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  1. Montague Reduction, Confirmation, and the Syntax-Semantics Relation.Stephan Hartmann & Kristina Liefke - manuscript
    Intertheoretic relations are an important topic in the philosophy of science. However, since their classical discussion by Ernest Nagel, such relations have mostly been restricted to relations between pairs of theories in the natural sciences. In this paper, we present a model of a new type of intertheoretic relation, called 'Montague Reduction', which is assumed in Montague's framework for the analysis and interpretation of natural language syntax. To motivate the adoption of our new model, we show that this model extends (...)
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  2. An Even Better Solution to the Paradox of the Ravens.James Hawthorne & Branden Fitelson - manuscript
    Think of confirmation in the context of the Ravens Paradox this way. The likelihood ratio measure of incremental confirmation gives us, for an observed Black Raven and for an observed non-Black non-Raven, respectively, the following “full” likelihood ratios.
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  3. Counterfactuals and the 'Grue-Speaker'.Alfred Schramm - manuscript
    Freitag (2015) and Schramm (2014) have proposed different, although converging, solutions of Goodman’s New Riddle of Induction. Answering their proposals, Dorst (2016 and 2018) has used the fictitious character of a ‘grue-speaker’ as his principal device for criticizing counterfactual-based treatments of the Riddle. In this paper, I argue that Dorst’s arguments fail: On the observation of no other than green emeralds, the ‘grue-speaker’ cannot use the symmetry between the ‘green’- and ‘grue’-languages for claiming ‘grue’- instead of ‘green’-evidence, and the counterfactuals (...)
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  4. Why Confirm Laws?Barry Ward - manuscript
    We argue that a particular approach to satisfying the broad predictive ambitions of the sciences demands law confirmation. On this approach we confirm non-nomic generalizations by confirming there are no actually realized ways of causing disconfirming cases. This gives causal generalizations a crucial role in prediction. We then show how rational judgements of relevant causal similarity can be used to confirm that causal generalizations themselves have no actual disconfirmers, providing a distinctive and clearly viable methodology for inductively confirming them. Finally, (...)
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  5. Fragmentation and Old Evidence.Will Fleisher - forthcoming - Episteme:1-26.
    Bayesian confirmation theory is our best formal framework for describing inductive reasoning. The problem of old evidence is a particularly difficult one for confirmation theory, because it suggests that this framework fails to account for central and important cases of inductive reasoning and scientific inference. I show that we can appeal to the fragmentation of doxastic states to solve this problem for confirmation theory. This fragmentation solution is independently well-motivated because of the success of fragmentation in solving other problems. I (...)
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  6. Is an Increase in Probability Always an Increase in Evidential Support?Artūrs Logins - forthcoming - Erkenntnis:1-25.
    Peter Achinstein has argued at length and on many occasions that the view according to which evidential support is defined in terms of probability-raising faces serious counterexamples and, hence, should be abandoned. Proponents of the positive probabilistic relevance view have remained unconvinced. The debate seems to be in a deadlock. This paper is an attempt to move the debate forward and revisit some of the central claims within this debate. My conclusion here will be that while Achinstein may be right (...)
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  7. A Conflict Between Indexical Credal Transparency & Relevance Confirmation.Joel Pust - forthcoming - Philosophy of Science.
    According to the probabilistic relevance account of confirmation, E confirms H relative to background knowledge K just in case P(H/K&E) > P(H/K). This requires an inequality between the rational degree of belief in H determined relative to two bodies of total knowledge which are such that one (K&E) includes the other (K) as a proper part. In this paper, I argue that it is quite plausible that there are no two possible bodies of total knowledge for ideally rational agents meeting (...)
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  8. Statistical Inference and the Replication Crisis.Lincoln J. Colling & Dénes Szűcs - 2021 - Review of Philosophy and Psychology 12 (1):121-147.
    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 hypotheses. (...)
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  9. 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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  10. Confirmation Based on Analogical Inference: Bayes Meets Jeffrey.Christian J. Feldbacher-Escamilla & Alexander Gebharter - 2020 - Canadian Journal of Philosophy 50 (2):174-194.
    Certain hypotheses cannot be directly confirmed for theoretical, practical, or moral reasons. For some of these hypotheses, however, there might be a workaround: confirmation based on analogical reasoning. In this paper we take up Dardashti, Hartmann, Thébault, and Winsberg’s (in press) idea of analyzing confirmation based on analogical inference Baysian style. We identify three types of confirmation by analogy and show that Dardashti et al.’s approach can cover two of them. We then highlight possible problems with their model as a (...)
