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  1. The Proceedings of the Plenary Session on Predictability in Science: Accuracy and Limitations: 3-6 November 2006.Werner Arber, N. Cabibbo & Marcelo Sánchez Sorondo (eds.) - 2008 - Pontifical Academy of Sciences.
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  2. What is the Problem of Ad Hoc Hypotheses?Greg Bamford - 1999 - 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. When Good Theories Make Bad Predictions.Vadim Batitsky & Zoltan Domotor - 2007 - 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. These challenges are incomplete in two respects: they do not show that chaotic regimes are unpredictable in principle and, as a result, that there is something conceptually wrong with idealized expectations of correct predictions from acceptable theories, and they do not explore whether chaos-induced predictive failures of deterministic models can be remedied by stochastic modeling. In this paper (...)
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  4. 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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  5. Mehr Besonnenheit, bitte! Über Prognosegrenzen und Politikberatung.Gregor Betz - 2011 - Ökologisches Wirtschaften 2011 (2):35-38.
    In einer Welt, in der der Umgang mit Komplexität und Unsicherheit an Bedeutung gewinnt, sind politische Entscheidungsträger immer stärker auf eine wissenschaftliche Beratung angewiesen. Trotz des Bedarfs der politischen Akteure nach konkreten Handlungsempfehlungen sollte seriöse Politikberatung die grundlegenden Werte wissenschaftlichen Arbeitens nicht aus den Augen verlieren.
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  6. Prediction.Gregor Betz - 2011 - In Ian Jarvie & Jesus Zamora-Bonilla (eds.), Handbook of Philosophy of Social Science. Sage Publications.
    Predictive success as an aim of science -- On the very possibility of prediction in the social sciences -- Empirical facts about social prediction: its mode, object and performance -- Understanding poor forecast performance.
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  7. What’s the Worst Case? The Methodology of Possibilistic Prediction.Gregor Betz - 2010 - Analyse & 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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  8. Underdetermination, Model-Ensembles and Surprises: On the Epistemology of Scenario-Analysis in Climatology.Gregor Betz - 2009 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 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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  9. What Range of Future Scenarios Should Climate Policy Be Based On? Modal Falsificationism and its Limitations.Gregor Betz - 2009 - 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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  10. Years of Successful Predictive Modeling Should Be Enough: Lessons for Philosophy of Science.Michael Bishop - 2002 - 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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  11. Scientific Uncertainty: A User's Guide.Seamus Bradley - 2012 - 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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  12. An Introduction to Experimentation.Brian Joseph Brinkworth - 1968 - New York: American Elsevier Pub. Co..
  13. A Theory of Evidence for Evidence-Based Policy.Nancy Cartwright & Jacob Stegenga - 2011 - In Philip Dawid, William Twining & Mimi Vasilaki (eds.), Evidence, Inference and Enquiry. Oup/British Academy. pp. 291.
    WE AIM HERE to outline a theory of evidence for use. More specifically we lay foundations for a guide for the use of evidence in predicting policy effectiveness in situ, a more comprehensive guide than current standard offerings, such as the Maryland rules in criminology, the weight of evidence scheme of the International Agency for Research on Cancer (IARC), or the US ‘What Works Clearinghouse’. The guide itself is meant to be well-grounded but at the same time to give practicable (...)
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  14. Beyond Belief Randomness, Prediction and Explanation in Science.J. L. Casti, Anders Karlqvist & Sweden - 1991
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  15. Prediction and Explanation in Historical Natural Science.C. E. Cleland - 2011 - 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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  16. Forgery: Prediction's Vile Twin.Joachim L. Dagg - 2003 - Science 302:783-784.
  17. Scientific Prediction and the Underdetermination of Scientific Theory Building.Richard Dawid - unknown
    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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  18. Therapeutic Inferences for Individual Patients.Luis J. Flores - 2015 - Journal of Evaluation in Clinical Practice 21 (3):440-447.
    RATIONALE, AIMS AND OBJECTIVES: Increased awareness of the gap between controlled research and medical practice has raised concerns over whether the special attention of doctors to probability estimates from clinical trials really improves the care of individuals. Evidence-based medicine has acknowledged that research results are not applicable to all kinds of patients, and consequently, has attempted to overcome this limitation by introducing improvements in the design and analysis of clinical trials. METHODS: A clinical case is used to highlight the premises (...)
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  19. Non-Bayesian Foundations for Statistical Estimation, Prediction, and the Ravens Example.Malcolm R. Forster - 1994 - 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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  20. Prediction in Epidemiology and Medicine.Jonathan Fuller, Alex Broadbent & Luis J. Flores - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 54:45-48.
  21. The Risk GP Model: The Standard Model of Prediction in Medicine.Jonathan Fuller & Luis J. Flores - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 54:49-61.
    With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated to a target (...)
