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  1. Tales of twin cities: what are climate analogues good for?Giovanni Valente, Hernán Bobadilla, Rawad El Skaf & Francesco Nappo - 2024 - European Journal for Philosophy of Science 14 (3):1-28.
    This article provides an epistemological assessment of climate analogue methods, with specific reference to the use of spatial analogues in the study of the future climate of target locations. Our contention is that, due to formal and conceptual inadequacies of geometrical dissimilarity metrics and the loss of relevant information, especially when reasoning from the physical to the socio-economical level, purported inferences from climate analogues of the spatial kind we consider here prove limited in a number of ways. Indeed, we formulate (...)
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  • How do different interpretations work together in a single scientific explanatory project? A case study of the Olami-Feder-Christensen model of earthquakes.Hernán Bobadilla - 2024 - European Journal for Philosophy of Science 14 (3):1-29.
    Interpretation plays a central role in using scientific models to explain natural phenomena: Meaning must be bestowed upon a model in terms of what it is and what it represents to be used for model explanations. However, it remains unclear how capacious and complex interpretation in models can be, particularly when conducted by the same group of scientists in the context of one explanatory project. This paper sheds light upon this question by examining modelling and explanatory practices related to the (...)
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  • Using Paleoclimate Analogues to Inform Climate Projections.Aja Watkins - 2024 - Perspectives on Science 32 (4):415-459.
    Philosophers of science have paid close attention to climate simulations as means of projecting the severity and effects of climate change, but have neglected the full diversity of methods in climate science. This paper shows the philosophical richness of another method in climate science: the practice of using paleoclimate analogues to inform our climate projections. First, I argue that the use of paleoclimate analogues can offer important insights to philosophers of the historical sciences. Rather than using the present as a (...)
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  • Scaling procedures in climate science: Using temporal scaling to identify a paleoclimate analogue.Aja Watkins - 2023 - Studies in History and Philosophy of Science Part A 102 (C):31-44.
    Using past episodes of climate change as a source of evidence to inform our projections about contemporary climate change requires establishing the extent to which episodes in the deep past are analogous to the current crisis. However, many scientists claim that contemporary rates of climate change (e.g., rates of carbon emissions or temperature change) are unprecedented, including compared to episodes in the deep past. If so, this would limit the utility of paleoclimate analogues. In this paper, I show how a (...)
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  • How to Express Implicit Attitudes.Elmar Unnsteinsson - 2024 - Philosophical Quarterly 74 (1):251-272.
    I argue that what speakers mean or express can be determined by their implicit or unconscious states, rather than explicit or conscious states. Further, on this basis, I show that the sincerity conditions for utterances can also be fixed by implicit states. This is a surprising result which goes against common assumptions about speech acts and sincerity. Roughly, I argue that the result is implied by two plausible and independent theories of the metaphysics of speaker meaning and, further, that this (...)
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  • Multi-model approaches to phylogenetics: Implications for idealization.Aja Watkins - 2021 - Studies in History and Philosophy of Science Part A 90 (C):285-297.
    Phylogenetic models traditionally represent the history of life as having a strictly-branching tree structure. However, it is becoming increasingly clear that the history of life is often not strictly-branching; lateral gene transfer, endosymbiosis, and hybridization, for example, can all produce lateral branching events. There is thus motivation to allow phylogenetic models to have a reticulate structure. One proposal involves the reconciliation of genealogical discordance. Briefly, this method uses patterns of disagreement – discordance – between trees of different genes to add (...)
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  • Model robustness in economics: the admissibility and evaluation of tractability assumptions.Ryan O’Loughlin & Dan Li - 2022 - Synthese 200 (1):1-23.
    Lisciandra poses a challenge for robustness analysis as applied to economic models. She argues that substituting tractability assumptions risks altering the main mathematical structure of the model, thereby preventing the possibility of meaningfully evaluating the same model under different assumptions. In such cases RA is argued to be inapplicable. However, Lisciandra is mistaken to take the goal of RA as keeping the mathematical properties of tractability assumptions intact. Instead, RA really aims to keep the modeling component while varying the corresponding (...)
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  • 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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  • Robustness reasoning in climate model comparisons.Ryan O’Loughlin - 2021 - Studies in History and Philosophy of Science Part A 85 (C):34-43.