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  11. Channels’ Confirmation and Predictions’ Confirmation: From the Medical Test to the Raven Paradox.Chenguang Lu - 2020 - Entropy 22 (4):384.
    After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple proposed the Raven Paradox. Then, Carnap used the increment of logical probability as the confirmation measure. So far, many confirmation measures have been proposed. Measure F proposed by Kemeny and Oppenheim among them possesses symmetries and asymmetries proposed by Elles and Fitelson, monotonicity proposed by Greco et al., and normalizing property suggested by many researchers. Based on the (...)
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  12. Hypothesis Testing in Scientific Practice: An Empirical Study.Moti Mizrahi - 2020 - International Studies in the Philosophy of Science 33 (1):1-21.
    It is generally accepted among philosophers of science that hypothesis testing is a key methodological feature of science. As far as philosophical theories of confirmation are con...
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  13. What is epistemically wrong with research affected by sponsorship bias? The evidential account.Alexander Reutlinger - 2020 - European Journal for Philosophy of Science 10 (2):1-26.
    Biased research occurs frequently in the sciences. In this paper, I will focus on one particular kind of biased research: research that is subject to sponsorship bias. I will address the following epistemological question: what precisely is epistemically wrong with biased research of this kind? I will defend the evidential account of epistemic wrongness: that is, research affected by sponsorship bias is epistemically wrong if and only if the researchers in question make false claims about the evidential support of some (...)
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  14. The Pursuit of Knowledge and the Problem of the Unconceived Alternatives.Fabio Sterpetti & Marta Bertolaso - 2020 - Topoi 39 (4):881-892.
    In the process of scientific discovery, knowledge ampliation is pursued by means of non-deductive inferences. When ampliative reasoning is performed, probabilities cannot be assigned objectively. One of the reasons is that we face the problem of the unconceived alternatives: we are unable to explore the space of all the possible alternatives to a given hypothesis, because we do not know how this space is shaped. So, if we want to adequately account for the process of knowledge ampliation, we need to (...)
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  15. Simulation Validation From a Bayesian Perspective.Claus Beisbart - 2019 - In Claus Beisbart & Nicole J. Saam (eds.), Computer Simulation Validation - Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Springer. pp. 173-201.
    Bayesian epistemologyEpistemology offers a powerful framework for characterizing scientific inference. Its basic idea is that rational belief comes in degrees that can be measured in terms of probabilities. The axioms of the probability calculus and a rule for updatingUpdating emerge as constraints on the formation of rational belief. Bayesian epistemologyEpistemology has led to useful explications of notions such asConfirmation confirmation. It thus is natural to ask whether Bayesian epistemologyEpistemology offers a useful framework for thinking about the inferences implicit in the (...)
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  16. Uncertainty Quantification Using Multiple Models - Prospects and Challenges.Reto Knutti, Christoph Baumberger & Gertrude Hirsch Hadorn - 2019 - In Claus Beisbart & Nicole J. Saam (eds.), Computer Simulation Validation: Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives. Cham, Switzerland: pp. 835-855.
    Model evaluation for long term climate predictions must be done on quantities other than the actual prediction, and a comprehensive uncertainty quantification is impossible. An ad hoc alternative is provided by coordinated model intercomparisons which typically use a “one model one vote” approach. The problem with such an approach is that it treats all models as independent and equally plausible. Reweighting all models of the ensemble for performance and dependence seems like an obvious way to improve on model democracy, yet (...)
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  17. Discrimination-Conduciveness and Observation Selection Effects.William Roche & Elliott Sober - 2019 - Philosophers' Imprint 19:1-26.
    We conceptualize observation selection effects (OSEs) by considering how a shift from one process of observation to another affects discrimination-conduciveness, by which we mean the degree to which possible observations discriminate between hypotheses, given the observation process at work. OSEs in this sense come in degrees and are causal, where the cause is the shift in process, and the effect is a change in degree of discrimination-conduciveness. We contrast our understanding of OSEs with others that have appeared in the literature. (...)
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  18. Quine and the Incoherence of the Indispensability Argument.Michael J. Shaffer - 2019 - Logos and Episteme 10 (2):207-213.
