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Profile: Jan Sprenger (Tilburg University)
  1. Richard Dawid, Stephan Hartmann & Jan Sprenger (forthcoming). The No Alternatives Argument. British Journal for the Philosophy of Science:axt045.
    Scientific theories are hard to find, and once scientists have found a theory, H, they often believe that there are not many distinct alternatives to H. But is this belief justified? What should scientists believe about the number of alternatives to H, and how should they change these beliefs in the light of new evidence? These are some of the questions that we will address in this article. We also ask under which conditions failure to find an alternative to H (...)
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  2. Rogier De Langhe, Stephan Hartmann & Jan Sprenger (forthcoming). The Progress of Science. Studies in History and Philosophy of Science Part A.
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  3. Stephan Hartmann & Jan Sprenger (forthcoming). Bayesian Epistemology. In Duncan Pritchard & Sven Bernecker (eds.), Routledge Companion to Epistemology. Routledge.
    Bayesian epistemology addresses epistemological problems with the help of the mathematical theory of probability. It turns out that the probability calculus is especially suited to represent degrees of belief (credences) and to deal with questions of belief change, confirmation, evidence, justification, and coherence. Compared to the informal discussions in traditional epistemology, Bayesian epis- temology allows for a more precise and fine-grained analysis which takes the gradual aspects of these central epistemological notions into account. Bayesian epistemology therefore complements traditional epistemology; it (...)
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  4. Jan Sprenger (forthcoming). Stephan Hartmann, Carlo Martini. Episteme.
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  5. Cyrille Imbert, Ryan Muldoon, Jan Sprenger & Kevin Zollman (2014). Introduction, SI of Synthese “The Collective Dimension of Science”. Synthese 191 (1):1-2.
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  6. Carlo Martini, Jan Sprenger & Mark Colyvan (2014). Erratum To: Resolving Disagreement Through Mutual Respect. [REVIEW] Erkenntnis 79 (3):669-670.
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  7. Ryan Muldoon, Chiara Lisciandra, Mark Colyvan, Carlo Martini, Giacomo Sillari & Jan Sprenger (2014). Disagreement Behind the Veil of Ignorance. Philosophical Studies 170 (3):377-394.
    In this paper we argue that there is a kind of moral disagreement that survives the Rawlsian veil of ignorance. While a veil of ignorance eliminates sources of disagreement stemming from self-interest, it does not do anything to eliminate deeper sources of disagreement. These disagreements not only persist, but transform their structure once behind the veil of ignorance. We consider formal frameworks for exploring these differences in structure between interested and disinterested disagreement, and argue that consensus models offer us a (...)
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  8. Cecilia Nardini & Jan Sprenger (2014). Bias and Conditioning in Sequential Medical Trials. Philosophy of Science 80 (5):1053-1064.
    Randomized controlled trials are currently the gold standard within evidence-based medicine. Usually they are monitored for early signs of effectiveness or harm. However, evidence from trials stopped early is often charged with bias toward implausibly large effects. To our mind, this skeptical attitude is unfounded and caused by the failure to perform appropriate conditioning in the statistical analysis of the evidence. We contend that conditional hypothesis tests give a superior appreciation of the obtained evidence and significantly improve the practice of (...)
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  9. Jan Sprenger (2014). Testing a Precise Null Hypothesis: The Case of Lindley's Paradox. Philosophy of Science 80 (5):733-744.
    Testing a point null hypothesis is a classical but controversial issue in statistical methodology. A prominent illustration is Lindley’s Paradox, which emerges in hypothesis tests with large sample size and exposes a salient divergence between Bayesian and frequentist inference. A close analysis of the paradox reveals that both Bayesians and frequentists fail to satisfactorily resolve it. As an alternative, I suggest Bernardo’s Bayesian Reference Criterion: (i) it targets the predictive performance of the null hypothesis in future experiments; (ii) it provides (...)
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  10. Carlo Martini, Jan Sprenger & Mark Colyvan (2013). Resolving Disagreement Through Mutual Respect. Erkenntnis 78 (4):881-898.
    This paper explores the scope and limits of rational consensus through mutual respect, with the primary focus on the best known formal model of consensus: the Lehrer–Wagner model. We consider various arguments against the rationality of the Lehrer–Wagner model as a model of consensus about factual matters. We conclude that models such as this face problems in achieving rational consensus on disagreements about unknown factual matters, but that they hold considerable promise as models of how to rationally resolve non-factual disagreements.