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  22. On the Neural Enrichment of Economic Models: Recasting the Challenge.Roberto Fumagalli - 2017 - Biology and Philosophy 32 (2):201-220.
    In a recent article in this Journal, Fumagalli argues that economists are provisionally justified in resisting prominent calls to integrate neural variables into economic models of choice. In other articles, various authors engage with Fumagalli’s argument and try to substantiate three often-made claims concerning neuroeconomic modelling. First, the benefits derivable from neurally informing some economic models of choice do not involve significant tractability costs. Second, neuroeconomic modelling is best understood within Marr’s three-level of analysis framework for information-processing systems. And third, (...)
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  23. Reichenbach's Concept of Prediction.J. González Wenceslao - 1995 - International Studies in the Philosophy of Science 9 (1):37-58.
    Abstract Reichenbach emphasizes the central importance of prediction, which is?for him?the principal aim of science. This paper offers a critical reconstruction of his concept of prediction, taking into account the different periods of his thought. First, prediction is studied as a key factor in rejecting the positivism of the Vienna Circle. This part of the discussion concentres on the general features of prediction before Experience and Prediction (EP) (section 1). Second, prediction is considered in the context of Reichenbach's disagreements with (...)
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  24. Economic Prediction and Human Activity. An Analysis of Prediction in Economics From Action Theory.W. Gonzalez - 1994 - Epistemologia 17 (2):253-294.
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  25. Explanation, Prediction, and Confirmation in the Social Sciences: Realm and Limits" (University of Amsterdam, 26-27 October 2009): Workshop of the Program "The Philosophy of Science in a European Perspective. [REVIEW]Wenceslao J. Gonzalez - 2010 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 41 (2):389 - 394.
  26. Explanation and Prediction in the Labour Process Theory.Richard Douglas Gordon - 1990 - Dissertation, The University of British Columbia (Canada)
    The view that large-scale, long-range social theories cannot be predictive other than "in principle" is sufficiently widespread as to be considered the orthodox view. It is widely held that, lacking this predictive quality, social theories are cut off from a crucial form of vindication enjoyed by the experimental sciences. Thus many would agree with Ryan's assessment that while with regard to large-scale social changes "long-range prediction is not in principle impossible," nonetheless as a matter of practical methodology such a goal (...)
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  27. Explanation, Prediction, and Confirmation: New Trends and Old Ones Reconsidered.Stephan Hartmann, Marcel Weber, Wenceslao Gonzalez, Dennis Dieks & Thomas Uebe (eds.) - forthcoming - Springer.
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  28. Ethics and Epistemology of Accurate Prediction in Clinical Research.Spencer Phillips Hey - 2015 - Journal of Medical Ethics 41 (7):559-562.
    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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  29. Do We Know Whether Researchers and Reviewers Are Estimating Risk and Benefit Accurately?Spencer Phillips Hey & Jonathan Kimmelman - 2016 - Bioethics 30 (8):609-617.
    Accurate estimation of risk and benefit is integral to good clinical research planning, ethical review, and study implementation. Some commentators have argued that various actors in clinical research systems are prone to biased or arbitrary risk/benefit estimation. In this commentary, we suggest the evidence supporting such claims is very limited. Most prior work has imputed risk/benefit beliefs based on past behavior or goals, rather than directly measuring them. We describe an approach – forecast analysis – that would enable direct and (...)
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  30. Reflections on Predictive Processing and the Mind. An Interview.Jakob Hohwy - 2015 - Avant: Trends in Interdisciplinary Studies (3):145-152.
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  31. A New View on Inductive Practices.Rami Israel - 2009 - 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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  32. The Curious Case of the Self-Refuting Straw Man: Trafimow and Earp’s Response to Klein (2014).Stan Klein - 2016 - Theory and Psychology 26:549– 556.
    In their critique of Klein (2014a), Trafimow and Earp present two theses. First, they argue that, contra Klein, a well-specified theory is not a necessary condition for successful replication. Second, they contend that even when there is a well-specified theory, replication depends more on auxiliary assumptions than on theory proper. I take issue with both claims, arguing that (a) their first thesis confuses a material conditional (what I said) with a modal claim (T&E’s misreading of what I said), and (b) (...)
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  33. A More Fulfilling (and Frustrating) Take on Reflexive Predictions.Matthew Kopec - 2011 - Philosophy of Science 78 (5):1249-1259.
    Even though social scientists continue to discuss the problems posed by self-fulfilling and self-frustrating predictions, philosophers of science have ignored the topic since the 1970s. Back then, the prevailing view was that the methodological problems posed by reflexive predictions are either minor or easily avoided. I believe that this consensus was premature, ultimately relying on an overly narrow understanding of the phenomenon. I present an improved way to understand reflexive predictions (framed in probabilistic terms) and show that, once such predictions (...)