    It is an under-appreciated fact that Quine's rejection of the analytic/synthetic distinction, when coupled with some other plausible and related views, implies that there are serious difficulties in demarcating empirical theories from pure mathematical theories within the Quinean framework. This is a serious problem because there seems to be a principled difference between the two disciplines that cannot apparently be captured in the orthodox Quienan framework. For the purpose of simplicity let us call this Quine's problem of demarcation. In this (...)
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  19. Mystery and the Evidential Impact of Unexplainables.Matteo Colombo & Dominik Klein - 2018 - Episteme 15 (4):463-475.
    How should the information that a proposition p is a mystery impact your credence in p? To answer this question, we first provide a taxonomy of mysteries; then, we develop a test to distinguish two types of mysteries. When faced with mysteries of the first type, rational epistemic agents should lower their credence in p upon learning that p is a mystery. The same information should not impact agents’ credence in p, when they face mysteries of the second type. Our (...)
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  20. Why Experiments Matter.Adrian Currie & Arnon Levy - 2018 - Inquiry: An Interdisciplinary Journal of Philosophy (9-10):1-25.
    Traditionally, experimentation is considered a privileged means of confirmation. However, how experiments are a better confirmatory source than other strategies is unclear, and recent discussions have identified experiments with various modeling strategies on the one hand, and with ‘natural’ experiments on the other hand. We argue that experiments aiming to test theories are best understood as controlled investigations of specimens. ‘Control’ involves repeated, fine-grained causal manipulation of focal properties. This capacity generates rich knowledge of the object investigated. ‘Specimenhood’ involves possessing (...)
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  21. Sir John F. W. Herschel and Charles Darwin: Nineteenth-Century Science and Its Methodology.Charles H. Pence - 2018 - Hopos: The Journal of the International Society for the History of Philosophy of Science 8 (1):108-140.
    There are a bewildering variety of claims connecting Darwin to nineteenth-century philosophy of science—including to Herschel, Whewell, Lyell, German Romanticism, Comte, and others. I argue here that Herschel’s influence on Darwin is undeniable. The form of this influence, however, is often misunderstood. Darwin was not merely taking the concept of “analogy” from Herschel, nor was he combining such an analogy with a consilience as argued for by Whewell. On the contrary, Darwin’s Origin is written in precisely the manner that one (...)
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  22. Is There a Place in Bayesian Confirmation Theory for the Reverse Matthew Effect?William Roche - 2018 - Synthese 195 (4):1631-1648.
    Bayesian confirmation theory is rife with confirmation measures. Many of them differ from each other in important respects. It turns out, though, that all the standard confirmation measures in the literature run counter to the so-called “Reverse Matthew Effect” (“RME” for short). Suppose, to illustrate, that H1 and H2 are equally successful in predicting E in that p(E | H1)/p(E) = p(E | H2)/p(E) > 1. Suppose, further, that initially H1 is less probable than H2 in that p(H1) < p(H2). (...)
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  23. Is Evidence of Evidence Evidence? Screening-Off Vs. No-Defeaters.Roche William - 2018 - Episteme 15 (4):451-462.
    I argue elsewhere (Roche 2014) that evidence of evidence is evidence under screening-off. Tal and Comesaña (2017) argue that my appeal to screening-off is subject to two objections. They then propose an evidence of evidence thesis involving the notion of a defeater. There is much to learn from their very careful discussion. I argue, though, that their objections fail and that their evidence of evidence thesis is open to counterexample.
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  24. What Is Bayesian Confirmation For?Darren Bradley - 2017 - International Studies in the Philosophy of Science 31 (3):229-241.
    Peter Brössel and Franz Huber in 2015 argued that the Bayesian concept of confirmation had no use. I will argue that it has both the uses they discussed—it can be used for making claims about how worthy of belief various hypotheses are, and it can be used to measure the epistemic value of experiments. Furthermore, it can be useful in explanations. More generally, I will argue that more coarse-grained concepts can be useful, even when we have more fine-grained concepts available.
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  25. How Explanation Guides Confirmation.Nevin Climenhaga - 2017 - Philosophy of Science 84 (2):359-68.
    Where E is the proposition that [If H and O were true, H would explain O], William Roche and Elliot Sober have argued that P(H|O&E) = P(H|O). In this paper I argue that not only is this equality not generally true, it is false in the very kinds of cases that Roche and Sober focus on, involving frequency data. In fact, in such cases O raises the probability of H only given that there is an explanatory connection between them.
