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  1. Werner Arber, N. Cabibbo & Marcelo Sánchez Sorondo (eds.) (2008). The Proceedings of the Plenary Session on Predictability in Science: Accuracy and Limitations: 3-6 November 2006. Pontifical Academy of Sciences.
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  2. Greg Bamford (1999). What is the Problem of Ad Hoc Hypotheses? Science and Education 8 (4):375 - 86..
    The received view of an ad hochypothesis is that it accounts for only the observation(s) it was designed to account for, and so non-ad hocness is generally held to be necessary or important for an introduced hypothesis or modification to a theory. Attempts by Popper and several others to convincingly explicate this view, however, prove to be unsuccessful or of doubtful value, and familiar and firmer criteria for evaluating the hypotheses or modified theories so classified are characteristically available. These points (...)
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  3. Vadim Batitsky & Zoltan Domotor (2007). When Good Theories Make Bad Predictions. Synthese 157 (1):79 - 103.
    Chaos-related obstructions to predictability have been used to challenge accounts of theory validation based on the agreement between theoretical predictions and experimental data (Rueger & Sharp, 1996. The British Journal for the Philosophy of Science, 47, 93–112; Koperski, 1998. Philosophy of Science, 40, 194–212). These challenges are incomplete in two respects: (a) they do not show that chaotic regimes are unpredictable in principle (i.e., with unbounded resources) and, as a result, that there is something conceptually wrong with idealized expectations of (...)
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  4. Gregor Betz (2011). Prediction. In Ian Jarvie & Jesus Zamora-Bonilla (eds.), Handbook of Philosophy of Social Science. Sage.
  5. Gregor Betz (2010). What’s the Worst Case? The Methodology of Possibilistic Prediction. Analyse and Kritik 32 (1):87-106.
    Frank Knight (1921) famously distinguished the epistemic modes of certainty, risk, and uncertainty in order to characterize situations where deterministic, probabilistic or possibilistic foreknowledge is available. Because our probabilistic knowledge is limited, i.e. because many systems, e.g. the global climate, cannot be described and predicted probabilistically in a reliable way, Knight's third category, possibilistic foreknowledge, is not simply swept by the probabilistic mode. This raises the question how to justify possibilistic predictionsincluding the identication of the worst case. The development of (...)
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  6. Gregor Betz (2009). Underdetermination, Model-Ensembles and Surprises: On the Epistemology of Scenario-Analysis in Climatology. [REVIEW] Journal for General Philosophy of Science 40 (1):3 - 21.
    As climate policy decisions are decisions under uncertainty, being based on a range of future climate change scenarios, it becomes a crucial question how to set up this scenario range. Failing to comply with the precautionary principle, the scenario methodology widely used in the Third Assessment Report of the International Panel on Climate Change (IPCC) seems to violate international environmental law, in particular a provision of the United Nations Framework Convention on Climate Change. To place climate policy advice on a (...)
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  7. Gregor Betz (2009). What Range of Future Scenarios Should Climate Policy Be Based On? Modal Falsificationism and its Limitations. Philosophia Naturalis 46 (1):133-158.
    Climate policy decisions are decisions under uncertainty and are, therefore, based on a range of future climate scenarios, describing possible consequences of alternative policies. Accordingly, the methodology for setting up such a scenario range becomes pivotal in climate policy advice. The preferred methodology of the Intergovernmental Panel on Climate Change will be characterised as ,,modal verificationism"; it suffers from severe shortcomings which disqualify it for scientific policy advice. Modal falsificationism, as a more sound alternative, would radically alter the way the (...)
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  8. Michael Bishop (2002). Years of Successful Predictive Modeling Should Be Enough: Lessons for Philosophy of Science. Philosophy of Science 69 (S3):S197-S208.
    Our aim in this paper is to bring the woefully neglected literature on predictive modeling to bear on some central questions in the philosophy of science. The lesson of this literature is straightforward: For a very wide range of prediction problems, statistical prediction rules (SPRs), often rules that are very easy to implement, make predictions than are as reliable as, and typically more reliable than, human experts. We will argue that the success of SPRs forces us to reconsider our views (...)