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  34. Zur Verteidigung Einiger Hempelscher Thesen Gegen Kritiken Stegmüllers.Michael Küttner - 1985 - Erkenntnis 22 (1-3):475 - 484.
    The aim of this paper is to defend some of C. G. Hempel's basic theses concerning the logic of explanation and prediction against criticisms recently made by W. Stegmüller. It is argued (very concisely) thatthese is no need for essentially pragmatic conditions in DN-arguments;only the structural identity sub-thesis “Every adequate prediction is ... an adequate explanation” can be held instead of the one Hempel has in mind;the notion of the ambiguity of probabilistic explanations should be reformulated;there is no need for (...)
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  35. Howson and Franklin on Prediction.Patrick Maher - 1993 - Philosophy of Science 60 (2):329-340.
    Evidence for a hypothesis typically confirms the hypothesis more if the evidence was predicted than if it was accommodated. Or so I argued in previous papers, where I also developed an analysis of why this should be so. But this was all a mistake if Howson and Franklin (1991) are to be believed. In this paper, I show why they are not to be believed. I also identify a grain of truth that may have been dimly grasped by those Bayesians (...)
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  36. Predictability in Science and Society: A Joint Symposium of the Royal Society and the British Academy Held on 20 and 21 March 1986. [REVIEW]B. J. Mason, Peter Mathias & J. H. Westcott (eds.) - 1986 - Scholium International.
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  37. Numerical Weather Prediction.Sir John Mason - 1986 - In B. J. Mason, Peter Mathias & J. H. Westcott (eds.), Predictability in Science and Society: A Joint Symposium of the Royal Society and the British Academy Held on 20 and 21 March 1986. Scholium International.
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  38. On Embodiment in Predictions. A Book Review. [REVIEW]Przemysław Nowakowski - 2015 - Avant: Trends in Interdisciplinary Studies (3):155-159.
  39. Space-Time Dimension Problem as a Stumbling Block of Inflationary Cosmology.Rinat M. Nugayev - 2013 - In Vadim V. Kazutinsky, Elena A. Mamchur, Alexandre D. Panov & V. D. Erekaev (eds.), Metauniverse,Space,Time. Institute of Philosophy of RAS. pp. 52-73.
    It is taken for granted that the explanation of the Universe’s space-time dimension belongs to the host of the arguments that exhibit the superiority of modern (inflationary) cosmology over the standard model. In the present paper some doubts are expressed . They are based upon the fact superstring theory is too formal to represent genuine unification of general relativity and quantum field theory. Neveretheless, the fact cannot exclude the opportunity that in future the superstring theory can become more physical. Hence (...)
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  40. Experience and Prediction.Hans Reichenbach - 1938 - University of Chicago Press.
  41. Scientific Innovation and the Limits of Social Scientific Prediction.Alex Rosenberg - 1993 - Synthese 97 (2):161 - 181.
    Philosophers and historians of philosophy have come to recognize that at the core of logical positivism was an attachment to prediction as the necessary condition for scientific knowledge.1 The inheritors of their tradition, especially the Bayesians among us, continue to seek a theory of confirmation that reflects this epistemic commitment. The importance of prediction in the growth of scientific knowledge is a commitment I share with the positivists, so I do not blanch at that designation, much less employ it as (...)
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  42. The Role of Hypotheses in Biomechanical Research.Darrell P. Rowbottom & R. McNeill Alexander - 2012 - 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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  43. Lesser Degrees of Explanation: Some Implications of F.A. Hayek’s Methodology of Sciences of Complex Phenomena.Scott Scheall - manuscript
    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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  44. Theory-Laden Experimentation.Samuel Schindler - 2013 - Studies in History and Philosophy of Science Part A 44 (1):89-101.
    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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  45. Predictive Policies: What Makes Some Policies Better Than Others?Aaron Sloman - 1967 - In Aristotelian Society Supplementary Volume. Blackwell-Wiley. pp. 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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  46. Indeterminism in the Immune System: The Case of Somatic Hypermutation.Bartlomiej Swiatczak - 2011 - 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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  47. Mathematical Modeling in Biology: Philosophy and Pragmatics.Rasmus Grønfeldt Winther - 2012 - 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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  48. Prediction in Selectionist Evolutionary Theory.Rasmus Grønfeldt Winther - 2009 - 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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  49. Prediction and Accommodation Revisited.John Worrall - 2014 - Studies in History and Philosophy of Science Part A 45 (1):54-61.
    The paper presents a further articulation and defence of the view on prediction and accommodation that I have proposed earlier. It operates by analysing two accounts of the issue—by Patrick Maher and by Marc Lange—that, at least at first sight, appear to be rivals to my own. Maher claims that the time-order of theory and evidence may be important in terms of degree of confirmation, while that claim is explicitly denied in my account. I argue, however, that when his account (...)
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  50. Predicting the Unpredictable.S. L. Zabell - 1992 - 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, this (...)
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