggested that disagreement among scientific experts is a reason not to trust those experts, even about matters on which they are in agreement. In direct opposition to this view, I argue here that the very fact that there is disagreement among experts on a given issue provides a positive reason for non-experts to trust that the experts really are justified in their attitudes towards consensus theories. I show how this line of thought can be spelled out in (...)
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  • Epistemic values of quantity and variety of evidence in biological mechanism research.Yin Chung Au - 2021 - European Journal for Philosophy of Science 11 (2):1-22.
    This paper proposes an extended version of the interventionist account for causal inference in the practical context of biological mechanism research. This paper studies the details of biological mechanism researchers’ practices of assessing the evidential legitimacy of experimental data, arguing why quantity and variety are two important criteria for this assessment. Because of the nature of biological mechanism research, the epistemic values of these two criteria result from the independence both between the causation of data generation and the causation in (...)
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  • Variety of Evidence.Jürgen Landes - 2020 - Erkenntnis 85 (1):183-223.
    Varied evidence confirms more strongly than less varied evidence, ceteris paribus. This epistemological Variety of Evidence Thesis enjoys widespread intuitive support. We put forward a novel explication of one notion of varied evidence and the Variety of Evidence Thesis within Bayesian models of scientific inference by appealing to measures of entropy. Our explication of the Variety of Evidence Thesis holds in many of our models which also pronounce on disconfirmatory and discordant evidence. We argue that our models pronounce rightly. Against (...)
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  • Measurement Perspective, Process, and the Pandemic.Vadim Keyser & Hannah Howland - 2020 - European Journal for Philosophy of Science 11 (1):1-26.
    This discussion centers on two desiderata: the role of measurement in information-gathering and physical interaction in scientific practice. By taking inspiration from van Fraassen’s view, we present a methodological account of perspectival measurement that addresses empirical practice where there is complex intervention, disagreeing results, and limited theory. The specific aim of our account is to provide a methodological prescription for developing measurement processes in the context of limited theory. The account should be useful to philosophers of science, who are interested (...)
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  • The strategy of model building in climate science.Lachlan Douglas Walmsley - forthcoming - Synthese:1-21.
    In the 1960s, theoretical biologist Richard Levins criticised modellers in his own discipline of population biology for pursuing the “brute force” strategy of building hyper-realistic models. Instead of exclusively chasing complexity, Levins advocated for the use of multiple different kinds of complementary models, including much simpler ones. In this paper, I argue that the epistemic challenges Levins attributed to the brute force strategy still apply to state-of-the-art climate models today: they have big appetites for unattainable data, they are limited by (...)
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  • Robust Biomarkers: Methodologically Tracking Causal Processes in Alzheimer’s Measurement.Vadim Keyser & Louis Sarry - 2020 - In Barbara Osimani & Adam La Caze (eds.), Uncertainty in Pharmacology. pp. 289-318.
    In biomedical measurement, biomarkers are used to achieve reliable prediction of, and useful causal information about patient outcomes while minimizing complexity of measurement, resources, and invasiveness. A biomarker is an assayable metric that discloses the status of a biological process of interest, be it normative, pathophysiological, or in response to intervention. The greatest utility from biomarkers comes from their ability to help clinicians (and researchers) make and evaluate clinical decisions. In this paper we discuss a specific methodological use of clinical (...)
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  • If I Could Talk to the Animals: Measuring Subjective Animal Welfare.Heather Browning - 2019 - Dissertation, Australian National University
    Animal welfare is a concept that plays a role within both our moral deliberations and the relevant areas of science. The study of animal welfare has impacts on decisions made by legislators, producers and consumers with regards to housing and treatment of animals. Our ethical deliberations in these domains need to consider our impact on animals, and the study of animal welfare provides the information that allows us to make informed decisions. This thesis focusses on taking a philosophical perspective to (...)
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  • The Multiple Dimensions of Multiple Determination.Klodian Coko - 2020 - Perspectives on Science 28 (4):505-541.