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  • Understanding climate phenomena with data-driven models.Benedikt Knüsel & Christoph Baumberger - 2020 - Studies in History and Philosophy of Science Part A 84 (C):46-56.
    In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple climate models are characterized by a larger graspability, state-of-the-art models have a higher representational accuracy and representational (...)
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  • 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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  • Mathematical Models and Robustness Analysis in Epistemic Democracy: A Systematic Review of Diversity Trumps Ability Theorem Models.Ryota Sakai - 2020 - Philosophy of the Social Sciences 50 (3):195-214.
    This article contributes to the revision of the procedure of robustness analysis of mathematical models in epistemic democracy using the systematic review method. It identifies the drawbacks of robustness analysis in epistemic democracy in terms of sample universality and inference from samples with the same results. To exemplify the effectiveness of systematic review, this article conducted a pilot review of diversity trumps ability theorem models, which are mathematical models of deliberation often cited by epistemic democrats. A review of nine models (...)
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  • 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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  • 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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  • Representationalism is a dead end.Guilherme Sanches de Oliveira - 2018 - Synthese 198 (1):209-235.
    Representationalism—the view that scientific modeling is best understood in representational terms—is the received view in contemporary philosophy of science. Contributions to this literature have focused on a number of puzzles concerning the nature of representation and the epistemic role of misrepresentation, without considering whether these puzzles are the product of an inadequate analytical framework. The goal of this paper is to suggest that this possibility should be taken seriously. The argument has two parts, employing the “can’t have” and “don’t need” (...)
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  • Microbes, mathematics, and models.Maureen A. O'Malley & Emily C. Parke - 2018 - Studies in History and Philosophy of Science Part A 72:1-10.
    Microbial model systems have a long history of fruitful use in fields that include evolution and ecology. In order to develop further insight into modelling practice, we examine how the competitive exclusion and coexistence of competing species have been modelled mathematically and materially over the course of a long research history. In particular, we investigate how microbial models of these dynamics interact with mathematical or computational models of the same phenomena. Our cases illuminate the ways in which microbial systems and (...)
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  • Neutral Theory, Biased World.William Bausman - 2016 - Dissertation, University of Minnesota
    The ecologist today finds scarce ground safe from controversy. Decisions must be made about what combination of data, goals, methods, and theories offers them the foundations and tools they need to construct and defend their research. When push comes to shove, ecologists often turn to philosophy to justify why it is their approach that is scientific. Karl Popper’s image of science as bold conjectures and heroic refutations is routinely enlisted to justify testing hypotheses over merely confirming them. One of the (...)
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  • The Disconnect Problem, Scientific Authority, and Climate Policy.Matthew J. Brown & Joyce C. Havstad - 2017 - Perspectives on Science 25 (1):67-94.
    The disconnect problem arises wherever there is ongoing and severe discordance between the scientific assessment of a politically relevant issue, and the politics and legislation of said issue. Here, we focus on the disconnect problem as it arises in the case of climate change, diagnosing a failure to respect the necessary tradeoff between authority and autonomy within a public institution like science. After assessing the problematic deployment of scientific authority in this arena, we offer suggestions for how to mitigate climate (...)
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  • Variety-of-evidence reasoning about the distant past: A case study in paleoclimate reconstruction.Martin A. Vezér - 2017 - European Journal for Philosophy of Science 7 (2):257-265.
    The epistemology of studies addressing questions about historical and prehistorical phenomena is a subject of increasing discussion among philosophers of science. A related field of inquiry that has yet to be connected to this topic is the epistemology of climate science. Branching these areas of research, I show how variety-of-evidence reasoning accounts for scientific inferences about the past by detailing a case study in paleoclimate reconstruction. This analysis aims to clarify the logic of historical inquiry in general and, by focusing (...)
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  • (1 other version)Computer models and the evidence of anthropogenic climate change: An epistemology of variety-of-evidence inferences and robustness analysis.Martin Vezer - 2016 - Computer Models and the Evidence of Anthropogenic Climate Change: An Epistemology of Variety-of-Evidence Inferences and Robustness Analysis MA Vezér Studies in History and Philosophy of Science 56:95-102.
    To study climate change, scientists employ computer models, which approximate target systems with various levels of skill. Given the imperfection of climate models, how do scientists use simulations to generate knowledge about the causes of observed climate change? Addressing a similar question in the context of biological modelling, Levins (1966) proposed an account grounded in robustness analysis. Recent philosophical discussions dispute the confirmatory power of robustness, raising the question of how the results of computer modelling studies contribute to the body (...)
