Results for 'scientific inference'

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  1.  57
    Cartesian Certainty, Realism and Scientific Inference.Manuel Barrantes - forthcoming - In Jorge Secada & C. Wee (eds.), The Cartesian Mind.
    In the Principles, Descartes explains several observable phenomena showing that they are caused by special arrangements of unobservable microparticles. Despite these microparticles being unobservable, many passages suggest that he was very confident that these explanations were correct. In other passages, however, Descartes points out that these explanations merely hold the status of ‘suppositions’ or ‘conjectures’ that could be wrong. The aim of this chapter is to clarify this apparent conflict. I argue that the possibility of natural explanations being wrong should (...)
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  2. The “Positive Argument” for Constructive Empiricism and Inference to the Best Explanation.Moti Mizrahi - 2018 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 49 (3):461–466.
    In this paper, I argue that the “positive argument” for Constructive Empiricism (CE), according to which CE “makes better sense of science, and of scientific activity, than realism does” (van Fraassen 1980, 73), is an Inference to the Best Explanation (IBE). But constructive empiricists are critical of IBE, and thus they have to be critical of their own “positive argument” for CE. If my argument is sound, then constructive empiricists are in the awkward position of having to reject (...)
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  3. Does IBE Require a "Model" of Explanation?Frank Cabrera - 2017 - British Journal for the Philosophy of Science:axy010.
    In this paper, I consider an important challenge to the popular theory of scientific inference commonly known as “Inference to the Best Explanation” (IBE), one which has received scant attention. The problem is that there exists a wide array of rival models of explanation, thus leaving IBE objectionably indeterminate. First, I briefly introduce IBE. Then, I motivate the problem and offer three potential solutions, the most plausible of which is to adopt a kind of pluralism about the (...)
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  4. The No‐Miracles Argument for Realism: Inference to an Unacceptable Explanation.Greg Frost‐Arnold - 2010 - Philosophy of Science 77 (1):35-58.
    I argue that a certain type of naturalist should not accept a prominent version of the no-miracles argument (NMA). First, scientists (usually) do not accept explanations whose explanans-statements neither generate novel predictions nor unify apparently disparate established claims. Second, scientific realism (as it appears in the NMA) is an explanans that makes no new predictions and fails to unify disparate established claims. Third, many proponents of the NMA explicitly adopt a naturalism that forbids philosophy of science from using any (...)
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  5.  16
    Inference Belief and Interpretation in Science.Avijit Lahiri - forthcoming - Kolkata India,: Avijit Lahiri.
    This monograph is an in-depth and engaging discourse on the deeply cognitive roots of human scientific quest. The process of making scientific inferences is continuous with the day-to-day inferential activity of individuals, and is predominantly inductive in nature. Inductive inference, which is fallible, exploratory, and open-ended, is of essential relevance in our incessant efforts at making sense of a complex and uncertain world around us, and covers a vast range of cognitive activities, among which scientific exploration (...)
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  6. Realism and the Limits of Explanatory Reasoning.Juha Saatsi - 2018 - In The Routledge Handbook of Scientific Realism. London: Routledge. pp. 200-211.
    This chapter examines issues surrounding inference to the best explanation, its justification, and its role in different arguments for scientific realism, as well as more general issues concerning explanations’ ontological commitments. Defending the reliability of inference to the best explanation has been a central plank in various realist arguments, and realists have drawn various ontological conclusions from the premise that a given scientific explanation best explains some phenomenon. This chapter stresses the importance of thinking carefully about (...)
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  7. Scientific Realism and the Pessimistic Meta-Modus Tollens.Timothy D. Lyons - 2002 - In Steve Clarke & Timothy D. Lyons (eds.), Recent Themes in the Philosophy of Science: Scientific Realism and Commonsense. Dordrecht: Springer. pp. 63-90.
    Broadly speaking, the contemporary scientific realist is concerned to justify belief in what we might call theoretical truth, which includes truth based on ampliative inference and truth about unobservables. Many, if not most, contemporary realists say scientific realism should be treated as ‘an overarching scientific hypothesis’ (Putnam 1978, p. 18). In its most basic form, the realist hypothesis states that theories enjoying general predictive success are true. This hypothesis becomes a hypothesis to be tested. To justify (...)
