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  1. Technological Revolutions and the Problem of Prediction.Nick Bostrom - forthcoming - Nanoethics: The Ethical and Social Implications of Nanotechnology. Wiley-Interscience, Hoboken, Nj.
  2. The Fate of Explanatory Reasoning in the Age of Big Data.Frank Cabrera - forthcoming - Philosophy and Technology:1-21.
    In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In section 3, I argue that these methodological claims are in tension with a (...)
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  3. Correlation Isn’T Good Enough: Causal Explanation and Big Data. [REVIEW]Frank Cabrera - forthcoming - Metascience:1-4.
    A review of Gary Smith and Jay Cordes: The Phantom Pattern Problem: The Mirage of Big Data. New York: Oxford University Press, 2020.
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  4. The Past of Predicting the Future: A Review of the Multidisciplinary History of Affective Forecasting.Maya A. Pilin - forthcoming - History of the Human Sciences:095269512097633.
    Affective forecasting refers to the ability to predict future emotions, a skill that is essential to making decisions on a daily basis. Studies of the concept have determined that individuals are often inaccurate in making such affective forecasts. However, the mechanisms of these errors are not yet clear. In order to better understand why affective forecasting errors occur, this article seeks to trace the theoretical roots of this theory with a focus on its multidisciplinary history. The roots of affective forecasting (...)
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  5. The Unvirtuous Prediction of the Pessimistic Induction.Seungbae Park - 2021 - Filosofija. Sociologija 32 (3):TBD.
    The pessimist predicts that future scientific theories will replace present scientific theories. However, she does not specify when the predicted events will take place, so we do not have the opportunity to blame her for having made a false prediction, although we might have the chance to praise her for having made a true prediction. Her prediction contrasts with the astronomer’s prediction. The astronomer specifies when the next solar eclipse will happen, so we have both the chance to blame her (...)
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  6. How to Interpret Covid-19 Predictions: Reassessing the IHME’s Model.S. Andrew Schroeder - 2021 - Philosophy of Medicine 1 (2).
    The IHME Covid-19 prediction model has been one of the most influential Covid models in the United States. Early on, it received heavy criticism for understating the extent of the epidemic. I argue that this criticism was based on a misunderstanding of the model. The model was best interpreted not as attempting to forecast the actual course of the epidemic. Rather, it was attempting to make a conditional projection: telling us how the epidemic would unfold, given certain assumptions. This misunderstanding (...)
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  7. Evidence and Explanation in Cicero's On Divination.Frank Cabrera - 2020 - Studies in History and Philosophy of Science Part A 82:34-43.
    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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  8. The Value of Imprecise Prediction.Alkistis Elliott-Graves - 2020 - Philosophy Theory and Practice in Biology 4 (12).
    The traditional philosophy of science approach to prediction leaves little room for appreciating the value and potential of imprecise predictions. At best, they are considered a stepping stone to more precise predictions, while at worst they are viewed as detracting from the scientific quality of a discipline. The aim of this paper is to show that imprecise predictions are undervalued in philosophy of science. I review the conceptions of imprecise predictions and the main criticisms levelled against them: (i) that they (...)
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  9. Philosophical Perspectives on Earth System Modeling: Truth, Adequacy and Understanding.G. Gramelsberger, J. Lenhard & Wendy Parker - 2020 - Journal of Advances in Modeling Earth Systems 12 (1):e2019MS001720.
    We explore three questions about Earth system modeling that are of both scientific and philosophical interest: What kind of understanding can be gained via complex Earth system models? How can the limits of understanding be bypassed or managed? How should the task of evaluating Earth system models be conceptualized?
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  10. Why is Information Retrieval a Scientific Discipline?Robert W. P. Luk - 2020 - Foundations of Science:1-27.
    It is relatively easy to state that information retrieval is a scientific discipline but it is rather difficult to understand why it is science because what is science is still under debate in the philosophy of science. To be able to convince others that IR is science, our ability to explain why is crucial. To explain why IR is a scientific discipline, we use a theory and a model of scientific study, which were proposed recently. The explanation involves mapping the (...)
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  11. Big Data and Prediction: Four Case Studies.Robert Northcott - 2020 - Studies in History and Philosophy of Science Part A 81:96-104.
    Has the rise of data-intensive science, or ‘big data’, revolutionized our ability to predict? Does it imply a new priority for prediction over causal understanding, and a diminished role for theory and human experts? I examine four important cases where prediction is desirable: political elections, the weather, GDP, and the results of interventions suggested by economic experiments. These cases suggest caution. Although big data methods are indeed very useful sometimes, in this paper’s cases they improve predictions either limitedly or not (...)
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  12. What Do Implicit Measures Measure?Michael Brownstein, Alex Madva & Bertram Gawronski - 2019 - WIREs Cognitive Science:1-13.
    We identify several ongoing debates related to implicit measures, surveying prominent views and considerations in each debate. First, we summarize the debate regarding whether performance on implicit measures is explained by conscious or unconscious representations. Second, we discuss the cognitive structure of the operative constructs: are they associatively or propositionally structured? Third, we review debates whether performance on implicit measures reflects traits or states. Fourth, we discuss the question of whether a person’s performance on an implicit measure reflects characteristics of (...)