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  11. Ryan Muldoon, Chiara Lisciandra, Cristina Bicchieri, Stephan Hartmann & Jan Sprenger (2013). On the Emergence of Descriptive Norms. Politics, Philosophy and Economics 13 (1):1470594-12447791.
    A descriptive norm is a behavioral rule that individuals follow when their empirical expectations of others following the same rule are met. We aim to provide an account of the emergence of descriptive norms by first looking at a simple case, that of the standing ovation. We examine the structure of a standing ovation, and show it can be generalized to describe the emergence of a wide range of descriptive norms.
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  12. Jan Sprenger (2013). A Synthesis of Hempelian and Hypothetico-Deductive Confirmation. Erkenntnis 78 (4):727-738.
    This paper synthesizes confirmation by instances and confirmation by successful predictions, and thereby the Hempelian and the hypothetico-deductive traditions in confirmation theory. The merger of these two approaches is subsequently extended to the piecemeal confirmation of entire theories. It is then argued that this synthetic account makes a useful contribution from both a historical and a systematic perspective.
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  13. Jan Sprenger (2013). The Role of Bayesian Philosophy Within Bayesian Model Selection. European Journal for Philosophy of Science 3 (1):101-114.
    Bayesian model selection has frequently been the focus of philosophical inquiry (e.g., Forster, Br J Philos Sci 46:399–424, 1995; Bandyopadhyay and Boik, Philos Sci 66:S390–S402, 1999; Dowe et al., Br J Philos Sci 58:709–754, 2007). This paper argues that Bayesian model selection procedures are very diverse in their inferential target and their justification, and substantiates this claim by means of case studies on three selected procedures: MML, BIC and DIC. Hence, there is no tight link between Bayesian model selection and (...)
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  14. Stephan Hartmann & Jan Sprenger (2012). Judgment Aggregation and the Problem of Tracking the Truth. Synthese 187 (1):209-221.
    The aggregation of consistent individual judgments on logically interconnected propositions into a collective judgment on those propositions has recently drawn much attention. Seemingly reasonable aggregation procedures, such as propositionwise majority voting, cannot ensure an equally consistent collective conclusion. The literature on judgment aggregation refers to that problem as the discursive dilemma. In this paper, we motivate that many groups do not only want to reach a factually right conclusion, but also want to correctly evaluate the reasons for that conclusion. In (...)
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  15. Stephan Hartmann & Jan Sprenger (2012). The Future of Philosophy of Science: Introduction. [REVIEW] European Journal for Philosophy of Science 2 (2):157-159.
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  16. Jan Sprenger (2012). A Unifying Framework of Probabilistic Reasoning. Metascience 21 (2):459-462.
    A unifying framework of probabilistic reasoning Content Type Journal Article Category Book Review Pages 1-4 DOI 10.1007/s11016-011-9573-x Authors Jan Sprenger, Tilburg Center for Logic and Philosophy of Science, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands Journal Metascience Online ISSN 1467-9981 Print ISSN 0815-0796.
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  17. Jan Sprenger (2012). Environmental Risk Analysis: Robustness Is Essential for Precaution. Philosophy of Science 79 (5):881-892.
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  18. Jonah N. Schupbach & Jan Sprenger (2011). The Logic of Explanatory Power. Philosophy of Science 78 (1):105-127.
    This article introduces and defends a probabilistic measure of the explanatory power that a particular explanans has over its explanandum. To this end, we propose several intuitive, formal conditions of adequacy for an account of explanatory power. Then, we show that these conditions are uniquely satisfied by one particular probabilistic function. We proceed to strengthen the case for this measure of explanatory power by proving several theorems, all of which show that this measure neatly corresponds to our explanatory intuitions. Finally, (...)
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  19. Jan Sprenger (2011). Hypothetico-Deductive Confirmation. Philosophy Compass 6 (7):497-508.
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  20. Jan Sprenger (2011). Science Without (Parametric) Models: The Case of Bootstrap Resampling. Synthese 180 (1):65 - 76.
    Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make (...)
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  21. Stephan Hartmann, Carlo Martini & Jan Sprenger (eds.) (2010). Formal Modeling in Social Epistemology. [REVIEW] Logic Journal of the IGPL (special issue).