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  26. Generalized Confirmation and Relevance Measures.Vincenzo Crupi - 2017 - In M. Massimi, Jan-Willem Romeijn & G. Schurz (eds.), EPSA15 Selected Papers: The 5th conference of the European Philosophy of Science Association in Düsseldorf. Springer. pp. 285-295.
    The main point of the paper is to show how popular probabilistic measures of incremental confirmation and statistical relevance with qualitatively different features can be embedded smoothly in generalized parametric families. In particular, I will show that the probability difference, log probability ratio, log likelihood ratio, odds difference, so-called improbability difference, and Gaifman’s measures of confirmation can all be subsumed within a convenient biparametric continuum. One intermediate step of this project may have interest on its own, as it provides a (...)
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  27. Inductive Explanation and Garber–Style Solutions to the Problem of Old Evidence.David Kinney - 2017 - Synthese:1-15.
    The Problem of Old Evidence is a perennial issue for Bayesian confirmation theory. Garber famously argues that the problem can be solved by conditionalizing on the proposition that a hypothesis deductively implies the existence of the old evidence. In recent work, Hartmann and Fitelson :712–717, 2015) and Sprenger :383–401, 2015) aim for similar, but more general, solutions to the Problem of Old Evidence. These solutions are more general because they allow the explanatory relationship between a new hypothesis and old evidence (...)
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  28. A Condition for Transitivity in High Probability.William Roche - 2017 - European Journal for Philosophy of Science 7 (3):435-444.
    There are many scientific and everyday cases where each of Pr and Pr is high and it seems that Pr is high. But high probability is not transitive and so it might be in such cases that each of Pr and Pr is high and in fact Pr is not high. There is no issue in the special case where the following condition, which I call “C1”, holds: H 1 entails H 2. This condition is sufficient for transitivity in high (...)
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  29. Confirmation, Increase in Probability, and the Likelihood Ratio Measure: A Reply to Glass and McCartney.William Roche - 2017 - Acta Analytica 32 (4):491-513.
    Bayesian confirmation theory is rife with confirmation measures. Zalabardo focuses on the probability difference measure, the probability ratio measure, the likelihood difference measure, and the likelihood ratio measure. He argues that the likelihood ratio measure is adequate, but each of the other three measures is not. He argues for this by setting out three adequacy conditions on confirmation measures and arguing in effect that all of them are met by the likelihood ratio measure but not by any of the other (...)
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  30. Is Explanatoriness a Guide to Confirmation? A Reply to Climenhaga.William Roche & Elliott Sober - 2017 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 48 (4):581-590.
    We argued that explanatoriness is evidentially irrelevant in the following sense: Let H be a hypothesis, O an observation, and E the proposition that H would explain O if H and O were true. Then our claim is that Pr = Pr. We defended this screening-off thesis by discussing an example concerning smoking and cancer. Climenhaga argues that SOT is mistaken because it delivers the wrong verdict about a slightly different smoking-and-cancer case. He also considers a variant of SOT, called (...)
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  31. Novelty Versus Replicability: Virtues and Vices in the Reward System of Science.Felipe Romero - 2017 - Philosophy of Science 84 (5):1031-1043.
    The reward system of science is the priority rule. The first scientist making a new discovery is rewarded with prestige, while second runners get little or nothing. Michael Strevens, following Philip Kitcher, defends this reward system, arguing that it incentivizes an efficient division of cognitive labor. I argue that this assessment depends on strong implicit assumptions about the replicability of findings. I question these assumptions on the basis of metascientific evidence and argue that the priority rule systematically discourages replication. My (...)
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  32. Three Arguments for Absolute Outcome Measures.Jan Sprenger & Jacob Stegenga - 2017 - Philosophy of Science 84 (5):840-852.
    Data from medical research are typically summarized with various types of outcome measures. We present three arguments in favor of absolute over relative outcome measures. The first argument is from cognitive bias: relative measures promote the reference class fallacy and the overestimation of treatment effectiveness. The second argument is decision-theoretic: absolute measures are superior to relative measures for making a decision between interventions. The third argument is causal: interpreted as measures of causal strength, absolute measures satisfy a set of desirable (...)
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  33. Significance Testing, P-Values and the Principle of Total Evidence.Bengt Autzen - 2016 - European Journal for Philosophy of Science 6 (2):281-295.