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  9. Seamus Bradley, Scientific Uncertainty: A User's Guide. Grantham Institute on Climate Change Discussion Paper.
    There are different kinds of uncertainty. I outline some of the various ways that uncertainty enters science, focusing on uncertainty in climate science and weather prediction. I then show how we cope with some of these sources of error through sophisticated modelling techniques. I show how we maintain confidence in the face of error.
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  10. Nancy Cartwright & Jacob Stegenga (2011). A Theory of Evidence for Evidence-Based Policy. In Philip Dawid, William Twining & Mimi Vasilaki (eds.), Evidence, Inference and Enquiry. Oup/British Academy. 291.
  11. C. E. Cleland (2011). Prediction and Explanation in Historical Natural Science. British Journal for the Philosophy of Science 62 (3):551-582.
    In earlier work ( Cleland [2001] , [2002]), I sketched an account of the structure and justification of ‘prototypical’ historical natural science that distinguishes it from ‘classical’ experimental science. This article expands upon this work, focusing upon the close connection between explanation and justification in the historical natural sciences. I argue that confirmation and disconfirmation in these fields depends primarily upon the explanatory (versus predictive or retrodictive) success or failure of hypotheses vis-à-vis empirical evidence. The account of historical explanation that (...)
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  12. Joachim L. Dagg (2003). Forgery: Prediction's Vile Twin. Science 302:783-784.
  13. Richard Dawid, Scientific Prediction and the Underdetermination of Scientific Theory Building.
    According to the no miracles argument, scientific realism provides the only satisfactory explanation of the predictive success of science. It is argued in the present article that a different explanatory strategy, based on the posit of limitations to the underdetermination of scientific theory building by the available empirical data, offers a more convincing understanding of scientific success.
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  14. Malcolm R. Forster (1994). Non-Bayesian Foundations for Statistical Estimation, Prediction, and the Ravens Example. Erkenntnis 40 (3):357 - 376.
    The paper provides a formal proof that efficient estimates of parameters, which vary as as little as possible when measurements are repeated, may be expected to provide more accurate predictions. The definition of predictive accuracy is motivated by the work of Akaike (1973). Surprisingly, the same explanation provides a novel solution for a well known problem for standard theories of scientific confirmation — the Ravens Paradox. This is significant in light of the fact that standard Bayesian analyses of the paradox (...)
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  15. Stephan Hartmann, Marcel Weber, Wenceslao Gonzalez, Dennis Dieks & Thomas Uebe (eds.) (forthcoming). Explanation, Prediction, and Confirmation: New Trends and Old Ones Reconsidered. Springer.
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  16. Spencer Phillips Hey (2014). Ethics and Epistemology of Accurate Prediction in Clinical Research. Journal of Medical Ethics 10:1-4.
    All major research ethics policies assert that the ethical review of clinical trial protocols should include a systematic assessment of risks and benefits. But despite this policy, protocols do not typically contain explicit probability statements about the likely risks or benefits involved in the proposed research. In this essay, I articulate a range of ethical and epistemic advantages that explicit forecasting would offer to the health research enterprise. I then consider how some particular confidence levels may come into conflict with (...)
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  17. Rami Israel (2009). A New View on Inductive Practices. VDM Verlag.
    The idea that reason can justify induction was famously criticized by David Hume. Hume concluded that there is no rational justification for inductive inferences and hence, no rational justification for most of our daily beliefs. Many philosophers attempted to solve Hume's problem with no success. Bertrand Russell commented regarding Hume's problem: "[if we cannot justify induction] we have no reason to expect the sun to rise tomorrow, to expect bread to be more nourishing than a stone, or to expect that (...)
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  18. B. J. Mason, Peter Mathias & J. H. Westcott (eds.) (1986). Predictability in Science and Society: A Joint Symposium of the Royal Society and the British Academy Held on 20 and 21 March 1986. [REVIEW] Distributed by Scholium International.
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  19. Darrell P. Rowbottom & R. McNeill Alexander (2012). The Role of Hypotheses in Biomechanical Research. Science in Context 25 (2):247-262.