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  8. Explanation and Explanationism in Science and Metaphysics.Juha Saatsi - forthcoming - In Matthew Slater & Zanja Yudell (eds.), Metaphysics and the Philosophy of Science: New Essays. Oxford University Press.
    This chapter examines the status of inference to the best explanation in naturalistic metaphysics. The methodology of inference to the best explanation in metaphysics is studied from the perspective of contemporary views on scientific explanation and explanatory inferences in the history and philosophy of science. This reveals serious shortcomings in prevalent attempts to vindicate metaphysical "explanationism" by reference to similarities between science and naturalistic metaphysics. This critique is brought out by considering a common gambit of methodological unity: (...)
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  9. Explanatory Rivals and the Ultimate Argument.Finnur Dellsén - 2015 - Theoria 82 (3):217-237.
    Although many aspects of Inference to the Best Explanation have been extensively discussed, very little has so far been said about what it takes for a hypothesis to count as a rival explanatory hypothesis in the context of IBE. The primary aim of this article is to rectify this situation by arguing for a specific account of explanatory rivalry. On this account, explanatory rivals are complete explanations of a given explanandum. When explanatory rivals are conceived of in this way, (...)
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  10. Presume It Not: True Causes in the Search for the Basis of Heredity.Aaron Novick & Raphael Scholl - 2017 - British Journal for the Philosophy of Science (1):axy001.
    Kyle Stanford has recently given substance to the problem of unconceived alternatives, which challenges the reliability of inference to the best explanation (IBE) in remote domains of nature. Conjoined with the view that IBE is the central inferential tool at our disposal in investigating these domains, the problem of unconceived alternatives leads to scientific anti-realism. We argue that, at least within the biological community, scientists are now and have long been aware of the dangers of IBE. We re-analyze (...)
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  11. Modeling and Inferring in Science.Emiliano Ippoliti, Thomas Nickles & Fabio Sterpetti - 2016 - In Emiliano Ippoliti, Fabio Sterpetti & Thomas Nickles (eds.), Models and Inferences in Science. Springer. pp. 1-9.
    Science continually contributes new models and rethinks old ones. The way inferences are made is constantly being re-evaluated. The practice and achievements of science are both shaped by this process, so it is important to understand how models and inferences are made. But, despite the relevance of models and inference in scientific practice, these concepts still remain contro-versial in many respects. The attempt to understand the ways models and infer-ences are made basically opens two roads. The first one (...)
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  12. Epistemic Selectivity, Historical Threats, and the Non-Epistemic Tenets of Scientific Realism.Timothy D. Lyons - 2017 - Synthese 194 (9):3203-3219.
    The scientific realism debate has now reached an entirely new level of sophistication. Faced with increasingly focused challenges, epistemic scientific realists have appropriately revised their basic meta-hypothesis that successful scientific theories are approximately true: they have emphasized criteria that render realism far more selective and, so, plausible. As a framework for discussion, I use what I take to be the most influential current variant of selective epistemic realism, deployment realism. Toward the identification of new case studies that (...)
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  13. 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 (...)
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  14.  84
    Beyond the Instinct-Inference Dichotomy: A Unified Interpretation of Peirce's Theory of Abduction.Mousa Mohammadian - 2019 - Transactions of the Charles S. Peirce Society 55 (2):138-160.
    I examine and resolve an exegetical dichotomy between two main interpretations of Peirce’s theory of abduction, namely, the Generative Interpretation and the Pursuitworthiness Interpretation. According to the former, abduction is the instinctive process of generating explanatory hypotheses through a mental faculty called insight. According to the latter, abduction is a rule-governed procedure for determining the relative pursuitworthiness of available hypotheses and adopting the worthiest one for further investigation—such as empirical tests—based on economic considerations. It is shown that the Generative Interpretation (...)