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  13. The Future of Predictive Ecology.Alkistis Elliott-Graves - 2019 - Philosophical Topics 47 (1):65-82.
    Prediction is an important aspect of scientific practice, because it helps us to confirm theories and effectively intervene on the systems we are investigating. In ecology, prediction is a controversial topic: even though the number of papers focusing on prediction is constantly increasing, many ecologists believe that the quality of ecological predictions is unacceptably low, in the sense that they are not sufficiently accurate sufficiently often. Moreover, ecologists disagree on how predictions can be improved. On one side are the ‘theory-driven’ (...)
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  14. What to Make of Mendeleev’s Predictions?K. Wray - 2019 - Foundations of Chemistry 21 (2):139-143.
    I critically examine Stewart’s suggestion that we should weigh the various predictions Mendeleev made differently. I argue that in his effort to justify discounting the weight of some of Mendeleev’s failures, Stewart invokes a principle that will, in turn, reduce the weight of some of the successful predictions Mendeleev made. So Stewart’s strategy will not necessarily lead to a net gain in Mendeleev’s favor.
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  15. What to Make of Mendeleev’s Predictions?K. Brad Wray - 2019 - Foundations of Chemistry 21 (2):139-143.
    I critically examine Stewart’s suggestion that we should weigh the various predictions Mendeleev made differently. I argue that in his effort to justify discounting the weight of some of Mendeleev’s failures, Stewart invokes a principle that will, in turn, reduce the weight of some of the successful predictions Mendeleev made. So Stewart’s strategy will not necessarily lead to a net gain in Mendeleev’s favor.
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  16. To Explain or to Predict: Which One is Mandatory?Robert Luk - 2018 - Foundations of Science 23 (2):411-414.
    Recently, Luk mentioned that scientific knowledge both explains and predicts. Do these two functions of scientific knowledge have equal significance, or is one of the two functions more important than the other? This commentary explains why prediction may be mandatory but explanation may be only desirable and optional.
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  17. Twierdzenie Bayesa w projektowaniu strategii diagnostycznych w medycynie.Tomasz Rzepiński - 2018 - Diametros 57:39-60.
    The paper will compare two methods used in the design of diagnostic strategies. The first one is a method that precises predictive value of diagnostic tests. The second one is based on the use of Bayes’ theorem. The main aim of this article is to identify the epistemological assumptions underlying both of these methods. For the purpose of this objective, example projects of one and multi-stage diagnostic strategy developed using both methods will be considered.
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  18. Prediction in General Relativity.C. McCoy - 2017 - Synthese 194 (2):491-509.
    Several authors have claimed that prediction is essentially impossible in the general theory of relativity, the case being particularly strong, it is said, when one fully considers the epistemic predicament of the observer. Each of these claims rests on the support of an underdetermination argument and a particular interpretation of the concept of prediction. I argue that these underdetermination arguments fail and depend on an implausible explication of prediction in the theory. The technical results adduced in these arguments can be (...)
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  19. When Does HARKing Hurt? Identifying When Different Types of Undisclosed Post Hoc Hypothesizing Harm Scientific Progress.Mark Rubin - 2017 - Review of General Psychology 21:308-320.
    Hypothesizing after the results are known, or HARKing, occurs when researchers check their research results and then add or remove hypotheses on the basis of those results without acknowledging this process in their research report (Kerr, 1998). In the present article, I discuss three forms of HARKing: (1) using current results to construct post hoc hypotheses that are then reported as if they were a priori hypotheses; (2) retrieving hypotheses from a post hoc literature search and reporting them as a (...)
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  20. 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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  21. 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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  22. 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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  23. Therapeutic Inferences for Individual Patients.Luis 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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  24. 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.
  25. 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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  26. Prediction and Novel Facts in the Methodology of Scientific Research Programs.Wenceslao J. Gonzalez - 2015 - In Philosophico-Methodological Analysis of Prediction and its Role in Economics. Springer Verlag. pp. 103-124.
    In the methodology of scientific research programs (MSRP) there are important features on the problem of prediction, especially regarding novel facts. In his approach, Imre Lakatos proposed three different levels on prediction: aim, process, and assessment. Chapter 5 pays attention to the characterization of prediction in the methodology of research programs. Thus, it takes into account several features: (1) its pragmatic characterization, (2) the logical perspective as a proposition, (3) the epistemological component, (4) its role in the appraisal of research (...)
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  27. 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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  28. On Embodiment in Predictions. A Book Review. [REVIEW]Przemysław Nowakowski - 2015 - Avant: Trends in Interdisciplinary Studies (3):155-159.
  29. Lesser Degrees of Explanation: Some Implications of F.A. Hayek’s Methodology of Sciences of Complex Phenomena.Scott Scheall - 2015 - Erasmus Journal for Philosophy and Economics 8 (1):42-60.