    The paper examines the claim that significance testing violates the Principle of Total Evidence. I argue that p-values violate PTE for two-sided tests but satisfy PTE for one-sided tests invoking a sufficient test statistic independent of the preferred theory of evidence. While the focus of the paper is to evaluate a particular claim about the relationship of significance testing and PTE, I clarify the reading of this methodological principle along the way.
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  34. Sober as a Judge: Elliott Sober: Ockham’s Razors: A User’s Manual. Cambridge: Cambridge University Press, 322pp, $29.99 , $99.99.Gordon Belot - 2016 - Metascience 25 (3):387-392.
    In Ockham's Razors: A User's Guide, Elliott Sober argues that parsimony considerations are epistemically relevant on the grounds that certain methods of model selection, such as the Akaike Information Criterion, exhibit good asymptotic behaviour and take the number of adjustable parameters in a model into account. I raise some worries about this form of argument.
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  35. Explanatory Judgment, Probability, and Abductive Inference.Matteo Colombo, Marie Postma & Jan Sprenger - 2016 - In A. Papafragou, D. Grodner, D. Mirman & J. C. Trueswell (eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 432-437) Cognitive Science Society. Austin, TX: Cognitive Science Society. pp. 432-437.
    Abductive reasoning assigns special status to the explanatory power of a hypothesis. But how do people make explanatory judgments? Our study clarifies this issue by asking: How does the explanatory power of a hypothesis cohere with other cognitive factors? How does probabilistic information affect explanatory judgments? In order to answer these questions, we conducted an experiment with 671 participants. Their task was to make judgments about a potentially explanatory hypothesis and its cognitive virtues. In the responses, we isolated three constructs: (...)
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  36. Understanding (With) Toy Models.Alexander Reutlinger, Dominik Hangleiter & Stephan Hartmann - 2016 - British Journal for the Philosophy of Science:axx005.
    Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models is that it is an unsettled question what the epistemic goal of toy modeling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this paper is to (...)
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  37. On the Correct Interpretation of P Values and the Importance of Random Variables.Guillaume Rochefort-Maranda - 2016 - Synthese 193 (6):1777-1793.
    The p value is the probability under the null hypothesis of obtaining an experimental result that is at least as extreme as the one that we have actually obtained. That probability plays a crucial role in frequentist statistical inferences. But if we take the word ‘extreme’ to mean ‘improbable’, then we can show that this type of inference can be very problematic. In this paper, I argue that it is a mistake to make such an interpretation. Under minimal assumptions about (...)
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  38. Can the Behavioral Sciences Self-Correct? A Social Epistemic Study.Felipe Romero - 2016 - Studies in History and Philosophy of Science Part A 60:55-69.
    Advocates of the self-corrective thesis argue that scientific method will refute false theories and find closer approximations to the truth in the long run. I discuss a contemporary interpretation of this thesis in terms of frequentist statistics in the context of the behavioral sciences. First, I identify experimental replications and systematic aggregation of evidence (meta-analysis) as the self-corrective mechanism. Then, I present a computer simulation study of scientific communities that implement this mechanism to argue that frequentist statistics may converge upon (...)
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  39. Keynes’s Coefficient of Dependence Revisited.Peter Brössel - 2015 - Erkenntnis 80 (3):521-553.
    Probabilistic dependence and independence are among the key concepts of Bayesian epistemology. This paper focuses on the study of one specific quantitative notion of probabilistic dependence. More specifically, section 1 introduces Keynes’s coefficient of dependence and shows how it is related to pivotal aspects of scientific reasoning such as confirmation, coherence, the explanatory and unificatory power of theories, and the diversity of evidence. The intimate connection between Keynes’s coefficient of dependence and scientific reasoning raises the question of how Keynes’s coefficient (...)
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  40. 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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  41. Reference Models: Using Models to Turn Data Into Evidence.Teru Miyake - 2015 - Philosophy of Science 82 (5):822-832.
    Reference models of the earth’s interior play an important role in the acquisition of knowledge about the earth’s interior and the earth as a whole. Such models are used as a sort of standard reference against which data are compared. I argue that the use of reference models merits more attention than it has gotten so far in the literature on models, for it is an example of a method of doing science that has a long and significant history, and (...)