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  15. Coherence of Our Best Scientific Theories.Seungbae Park - 2011 - Foundations of Science 16 (1):21-30.
    Putnam (1975) infers from the success of a scientific theory to its approximate truth and the reference of its key term. Laudan (1981) objects that some past theories were successful, and yet their key terms did not refer, so they were not even approximately true. Kitcher (1993) replies that the past theories are approximately true because their working posits are true, although their idle posits are false. In contrast, I argue that successful theories which cohere with each other are (...)
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  16.  14
    Resolving the Raven Paradox: Simple Random Sampling, Stratified Random Sampling, and Inference to the Best Explanation.Barry Ward - manuscript
    We object to standard, simple random sampling resolutions of the raven paradox on the grounds that they relevantly diverge from scientific practice. In response, we develop a stratified random sampling model. It provides a better fit and apparently rehabilitates simple random sampling resolutions as legitimate idealizations of that practice. However, neither simple nor stratified models fare well with a second concern, the objection from potential bias. In response, we develop a third model on which we systematically check kinds of (...)
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  17. Scientific Realism, Adaptationism and the Problem of the Criterion.Fabio Sterpetti - 2015 - Kairos 13 (1):7-45.
    Scientific Realism (SR) has three crucial aspects: 1) the centrality of the concept of truth, 2) the idea that success is a reliable indicator of truth, and 3) the idea that the Inference to the Best Explanation is a reliable inference rule. It will be outlined how some realists try to overcome the difficulties which arise in justifying such crucial aspects relying on an adaptationist view of evolutionism, and why such attempts are inadequate. Finally, we will briefly (...)
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  18.  23
    Scientific Realism Without Rigid Designation in Kant's Analogies.David Landy - 2016 - Kant E-Prints 11 (2):70-89.
    In Kant, Science, and Human Nature, Robert Hanna argues against a version of scientific realism founded on the Kripke/Putnam theory of reference, and defends a Kant-inspired manifest realism in its place. I reject Kriple/Putnam for different reasons than Hanna does, and argue that what should replace it is not manifest realism, but Kant‘s own scientific realism, which rests on a radically different theory of reference. Kant holds that we picture manifest objects by uniting manifolds of sensation using concepts-qua-inferential-rules. (...)
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  19. Recipes for Science: An Introduction to Scientific Methods and Reasoning.Angela Potochnik, Matteo Colombo & Cory Wright - 2018 - New York: Routledge.
    There is widespread recognition at universities that a proper understanding of science is needed for all undergraduates. Good jobs are increasingly found in fields related to Science, Technology, Engineering, and Medicine, and science now enters almost all aspects of our daily lives. For these reasons, scientific literacy and an understanding of scientific methodology are a foundational part of any undergraduate education. Recipes for Science provides an accessible introduction to the main concepts and methods of scientific reasoning. With (...)
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  20. How to Study Adaptation (and Why to Do It That Way).Mark E. Olson & Alfonso Arroyo-Santos - 2015 - Quarterly Review of Biology 90 (2):167-191.
    Some adaptationist explanations are regarded as maximally solid and others fanciful just-so stories. Just-so stories are explanations based on very little evidence. Lack of evidence leads to circular-sounding reasoning: “this trait was shaped by selection in unseen ancestral populations and this selection must have occurred because the trait is present.” Well-supported adaptationist explanations include evidence that is not only abundant but selected from comparative, populational, and optimality perspectives, the three adaptationist subdisciplines. Each subdiscipline obtains its broad relevance in evolutionary biology (...)
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  21. Introduction: Lessons From the Scientific Butchery.Matthew H. Slater & Andrea Borghini - 2013 - In Joseph Keim Campbell, Michael O'Rourke & Matthew H. Slater (eds.), Carving Nature at its Joints: Natural Kinds in Metaphysics and Science. MIT Press.
    Good chefs know the importance of maintaining sharp knives in the kitchen. What’s their secret? A well-worn Taoist allegory offers some advice. The king asks about his butcher’s impressive knifework. “Ordinary butchers,” he replied “hack their way through the animal. Thus their knife always needs sharpening. My father taught me the Taoist way. I merely lay the knife by the natural openings and let it find its own way through. Thus it never needs sharpening” (Kahn 1995, vii; see also Watson (...)