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  • Severe testing of climate change hypotheses.Joel Katzav - 2013 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 44 (4):433-441.
    I examine, from Mayo's severe testing perspective, the case found in the Intergovernmental Panel on Climate Change fourth report for the claim that increases in anthropogenic greenhouse gas concentrations caused most of the post-1950 global warming. My examination begins to provide an alternative to standard, probabilistic assessments of OUR FAULT. It also brings out some of the limitations of variety of evidence considerations in assessing this and other hypotheses about the causes of climate change, and illuminates the epistemology of optimal (...)
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  • Robustness Analysis as Explanatory Reasoning.Jonah N. Schupbach - 2018 - British Journal for the Philosophy of Science 69 (1):275-300.
    When scientists seek further confirmation of their results, they often attempt to duplicate the results using diverse means. To the extent that they are successful in doing so, their results are said to be robust. This paper investigates the logic of such "robustness analysis" [RA]. The most important and challenging question an account of RA can answer is what sense of evidential diversity is involved in RAs. I argue that prevailing formal explications of such diversity are unsatisfactory. I propose a (...)
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  • Robustness, Diversity of Evidence, and Probabilistic Independence.Jonah N. Schupbach - 2015 - In Uskali Mäki, Stéphanie Ruphy, Gerhard Schurz & Ioannis Votsis (eds.), Recent Developments in the Philosophy of Science. Cham: Springer. pp. 305-316.
    In robustness analysis, hypotheses are supported to the extent that a result proves robust, and a result is robust to the extent that we detect it in diverse ways. But what precise sense of diversity is at work here? In this paper, I show that the formal explications of evidential diversity most often appealed to in work on robustness – which all draw in one way or another on probabilistic independence – fail to shed light on the notion of diversity (...)
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  • Uncertainties, Plurality, and Robustness in Climate Research and Modeling: On the Reliability of Climate Prognoses.Anna Leuschner - 2015 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 46 (2):367-381.
    The paper addresses the evaluation of climate models and gives an overview of epistemic uncertainties in climate modeling; the uncertainties concern the data situation as well as the causal behavior of the climate system. In order to achieve reasonable results nonetheless, multimodel ensemble studies are employed in which diverse models simulate the future climate under different emission scenarios. The models jointly deliver a robust range of climate prognoses due to a broad plurality of theories, techniques, and methods in climate research; (...)
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  • Are climate models credible worlds? Prospects and limitations of possibilistic climate prediction.Gregor Betz - 2015 - European Journal for Philosophy of Science 5 (2):191-215.
    Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would allow one to (...)
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  • A practical philosophy of complex climate modelling.Gavin A. Schmidt & Steven Sherwood - 2015 - European Journal for Philosophy of Science 5 (2):149-169.
    We give an overview of the practice of developing and using complex climate models, as seen from experiences in a major climate modelling center and through participation in the Coupled Model Intercomparison Project. We discuss the construction and calibration of models; their evaluation, especially through use of out-of-sample tests; and their exploitation in multi-model ensembles to identify biases and make predictions. We stress that adequacy or utility of climate models is best assessed via their skill against more naïve predictions. The (...)
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  • Robustness and sensitivity of biological models.Jani Raerinne - 2013 - Philosophical Studies 166 (2):285-303.
    The aim of this paper is to develop ideas about robustness analyses. I introduce a form of robustness analysis that I call sufficient parameter robustness, which has been neglected in the literature. I claim that sufficient parameter robustness is different from derivational robustness, the focus of previous research. My purpose is not only to suggest a new taxonomy of robustness, but also to argue that previous authors have concentrated on a narrow sense of robustness analysis, which they have inadequately distinguished (...)
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  • Robust! -- Handle with care.Wybo Houkes & Krist Vaesen - 2012 - Philosophy of Science 79 (3):1-20.
    Michael Weisberg has argued that robustness analysis is useful in evaluating both scientific models and their implications and that robustness analysis comes in three types that share their form and aim. We argue for three cautionary claims regarding Weisberg's reconstruction: robustness analysis may be of limited or no value in evaluating models and their implications; the unificatory reconstruction conceals that the three types of robustness differ in form and role; there is no confluence of types of robustness. We illustrate our (...)