    Multiple determination is the epistemic strategy of establishing the same result by means of multiple, independent procedures. It is an important strategy praised by both philosophers of science and practicing scientists. Despite the heavy appeal to multiple determination, little analysis has been provided regarding the specific grounds upon which its epistemic virtues rest. This article distinguishes between the various dimensions of multiple determination and shows how they can be used to evaluate the epistemic force of the strategy in particular cases. (...)
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  • Dissecting Scientific Explanation in AI (sXAI): A Case for Medicine and Healthcare.Juan M. Durán - 2021 - Artificial Intelligence 297:103498.
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  • Robustness Reasoning in Climate Model Comparisons.Ryan O’Loughlin - 2021 - Studies in History and Philosophy of Science Part A 85:34-43.
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  • Justifying the Norms of Inductive Inference.Olav Benjamin Vassend - forthcoming - British Journal for the Philosophy of Science:axz041.
    Bayesian inference is limited in scope because it cannot be applied in idealized contexts where none of the hypotheses under consideration is true and because it is committed to always using the likelihood as a measure of evidential favouring, even when that is inappropriate. The purpose of this article is to study inductive inference in a very general setting where finding the truth is not necessarily the goal and where the measure of evidential favouring is not necessarily the likelihood. I (...)
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  • Psychopathology and Truth: A Defense of Realism.Markus I. Eronen - 2019 - Journal of Medicine and Philosophy 44 (4):507-520.
    Recently Kenneth Kendler and Peter Zachar have raised doubts about the correspondence theory of truth and scientific realism in psychopathology. They argue that coherentist or pragmatist approaches to truth are better suited for understanding the reality of psychiatric disorders. In this article, I show that rejecting realism based on the correspondence theory is deeply problematic: It makes psychopathology categorically different from other sciences, and results in an implausible view of scientific discovery and progress. As an alternative, I suggest a robustness-based (...)
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  • Robust Realism for the Life Sciences.Markus Eronen - 2019 - Synthese 196 (6):2341-2354.
    Although scientific realism is the default position in the life sciences, philosophical accounts of realism are geared towards physics and run into trouble when applied to fields such as biology or neuroscience. In this paper, I formulate a new robustness-based version of entity realism, and show that it provides a plausible account of realism for the life sciences that is also continuous with scientific practice. It is based on the idea that if there are several independent ways of measuring, detecting (...)
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  • The Epistemic Virtue of Robustness in Climate Modeling (MA Dissertation).Parjanya Joshi - 2019 - Dissertation, Tata Institute of Social Sciences
    The aim of this dissertation is to comprehensively study various robustness arguments proposed in the literature from Levins to Lloyd as well as the opposition offered to them and pose enquiry into the degree of epistemic virtue that they provide to the model prediction results with respect to climate science and modeling. Another critical issue that this dissertation strives to examine is that of the actual epistemic notion that is operational when scientists and philosophers appeal to robustness. In attempting to (...)
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  • Robustness and Independent Evidence.Jacob Stegenga & Tarun Menon - 2017 - Philosophy of Science 84 (3):414-435.
    Robustness arguments hold that hypotheses are more likely to be true when they are confirmed by diverse kinds of evidence. Robustness arguments require the confirming evidence to be independent. We identify two kinds of independence appealed to in robustness arguments: ontic independence —when the multiple lines of evidence depend on different materials, assumptions, or theories—and probabilistic independence. Many assume that OI is sufficient for a robustness argument to be warranted. However, we argue that, as typically construed, OI is not a (...)
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  • Robustness, Evidence, and Uncertainty: An Exploration of Policy Applications of Robustness Analysis.Nicolas Wüthrich - unknown
    Policy-makers face an uncertain world. One way of getting a handle on decision-making in such an environment is to rely on evidence. Despite the recent increase in post-fact figures in politics, evidence-based policymaking takes centre stage in policy-setting institutions. Often, however, policy-makers face large volumes of evidence from different sources. Robustness analysis can, prima facie, handle this evidential diversity. Roughly, a hypothesis is supported by robust evidence if the different evidential sources are in agreement. In this thesis, I strengthen the (...)
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