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  22.  72
    Statistical Inference and the Replication Crisis.Lincoln J. Colling & Dénes Szűcs - forthcoming - Review of Philosophy and Psychology.
    The replication crisis has prompted many to call for statistical reform within the psychological sciences. Here we examine issues within Frequentist statistics that may have led to the replication crisis, and we examine the alternative—Bayesian statistics—that many have suggested as a replacement. The Frequentist approach and the Bayesian approach offer radically different perspectives on evidence and inference with the Frequentist approach prioritising error control and the Bayesian approach offering a formal method for quantifying the relative strength of evidence for (...)
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  23. Demonstrative Induction and the Skeleton of Inference.P. D. Magnus - 2008 - International Studies in the Philosophy of Science 22 (3):303-315.
    It has been common wisdom for centuries that scientific inference cannot be deductive; if it is inference at all, it must be a distinctive kind of inductive inference. According to demonstrative theories of induction, however, important scientific inferences are not inductive in the sense of requiring ampliative inference rules at all. Rather, they are deductive inferences with sufficiently strong premises. General considerations about inferences suffice to show that there is no difference in justification between (...)
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  24. Model Organisms Are Not (Theoretical) Models.Arnon Levy & Adrian Currie - 2015 - British Journal for the Philosophy of Science 66 (2):327-348.
    Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different epistemic characters. (...)
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  25. Belief is Not the Issue: A Defence of Inference to the Best Explanation.Gregory W. Dawes - 2013 - Ratio 26 (1):62-78.
    Defences of inference to the best explanation (IBE) frequently associate IBE with scientific realism, the idea that it is reasonable to believe our best scientific theories. I argue that this linkage is unfortunate. IBE does not warrant belief, since the fact that a theory is the best available explanation does not show it to be (even probably) true. What IBE does warrant is acceptance: taking a proposition as a premise in theoretical and/or practical reasoning. We ought to (...)
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  26.  48
    Surrealism Is Not an Alternative to Scientific Realism.Seungbae Park - 2019 - Logos and Episteme: An International Journal of Epistemology 10 (4):379–393.
    Surrealism holds that observables behave as if T were true, whereas scientific realism holds that T is true. Surrealism and scientific realism give different explanations of why T is empirically adequate. According to surrealism, T is empirically adequate because observables behave as if it were true. According to scientific realism, T is empirically adequate because it is true. I argue that the surrealist explanation merely clarifies the concept of empirical adequacy, whereas the realist explanation makes an inductive (...)
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  27. Experimental Design: Ethics, Integrity and the Scientific Method.Jonathan Lewis - 2019 - In Ron Iphofen (ed.), Handbook of Research Ethics and Scientific Integrity. Cham, Switzerland: pp. 1-16.
    Experimental design is one aspect of a scientific method. A well-designed, properly conducted experiment aims to control variables in order to isolate and manipulate causal effects and thereby maximize internal validity, support causal inferences, and guarantee reliable results. Traditionally employed in the natural sciences, experimental design has become an important part of research in the social and behavioral sciences. Experimental methods are also endorsed as the most reliable guides to policy effectiveness. Through a discussion of some of the central (...)
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  28. Constructive Empiricism: Normative or Descriptive?Moti Mizrahi - 2014 - International Journal of Philosophical Studies 22 (4):604-616.
    In this paper, I argue that Constructive Empiricism (CE) is ambiguous between two interpretations: CE as a normative epistemology of science and CE as a descriptive philosophy of science. When they present CE, constructive empiricists write as if CE is supposed to be more than a normative epistemology of science and that it is meant to be responsible to actual scientific practices. However, when they respond to objections, constructive empiricists fall back on a strictly normative interpretation of CE. This (...)