    This paper investigates whether there is a discrepancy between the stated and actual aims in biomechanical research, particularly with respect to hypothesis testing. We present an analysis of one hundred papers recently published in The Journal of Experimental Biology and Journal of Biomechanics, and examine the prevalence of papers which (a) have hypothesis testing as a stated aim, (b) contain hypothesis testing claims that appear to be purely presentational (i.e. which seem not to have influenced the actual study), and (c) (...)
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  20. Scott Scheall, Lesser Degrees of Explanation: Some Implications of F.A. Hayek’s Methodology of Sciences of Complex Phenomena.
    From the early-1950s on, F.A. Hayek was concerned with the development of a methodology of sciences that study systems of complex phenomena. Hayek argued that the knowledge that can be acquired about such systems is, in virtue of their complexity (and the comparatively narrow boundaries of human cognitive faculties), relatively limited. The paper aims to elucidate the implications of Hayek’s methodology with respect to the specific dimensions along which the scientist’s knowledge of some complex phenomena may be limited. Hayek’s fallibilism (...)
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  21. Samuel Schindler (2013). Theory-Laden Experimentation. Studies in History and Philosophy of Science Part A 44 (1):89-.
    The thesis of theory-ladenness of observations, in its various guises, is widely considered as either ill-conceived or harmless to the rationality of science. The latter view rests partly on the work of the proponents of New Experimentalism who have argued, among other things, that experimental practices are efficient in guarding against any epistemological threat posed by theory-ladenness. In this paper I show that one can generate a thesis of theory-ladenness for experimental practices from an influential New Experimentalist account. The notion (...)
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  22. Aaron Sloman (1967). Predictive Policies: What Makes Some Policies Better Than Others? In Aristotelian Society Supplementary Volume. Blackwell-Wiley. 57 - 94.
    Response to "Predictive Policies" by R.S.McGowan Mr. McGowan has assumed that there is a clear distinction between inductive inferences and others, that we all know how to make the distinction, that we all agree that the inductive ones are somehow better or more reasonable than the alternatives, and I have criticised all of these assumptions. Further he hasformulated the philosophical problem of induction as the problem of showing why the inductive ones are better, and he has attempted to show that (...)
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  23. Bartlomiej Swiatczak (2011). Indeterminism in the Immune System: The Case of Somatic Hypermutation. Paradigmi 1:49-65.
    One of the fundamental questions of life sciences is one of whether there are genuinely random biological processes. An affirmative or negative answer to this question may have important methodological consequences. It appears that a number of biological processes are explicitly classified as random. One of them is the so-called somatic hypermutation. However, closer analysis of somatic hypermutation reveals that it is not a genuinely random process. Somatic hypermutation is called random because the exact outcome of this process is difficult (...)
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  24. Rasmus Grønfeldt Winther (2012). Mathematical Modeling in Biology: Philosophy and Pragmatics. Frontiers in Plant Evolution and Development 2012:1-3.
    Philosophy can shed light on mathematical modeling and the juxtaposition of modeling and empirical data. This paper explores three philosophical traditions of the structure of scientific theory—Syntactic, Semantic, and Pragmatic—to show that each illuminates mathematical modeling. The Pragmatic View identifies four critical functions of mathematical modeling: (1) unification of both models and data, (2) model fitting to data, (3) mechanism identification accounting for observation, and (4) prediction of future observations. Such facets are explored using a recent exchange between two groups (...)
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  25. Rasmus Grønfeldt Winther (2009). Prediction in Selectionist Evolutionary Theory. Philosophy of Science 76 (5):889-901.
    Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli and the origin of eukaryotic cells through endosymbiosis. †To contact the author, please write to: Philosophy Department, University of California, Santa Cruz, (...)
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  26. S. L. Zabell (1992). Predicting the Unpredictable. Synthese 90 (2):205 - 232.
    A major difficulty for currently existing theories of inductive inference involves the question of what to do when novel, unknown, or previously unsuspected phenomena occur. In this paper one particular instance of this difficulty is considered, the so-called sampling of species problem.The classical probabilistic theories of inductive inference due to Laplace, Johnson, de Finetti, and Carnap adopt a model of simple enumerative induction in which there are a prespecified number of types or species which may be observed. But, realistically, (...)
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