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  42. Reichenbach on Causality in 1923: Scientific Inference, Coordination, and Confirmation.Flavia Padovani - 2015 - Studies in History and Philosophy of Science Part A 53:3-11.
  43. Confirmation Versus Falsificationism.Ray Scott Percival - 2015 - In Robin L. Cautin & Scott O. Lilienfeld (eds.), Encyclopedia of Clinical Psychology.
    Confirmation and falsification are different strategies for testing theories and characterizing the outcomes of those tests. Roughly speaking, confirmation is the act of using evidence or reason to verify or certify that a statement is true, definite, or approximately true, whereas falsification is the act of classifying a statement as false in the light of observation reports. After expounding the intellectual history behind confirmation and falsificationism, reaching back to Plato and Aristotle, I survey some of the main controversial issues and (...)
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  44. Model Tuning in Engineering: Uncovering the Logic.Katie Steele & Charlotte Werndl - 2015 - Journal of Strain Analysis for Engineering Design 51 (1):63-71.
    In engineering, as in other scientific fields, researchers seek to confirm their models with real-world data. It is common practice to assess models in terms of the distance between the model outputs and the corresponding experimental observations. An important question that arises is whether the model should then be ‘tuned’, in the sense of estimating the values of free parameters to get a better fit with the data, and furthermore whether the tuned model can be confirmed with the same data (...)
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  45. Anthropomorphism, Anthropectomy, and the Null Hypothesis.Kristin Andrews & Brian Huss - 2014 - Biology and Philosophy 29 (5):711-729.
    We examine the claim that the methodology of psychology leads to a bias in animal cognition research against attributing “anthropomorphic” properties to animals . This charge is examined in light of a debate on the role of folk psychology between primatologists who emphasize similarities between humans and other apes, and those who emphasize differences. We argue that while in practice there is sometimes bias, either in the formulation of the null hypothesis or in the preference of Type-II errors over Type-I (...)
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  46. Neuroeconomics and Confirmation Theory.Christopher Clarke - 2014 - Philosophy of Science 81 (2):195-215.
    Neuroeconomics is a research programme founded on the thesis that cognitive and neurobiological data constitute evidence for answering economic questions. I employ confirmation theory in order to reject arguments both for and against neuroeconomics. I also emphasize that some arguments for neuroeconomics will not convince the skeptics because these arguments make a contentious assumption: economics aims for predictions and deep explanations of choices in general. I then argue for neuroeconomics by appealing to a much more restrictive (and thereby skeptic-friendly) characterization (...)
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  47. Darwin’s Principle: The Use of Contrastive Reasoning in the Confirmation of Evolution.Cornelius Hunter - 2014 - Hopos: The Journal of the International Society for the History of Philosophy of Science 4 (1):106-149.
  48. Model Change and Reliability in Scientific Inference.Erich Kummerfeld & David Danks - 2014 - Synthese 191 (12):2673-2693.
    One persistent challenge in scientific practice is that the structure of the world can be unstable: changes in the broader context can alter which model of a phenomenon is preferred, all without any overt signal. Scientific discovery becomes much harder when we have a moving target, and the resulting incorrect understandings of relationships in the world can have significant real-world and practical consequences. In this paper, we argue that it is common (in certain sciences) to have changes of context that (...)
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  49. The Dangers of Pragmatic Virtue.Daniel Nolan - 2014 - Inquiry: An Interdisciplinary Journal of Philosophy 57 (5-6):623-644.
    Many people want to hold that some theoretical virtues—simplicity, elegance, familiarity or others—are only pragmatic virtues. That is, these features do not give us any more reason to think a theory is true, or close to true, but they justify choosing one theoretical option over another because they are desirable for some other, practical purpose. Using pragmatic virtues in theory choice apparently brings with it a dilemma: if we are deciding what to accept on the basis of considerations that are (...)
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  50. Confirmation Measures and Collaborative Belief Updating.Ilho Park - 2014 - Synthese 191 (16):3955-3975.
    There are some candidates that have been thought to measure the degree to which evidence incrementally confirms a hypothesis. This paper provides an argument for one candidate—the log-likelihood ratio measure. For this purpose, I will suggest a plausible requirement that I call the Requirement of Collaboration. And then, it will be shown that, of various candidates, only the log-likelihood ratio measure \(l\) satisfies this requirement. Using this result, Jeffrey conditionalization will be reformulated so as to disclose explicitly what determines new (...)
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