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  29. On Probability and Cosmology: Inference Beyond Data?Martin Sahlen - 2017 - In K. Chamcham, J. Silk, J. D. Barrow & S. Saunders (eds.), The Philosophy of Cosmology. Cambridge, UK:
    Modern scientific cosmology pushes the boundaries of knowledge and the knowable. This is prompting questions on the nature of scientific knowledge. A central issue is what defines a 'good' model. When addressing global properties of the Universe or its initial state this becomes a particularly pressing issue. How to assess the probability of the Universe as a whole is empirically ambiguous, since we can examine only part of a single realisation of the system under investigation: at some point, (...)
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  30. Axiological Scientific Realism and Methodological Prescription.Timothy D. Lyons - 2010 - In Henk W. de Regt (ed.), Epsa Philosophy of Science: Amsterdam 2009. Springer. pp. 187--197.
    In this paper I distinguish between two kinds of meta-hypotheses, or hypotheses about science, at issue in the scientific realism debate. The first are descriptive empirical hypotheses regarding the nature of scientific inquiry. The second are epistemological theories about what individuals should / can justifiably believe about scientific theories. Favoring the realist Type-D meta-hypotheses, I argue that a particular set of realist and non-realist efforts in the debate over Type-E’s have been valuable in the quest to describe (...)
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  31. What is Hacking’s Argument for Entity Realism?Boaz Miller - 2016 - Synthese 193 (3):991-1006.
    According to Ian Hacking’s Entity Realism, unobservable entities that scientists carefully manipulate to study other phenomena are real. Although Hacking presents his case in an intuitive, attractive, and persuasive way, his argument remains elusive. I present five possible readings of Hacking’s argument: a no-miracle argument, an indispensability argument, a transcendental argument, a Vichian argument, and a non-argument. I elucidate Hacking’s argument according to each reading, and review their strengths, their weaknesses, and their compatibility with each other.
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  32. Accepting Our Best Scientific Theories.Seungbae Park - 2015 - Filosofija. Sociologija 26 (3):218-227.
    Dawes (2013) claims that we ought not to believe but to accept our best scientific theories. To accept them means to employ them as premises in our reasoning with the goal of attaining knowledge about unobservables. I reply that if we do not believe our best scientific theories, we cannot gain knowledge about unobservables, our opponents might dismiss the predictions derived from them, and we cannot use them to explain phenomena. We commit an unethical speech act when we (...)
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  33. Why Scientific Knowledge Is Still the Best.Moti Mizrahi - 2018 - Social Epistemology Review and Reply Collective 7 (9):18-32.
    In his latest attack, even though he claims to be a practitioner of “close reading” (Wills 2018b, 34), it appears that Wills still has not bothered to read the paper in which I defend the thesis he seeks to attack (Mizrahi 2017a), or any of the papers in my exchange with Brown (Mizrahi 2017b; 2018a), as evidenced by the fact that he does not cite them at all. This explains why Wills completely misunderstands Weak Scientism and the arguments for the (...)
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  34. On the Role of Explanatory and Systematic Power in Scientific Reasoning.Peter Brössel - 2015 - Synthese 192 (12):3877-3913.
    The paper investigates measures of explanatory power and how to define the inference schema “Inference to the Best Explanation”. It argues that these measures can also be used to quantify the systematic power of a hypothesis and the inference schema “Inference to the Best Systematization” is defined. It demonstrates that systematic power is a fruitful criterion for theory choice and IBS is truth-conducive. It also shows that even radical Bayesians must admit that systemic power is an (...)
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  35. Data and Safety Monitoring Board and the Ratio Decidendi of the Trial.Roger Stanev - 2015 - Journal of Philosophy, Science and Law 15:1-26.
    Decision-making by a Data and Safety Monitoring Board (DSMB) regarding clinical trial conduct and termination is intricate and largely limited by cases and rules. Decision-making by legal jury is also intricate and largely constrained by cases and rules. In this paper, I argue by analogy that legal decision-making, which strives for a balance between competing demands of conservatism and innovation, supplies a good basis to the logic behind DSMB decision-making. Using the doctrine of precedents in legal reasoning as my central (...)