    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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  30. Reflections on Predictive Processing and the Mind. An Interview.Jakob Hohwy - 2014 - Avant: Trends in Interdisciplinary Studies (3):145-152.
  31. Introducing Knowledge-Based Medicine - Conference Presentation - Medicine is Not Science: Guessing the Future, Predicting the Past.Clifford Miller - 2014 - Conference Presentation Universidad Franscisco de Vitoria Person Centered Medicine July 2014; 07/2014.
    There is a middle ground of imperfect knowledge in fields like medicine and the social sciences. It stands between our day-to-day relatively certain knowledge obtained from ordinary basic observation of regularities in our world and our knowledge from well-validated theories in the physical sciences. -/- The latter enable reliable prediction a great deal of the time of the happening of events never before experienced. The former enable prediction only of what has happened before and beyond that of educated guesses which (...)
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  32. 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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  33. State of the Field: Why Novel Prediction Matters.Heather Douglas & P. D. Magnus - 2013 - Studies in History and Philosophy of Science Part A 44 (4):580-589.
    There is considerable disagreement about the epistemic value of novel predictive success, i.e. when a scientist predicts an unexpected phenomenon, experiments are conducted, and the prediction proves to be accurate. We survey the field on this question, noting both fully articulated views such as weak and strong predictivism, and more nascent views, such as pluralist reasons for the instrumental value of prediction. By examining the various reasons offered for the value of prediction across a range of inferential contexts , we (...)
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  34. Review: Cornelis Menke: Zum Methodologischen Wert von Vorhersagen . Paderborn: Mentis, 2009, 188 Pages. [REVIEW]Christian J. Feldbacher - 2013 - Kriterion - Journal of Philosophy 27 (1):53-64.
  35. Divination and Scientific Prediction.Alexander Fidora - 2013 - Early Science and Medicine 18 (6):517-535.
    Starting with a survey of the terminology that was used to describe the epistemological status of the mantic arts during the Middle Ages, this article focuses on the connections between the theoretical assumptions of these arts and of other prognostic disciplines of the time. While during the thirteenth century, mantic disciplines, medicine and meteorology were classified altogether as conjectural sciences that were all based on the interpretation of signs, during the fourteenth century, a more differentiated model of scientific prediction developed (...)
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  36. How Simulations Fail.Patrick Grim, Robert Rosenberger, Adam Rosenfeld, Brian Anderson & Robb E. Eason - 2013 - Synthese 190 (12):2367-2390.
    ‘The problem with simulations is that they are doomed to succeed.’ So runs a common criticism of simulations—that they can be used to ‘prove’ anything and are thus of little or no scientific value. While this particular objection represents a minority view, especially among those who work with simulations in a scientific context, it raises a difficult question: what standards should we use to differentiate a simulation that fails from one that succeeds? In this paper we build on a structural (...)
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  37. 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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  38. 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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  39. 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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  40. The Pumpkin or the Tiger? Michael Polanyi, Frederick Soddy, and Anticipating Emerging Technologies.David H. Guston - 2012 - Minerva 50 (3):363-379.
    Imagine putting together a jigsaw puzzle that works like the board game in the movie “Jumanji”: When you finish, whatever the puzzle portrays becomes real. The children playing “Jumanji” learn to prepare for the reality that emerges from the next throw of the dice. But how would this work for the puzzle of scientific research? How do you prepare for unlocking the secrets of the atom, or assembling from the bottom-up nanotechnologies with unforeseen properties – especially when completion of such (...)
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  41. The Role of Hypotheses in Biomechanical Research.Darrell Patrick Rowbottom & R. Mcneill Alexander - 2012 - Science in Context 25 (2):247-262.
    This paper investigates whether there is a discrepancy between 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 have hypothesis testing as a stated aim, contain hypothesis testing claims that appear to be purely presentational, and have exploration as a stated aim. We found that whereas no papers had exploration (...)
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  42. 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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  43. 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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  44. 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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  45. 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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  46. Prediction and Explanation in Historical Natural Science.Carol 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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  47. Eric Christian Barnes: The Paradox of Predictivism. [REVIEW]David Harker - 2011 - British Journal for the Philosophy of Science 62 (1):219-223.
  48. Review of Eric Barnes' The Paradox of Predictivism. [REVIEW]David Harker - 2011 - British Journal for the Philosophy of Science 62 (1):219-223.
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  49. Explanation, Prediction, and Confirmation.Stephan Hartmann, Marcel Weber, Wenceslao Gonzalez, Dennis Dieks & Thomas Uebe (eds.) - 2011 - Berlin: Springer.
    This volume, the second in the Springer series Philosophy of Science in a European Perspective, contains selected papers from the workshops organised by the ESF Research Networking Programme PSE (The Philosophy of Science in a European Perspective) in 2009. Five general topics are addressed: 1. Formal Methods in the Philosophy of Science; 2. Philosophy of the Natural and Life Sciences; 3. Philosophy of the Cultural and Social Sciences; 4. Philosophy of the Physical Sciences; 5. History of the Philosophy of Science. (...)
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  50. 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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