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  • Scientific Models.Stephen M. Downes - 2011 - Philosophy Compass 6 (11):757-764.
    This contribution provides an assessment of the epistemological role of scientific models. The prevalent view that all scientific models are representations of the world is rejected. This view points to a unified way of resolving epistemic issues for scientific models. The emerging consensus in philosophy of science that models have many different epistemic roles in science is presented and defended.
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  • Variety of evidence in multimessenger astronomy.Shannon Sylvie Abelson - 2022 - Studies in History and Philosophy of Science Part A 94 (C):133-142.
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  • What does robustness teach us in climate science: a re-appraisal.Eric Winsberg - 2021 - Synthese 198 (Suppl 21):5099-5122.
    In the philosophy of climate science, debate surrounding the issue of variety of evidence has mostly taken the form of attempting to connect these issues in climate science and climate modeling with philosophical accounts of what has come to be known as “robustness analysis.” I argue that an “explanatory” conception of robustness is the best candidate for understanding variety of evidence in climate science. I apply the analysis to both examples of model agreement, as well at to the convergence of (...)
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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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  • (1 other version)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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  • Aggregating Evidence in Climate Science: Consilience, Robustness and the Wisdom of Multiple Models.Martin A. Vezér - unknown
    The goal of this dissertation is to contribute to the epistemology of science by addressing a set of related questions arising from current discussions in the philosophy and science of climate change: (1) Given the imperfection of computer models, how do they provide information about large and complex target systems? (2) What is the relationship between consilient reasoning and robust evidential support in the production of scientific knowledge? (3) Does taking the mean of a set of model outputs provide epistemic (...)
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  • Similarity, Adequacy, and Purpose: Understanding the Success of Scientific Models.Melissa Jacquart - 2016 - Dissertation, University of Western Ontario
    A central component to scientific practice is the construction and use of scientific models. Scientists believe that the success of a model justifies making claims that go beyond the model itself. However, philosophical analysis of models suggests that drawing inferences about the world from successful models is more complex. In this dissertation I develop a framework that can help disentangle the related strands of evaluation of model success, model extendibility, and the ability to draw ampliative inferences about the world from (...)
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  • “Agreement” in the IPCC Confidence measure.William Rehg & Kent Staley - 2017 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 57:126-134.
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  • Robustness in evolutionary explanations: a positive account.Cédric Paternotte & Jonathan Grose - 2017 - Biology and Philosophy 32 (1):73-96.
    Robustness analysis is widespread in science, but philosophers have struggled to justify its confirmatory power. We provide a positive account of robustness by analysing some explicit and implicit uses of within and across-model robustness in evolutionary theory. We argue that appeals to robustness are usually difficult to justify because they aim to increase the likeliness that a phenomenon obtains. However, we show that robust results are necessary for explanations of phenomena with specific properties. Across-model robustness is necessary for how-possibly explanations (...)
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  • “Location” Incommensurability and “Replication” Indeterminacy: Clarifying an Entrenched Conflation by Using an Involved Approach.Ayelet Shavit - 2016 - Perspectives on Science 24 (4):425-442.
    . Reproducible results and repeatable measurements at the same location are fundamental to science, yet of grave concern to scientists. Involvement in biological re-surveys under MVZ-Berkeley, Harvard-LTER and Hamaarag elucidated “replication” and “location” and untangled “incommensurability” from “no fact of the matter” and “indeterminacy.” All cases revealed incommensurability without indeterminacy on the smallest scale and indeterminacy without incommensurability on higher scales, with communication failure in the former and successful workarounds in the latter. I argue that an involved philosophy helps clarify (...)
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  • Allocating confirmation with derivational robustness.Aki Lehtinen - 2016 - Philosophical Studies 173 (9):2487-2509.
    Robustness may increase the degree to which the robust result is indirectly confirmed if it is shown to depend on confirmed rather than disconfirmed assumptions. Although increasing the weight with which existing evidence indirectly confirms it in such a case, robustness may also be irrelevant for confirmation, or may even disconfirm. Whether or not it confirms depends on the available data and on what other results have already been established.
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  • Structural Modeling Error and the System Individuation Problem.Jon Lawhead - forthcoming - British Journal for the Philosophy of Science.