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  36. Symbol Systems as Collective Representational Resources: Mary Hesse, Nelson Goodman, and the Problem of Scientific Representation.Axel Gelfert - 2015 - Social Epistemology Review and Reply Collective 4 (6):52-61.
    This short paper grew out of an observation—made in the course of a larger research project—of a surprising convergence between, on the one hand, certain themes in the work of Mary Hesse and Nelson Goodman in the 1950/60s and, on the other hand, recent work on the representational resources of science, in particular regarding model-based representation. The convergence between these more recent accounts of representation in science and the earlier proposals by Hesse and Goodman consists in the recognition that, in (...)
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  37. Can There Be a Bayesian Explanationism? On the Prospects of a Productive Partnership.Frank Cabrera - 2017 - Synthese 194 (4):1245–1272.
    In this paper, I consider the relationship between Inference to the Best Explanation and Bayesianism, both of which are well-known accounts of the nature of scientific inference. In Sect. 2, I give a brief overview of Bayesianism and IBE. In Sect. 3, I argue that IBE in its most prominently defended forms is difficult to reconcile with Bayesianism because not all of the items that feature on popular lists of “explanatory virtues”—by means of which IBE ranks competing (...)
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  38. Machine Learning and Irresponsible Inference: Morally Assessing the Training Data for Image Recognition Systems.Owen King - 2019 - In Matteo Vincenzo D'Alfonso & Don Berkich (eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence. Springer Verlag. pp. 265-282.
    Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in which (...)
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  39. Ontic Structural Realism and Modality.Nora Berenstain & James Ladyman - 2012 - In Elaine Landry & Dean Rickles (eds.), Structural Realism: Structure, Object, and Causality. Springer.
    There is good reason to believe that scientific realism requires a commitment to the objective modal structure of the physical world. Causality, equilibrium, laws of nature, and probability all feature prominently in scientific theory and explanation, and each one is a modal notion. If we are committed to the content of our best scientific theories, we must accept the modal nature of the physical world. But what does the scientific realist’s commitment to physical modality require? We (...)
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  40.  60
    Evidence and Explanation in Cicero's On Divination.Frank Cabrera - forthcoming - Studies in History and Philosophy of Science Part A.
    In this paper, I examine Cicero’s oft-neglected De Divinatione, a dialogue investigating the legitimacy of the practice of divination. First, I offer a novel analysis of the main arguments for divination given by Quintus, highlighting the fact that he employs two logically distinct argument forms. Next, I turn to the first of the main arguments against divination given by Marcus. Here I show, with the help of modern probabilistic tools, that Marcus’ skeptical response is far from the decisive, proto-naturalistic assault (...)
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  41. The Pessimistic Induction and the Golden Rule.Seungbae Park - 2018 - Problemos 93:70-80.
    Nickles (2017) advocates scientific antirealism by appealing to the pessimistic induction over scientific theories, the illusion hypothesis (Quoidbach, Gilbert, and Wilson, 2013), and Darwin’s evolutionary theory. He rejects Putnam’s (1975: 73) no-miracles argument on the grounds that it uses inference to the best explanation. I object that both the illusion hypothesis and evolutionary theory clash with the pessimistic induction and with his negative attitude towards inference to the best explanation. I also argue that Nickles’s positive philosophical (...)
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  42. Practice Oriented Controversies and Borrowed Epistemic Support in Current Evolutionary Biology. The Case of Phylogeography.Alfonso Arroyo-Santos, Mark E. Olson & Francisco Vergara-Silva - 2015 - Perspectives on Science 23 (3):310-334.
    Although there is increasing recognition that theory and practice in science are often inseparably intertwined, discussions of scientific controversies often continue to focus on theory, and not practice or methodologies. As a contribution to constructing a framework towards understanding controversies linked to scientific practices, we introduce the notion of borrowed epistemic credibility, to describe the situation in which scientists exploit fallacious similarities between accepted tenets in other fields to garner support for a given position in their own field. (...)
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  43. 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 (...)
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  44. Existence Problems in Philosophy and Science.Peter W. Ross & Dale Turner - 2013 - Synthese 190 (18):4239-4259.