    Special issue. With contributions by Rogier De Langhe and Matthias Greiff, Igor Douven and Alexander Riegler, Stephan Hartmann and Jan Sprenger, Carl Wagner, Paul Weirich, and Jesús Zamora Bonilla.
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  22. Stephan Hartmann, Gabriella Pigozzi & Jan Sprenger (2010). Reliable Methods of Judgment Aggregation. Journal for Logic and Computation 20:603--617.
    The aggregation of consistent individual judgments on logically interconnected propositions into a collective judgment on the same propositions has recently drawn much attention. Seemingly reasonable aggregation procedures, such as propositionwise majority voting, cannot ensure an equally consistent collective conclusion. The literature on judgment aggregation refers to such a problem as the \textit{discursive dilemma}. In this paper we assume that the decision which the group is trying to reach is factually right or wrong. Hence, we address the question of how good (...)
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  23. Stephan Hartmann & Jan Sprenger (2010). The Weight of Competence Under a Realistic Loss Function. Logic Journal of the IGPL 18:346-352.
    In many scientific, economic and policy-related problems, pieces of information from different sources have to be aggregated. Typically, the sources are not equally competent. This raises the question of how the relative weights and competences should be related to arrive at an optimal final verdict. Our paper addresses this question under a more realistic perspective of measuring the practical loss implied by an inaccurate verdict.
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  24. Jan Sprenger (2010). Probability, Rational Single-Case Decisions and the Monty Hall Problem. Synthese 174 (3):331 - 340.
    The application of probabilistic arguments to rational decisions in a single case is a contentious philosophical issue which arises in various contexts. Some authors (e.g. Horgan, Philos Pap 24:209–222, 1995; Levy, Synthese 158:139–151, 2007) affirm the normative force of probabilistic arguments in single cases while others (Baumann, Am Philos Q 42:71–79, 2005; Synthese 162:265–273, 2008) deny it. I demonstrate that both sides do not give convincing arguments for their case and propose a new account of the relationship between probabilistic reasoning (...)
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  25. Jan Sprenger (2010). Statistical Inference Without Frequentist Justifications. In. In M. Dorato M. Suàrez (ed.), Epsa Epistemology and Methodology of Science. Springer. 289--297.
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  26. Jan Sprenger (2009). Evidence and Experimental Design in Sequential Trials. Philosophy of Science 76 (5):637-649.
    To what extent does the design of statistical experiments, in particular sequential trials, affect their interpretation? Should postexperimental decisions depend on the observed data alone, or should they account for the used stopping rule? Bayesians and frequentists are apparently deadlocked in their controversy over these questions. To resolve the deadlock, I suggest a three‐part strategy that combines conceptual, methodological, and decision‐theoretic arguments. This approach maintains the pre‐experimental relevance of experimental design (...)
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  27. Jan Sprenger, Carlo Martini & Stephan Hartmann (2009). Consensual Decision-Making Among Epistemic Peers. Episteme 6 (2):110-129.
    This paper focuses on the question of how to resolve disagreement and uses the Lehrer-Wagner model as a formal tool for investigating consensual decision-making. The main result consists in a general definition of when agents treat each other as epistemic peers (Kelly 2005; Elga 2007), and a theorem vindicating the “equal weight view” to resolve disagreement among epistemic peers. We apply our findings to an analysis of the impact of social network structures on group deliberation processes, and we demonstrate their (...)
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  28. Jan Sprenger, Falsificationist Confirmation.
    Existing accounts of hypothetico-deductive confirmation are able to circumvent the classical objections (e.g. the tacking problems), but the confirmation of conjunctions of hypotheses brings them into trouble. Therefore this paper develops a new, falsificationist account of qualitative confirmation by means of Ken Gemes' theory of content parts. The new approach combines the hypothetico-deductive view with falsificationist and instance confirmation principles. It is considerably simpler than the previous suggestions and gives a better treatment of conjunctive hypotheses while solving the tacking (...)
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  29. Jan Sprenger, Surprise and Evidence in Statistical Model Checking.
    There is considerable confusion about the role of p-values in statistical model checking. To clarify that point, I introduce the distinction between measures of surprise and measures of evidence which come with different epistemological functions. I argue that p-values, often understood as measures of evidence against a null model, do not count as proper measures of evidence and are closer to measures of surprise. Finally, I sketch how the problem of old evidence may be tackled by acknowledging the epistemic role (...)
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