    Recent work by Frigg et. al. and Mayo-Wilson have called attention to a particular sort of error associated with attempts to model certain complex systems: structural modeling error. The assessment of the degree of SME in a model presupposes agreement between modelers about the best way to individuate natural systems, an agreement which can be more problematic than it appears. This problem, which we dub “the system individuation problem” arises in many of the same contexts as SME, and the two (...)
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  • Model robustness as a confirmatory virtue: The case of climate science.Elisabeth A. Lloyd - 2015 - Studies in History and Philosophy of Science Part A 49:58-68.
    I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independentlysupported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate (...)
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  • About the warrants of computer-based empirical knowledge.Anouk Barberousse & Marion Vorms - 2014 - Synthese 191 (15):3595-3620.
    Computer simulations are widely used in current scientific practice, as a tool to obtain information about various phenomena. Scientists accordingly rely on the outputs of computer simulations to make statements about the empirical world. In that sense, simulations seem to enable scientists to acquire empirical knowledge. The aim of this paper is to assess whether computer simulations actually allow for the production of empirical knowledge, and how. It provides an epistemological analysis of present-day empirical science, to which the traditional epistemological (...)
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  • Convergence strategies for theory assessment.Elena Castellani - 2024 - Studies in History and Philosophy of Science Part A 104 (C):78-87.
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  • Models in the Geosciences.Alisa Bokulich & Naomi Oreskes - 2017 - In Magnani Lorenzo & Bertolotti Tommaso Wayne (eds.), Springer Handbook of Model-Based Science. Springer. pp. 891-911.
    The geosciences include a wide spectrum of disciplines ranging from paleontology to climate science, and involve studies of a vast range of spatial and temporal scales, from the deep-time history of microbial life to the future of a system no less immense and complex than the entire Earth. Modeling is thus a central and indispensable tool across the geosciences. Here, we review both the history and current state of model-based inquiry in the geosciences. Research in these fields makes use of (...)
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  • Philosophy of climate science part II: modelling climate change.Roman Frigg, Erica Thompson & Charlotte Werndl - 2015 - Philosophy Compass 10 (12):965-977.
    This is the second of three parts of an introduction to the philosophy of climate science. In this second part about modelling climate change, the topics of climate modelling, confirmation of climate models, the limits of climate projections, uncertainty and finally model ensembles will be discussed.
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  • Environmental philosophy: Response to critics.Sahotra Sarkar - 2014 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 45 (1):105-109.
    The following piece is a response to the critiques from Frank, Garson, and Odenbaugh. The issues at stake are: the definition of biodiversity and its normativity, historical fidelity in ecological restoration, naturalism in environmental ethics, and the role of decision theory. The normativity of the concept of biodiversity in conservation biology is defended. Historical fidelity is criticized as an operative goal for ecological restoration. It is pointed out that the analysis requires only minimal assumptions about ethics. Decision theory is presented (...)
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  • The Elusive Basis of Inferential Robustness.James Justus - 2012 - Philosophy of Science 79 (5):795-807.
    Robustness concepts are often invoked to manage two obstacles confronting models of ecological systems: complexity and uncertainty. The intuitive idea is that any result derived from many idealized but credible models is thereby made more reliable or is better confirmed. An appropriate basis for this inference has proven elusive. Here, several representations of robustness analysis are vetted, paying particular attention to complex models of ecosystems and the global climate. The claim that robustness is itself confirmatory because robustness analysis employs a (...)
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  • (1 other version)The role of 'complex' empiricism in the debates about satellite data and climate models.Elisabeth A. Lloyd - 2012 - Studies in History and Philosophy of Science Part A 43 (2):390-401.
    climate scientists have been engaged in a decades-long debate over the standing of satellite measurements of the temperature trends of the atmosphere above the surface of the earth. This is especially significant because skeptics of global warming and the greenhouse effect have utilized this debate to spread doubt about global climate models used to predict future states of climate. I use this case from an under-studied science to illustrate two distinct philosophical approaches to the relation among data, scientists, measurement, models, (...)
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  • The strategy of model building in climate science.Lachlan Douglas Walmsley - 2020 - Synthese 199 (1-2):745-765.
    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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  • (1 other version)Computer models and the evidence of anthropogenic climate change: An epistemology of variety-of-evidence inferences and robustness analysis.Martin A. Vezér - 2016 - Studies in History and Philosophy of Science Part A 56 (C):95-102.
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