    We initially characterize what we’ll call existence problems as problems where there is evidence that a putative entity exists and this evidence is not easily dismissed; however, the evidence is not adequate to justify the claim that the entity exists, and in particular the entity hasn’t been detected. The putative entity is elusive. We then offer a strategy for determining whether an existence problem is philosophical or scientific. According to this strategy (1) existence problems are characterized in terms of (...)
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  45. Is Captain Kirk a Natural Blonde? Do X-Ray Crystallographers Dream of Electron Clouds? Comparing Model-Based Inferences in Science with Fiction.Ann-Sophie Barwich - 2018 - In Otávio Bueno, George Darby, Steven French & Dean Rickles (eds.), Thinking About Science, Reflecting on Art: Bringing Aesthetics and Philosophy of Science Together. London, UK:
    Scientific models share one central characteristic with fiction: their relation to the physical world is ambiguous. It is often unclear whether an element in a model represents something in the world or presents an artifact of model building. Fiction, too, can resemble our world to varying degrees. However, we assign a different epistemic function to scientific representations. As artifacts of human activity, how are scientific representations allowing us to make inferences about real phenomena? In reply to this (...)
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  46. Psychoanalytic and Scientific Reasoning.Jim Hopkins - 1996 - British Journal of Psychotherapy 13 (1).
    Psychoanalytic reasoning is an instance of inference to the best explanation and provides an extension of commonsense psychology that is potentially cogent, cumulative, and radical.
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  47. From Cognition to Consciousness: A Discussion About Learning, Reality Representation and Decision Making.David Guez - 2010 - Biological Theory 5 (2):136-141.
    The scientific understanding of cognition and consciousness is currently hampered by the lack of rigorous and universally accepted definitions that permit comparative studies. This paper proposes new functional and un- ambiguous definitions for cognition and consciousness in order to provide clearly defined boundaries within which general theories of cognition and consciousness may be developed. The proposed definitions are built upon the construction and manipulation of reality representation, decision making and learning and are scoped in terms of an underlying logical (...)
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  48.  68
    Investigating the Elasticity of Meat Consumption for Climate Mitigation: 4Rs for Responsible Meat Use.Sophia Efstathiou - 2019 - In Eija Vinnari & Markus Vinnari (eds.), Sustainable Governance and Management of Food Systems: Ethical Perspectives. Wageningen, Netherlands: pp. 19-25.
    Our main research question is how pliable Norwegian meat consumption practices are. However it is not any type of elasticity we are interested in. We are specifically interested in the scope for what we dub the “4Rs” of responsible meat consumption within existing food systems: 1. Reducing the amount of animal-based proteins used 2. Replacing animal-based protein with plant-based, or insect-based alternatives 3. Refining processes of utilization of animal-based protein to minimize emissions, loss and waste 4. Recognising animal-based protein as (...)
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  49. Circularities In The Contemporary Philosophical Accounts Of The Applicability Of Mathematics In The Physical Universe.Catalin Barboianu - 2015 - Revista de Filosofie 61 (5):517-542.
    Contemporary philosophical accounts of the applicability of mathematics in physical sciences and the empirical world are based on formalized relations between the mathematical structures and the physical systems they are supposed to represent within the models. Such relations were constructed both to ensure an adequate representation and to allow a justification of the validity of the mathematical models as means of scientific inference. This article puts in evidence the various circularities (logical, epistemic, and of definition) that are present (...)
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  50. Reasoning, Science, and the Ghost Hunt.W. John Koolage & Timothy Hansel - 2017 - Teaching Philosophy 40 (2):201-229.
    This paper details how ghost hunting, as a set of learning activities, can be used to enhance critical thinking and philosophy of science classes. We describe in some detail our own work with ghost hunting, and reflect on both intended and unintended consequences of this pedagogical choice. This choice was partly motivated by students’ lack of familiarity with science and philosophic questions about it. We offer reflections on our three different implementations of the ghost hunting activities. In addition, we discuss (...)
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