Search results for 'Hartmann Stephan' (try it on Scholar)

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  1.  5
    Luc Bovens & Stephan Hartmann (2007). Special Issue on Bayesian Epistemology Edited by L. Bovens and S. Hartmann. Synthese 156 (3).
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  2.  13
    Ulrich Gähde & Stephan Hartmann (2005). Coherence, Truth and Testimony. Erkenntnis 63 (3).
    Special issue. With contributions by Luc Bovens and Stephan Hartmann, David Glass, Keith Lehrer, Erik Olsson, Tomoji Shogenji, Mark Siebel, and Paul Thagard.
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  3. 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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  4.  23
    Luc Bovens & Stephan Hartmann (2003). Bayesian Epistemology. Oxford: Oxford University Press.
    Bovens and Hartmann provide a systematic guide to the use of probabilistic methods not just in epistemology, but also in philosophy of science, voting theory, ...
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  5.  39
    Luc Bovens & Stephan Hartmann (eds.) (2004). Bayesian Epistemology. OUP Oxford.
    Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information (...)
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  6.  78
    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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  7. Luc Bovens & Stephan Hartmann (2004). Bayesian Epistemology. Oxford University Press Uk.
    Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information (...)
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  8. Stephan Hartmann & Matteo Colombo (forthcoming). Bayesian Cognitive Science, Unification and Explanation. British Journal for the Philosophy of Science:axv036.
    It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be (...)
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  9. Stephan Hartmann & Luc Bovens (2005). Why There Cannot Be a Single Probabilistic Measure of Coherence. Erkenntnis 63 (3):361-374.
    Bayesian Coherence Theory of Justification or, for short, Bayesian Coherentism, is characterized by two theses, viz. (i) that our degree of confidence in the content of a set of propositions is positively affected by the coherence of the set, and (ii) that coherence can be characterized in probabilistic terms. There has been a longstanding question of how to construct a measure of coherence. We will show that Bayesian Coherentism cannot rest on a single measure of coherence, but requires a vector (...)
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  10.  34
    Radin Dardashti, Stephan Hartmann, Karim P. Y. Thebault & Eric Winsberg, Confirmation Via Analogue Simulation: A Bayesian Analysis.
    Analogue simulation is a novel mode of scientific inference found increasingly within modern physics, and yet all but neglected in the philosophical literature. Experiments conducted upon a table-top ‘source system’ are taken to provide insight into features of an inaccessible ‘target system’, based upon a syntactic isomorphism between the relevant modelling frameworks. An important example is the use of acoustic ‘dumb hole’ systems to simulate gravitational black holes. In a recent paper it was argued that there exists circumstances in which (...)
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  11. Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann (2010). Who's Afraid of Nagelian Reduction? Erkenntnis 73 (3):393-412.
    We reconsider the Nagelian theory of reduction and argue that, contrary to a widely held view, it is the right analysis of intertheoretic reduction, since the alleged difficulties of the theory either vanish upon closer inspection or turn out to be substantive philosophical questions rather than knock-down arguments.
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  12.  24
    Richard Dawid & Stephan Hartmann, The No Miracles Argument Without the Base Rate Fallacy.
    According to an argument by Colin Howson, the no-miracles argument is contingent on committing the base-rate fallacy and is therefore bound to fail. We demonstrate that Howson's argument only applies to one of two versions of the no-miracles argument. The other, more considerate version is not adequately reconstructed in Howson's approach and thus remains unaffected by his line of reasoning. We provide a Bayesian reconstruction of this version of the no-miracles argument and show that it is valid. We then proceed (...)
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  13.  55
    Stephan Hartmann (forthcoming). Imprecise Probabilities in Quantum Mechanics. In Colleen E. Crangle, Adolfo García de la Sienra & Helen E. Longino (eds.), Foundations and Methods from Mathematics to Neuroscience. CSLI Publications
  14.  51
    Stephan Hartmann & Kristina Liefke, Montague Reduction, Confirmation, and the Syntax-Semantics Relation.
    Intertheoretic relations are an important topic in the philosophy of science. However, since their classical discussion by Ernest Nagel, such relations have mostly been restricted to relations between pairs of theories in the natural sciences. In this paper, we present a model of a new type of intertheoretic relation, called 'Montague Reduction', which is assumed in Montague's framework for the analysis and interpretation of natural language syntax. To motivate the adoption of our new model, we show that this model extends (...)
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  15.  61
    Richard Dawid, Stephan Hartmann & Jan Sprenger (2015). The No Alternatives Argument. British Journal for the Philosophy of Science 66 (1):213-234.
    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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  16. Luc Bovens & Stephan Hartmann (2003). ``Solving the Riddle of Coherence". Mind 112 (448):601-634.
    A coherent story is a story that fits together well. This notion plays a central role in the coherence theory of justification and has been proposed as a criterion for scientific theory choice. Many attempts have been made to give a probabilistic account of this notion. A proper account of coherence must not start from some partial intuitions, but should pay attention to the role that this notion is supposed to play within a particular context. Coherence is a property of (...)
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  17. Alan Hájek & Stephan Hartmann (2010). Bayesian Epistemology. In J. Dancy et al (ed.), A Companion to Epistemology. Blackwell
    Bayesianism is our leading theory of uncertainty. Epistemology is defined as the theory of knowledge. So “Bayesian Epistemology” may sound like an oxymoron. Bayesianism, after all, studies the properties and dynamics of degrees of belief, understood to be probabilities. Traditional epistemology, on the other hand, places the singularly non-probabilistic notion of knowledge at centre stage, and to the extent that it traffics in belief, that notion does not come in degrees. So how can there be a Bayesian epistemology?
     
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  18.  39
    Luc Bovens & Stephan Hartmann (2003). Solving the Riddle of Coherence. Mind 112 (448):601-633.
    A coherent story is a story that fits together well. This notion plays a central role in the coherence theory of justification and has been proposed as a criterion for scientific theory choice. Many attempts have been made to give a probabilistic account of this notion. A proper account of coherence must not start from some partial intuitions, but should pay attention to the role that this notion is supposed to play within a particular context. Coherence is a property of (...)
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  19.  91
    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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  20. Stephan Hartmann (1996). The World as a Process: Simulations in the Natural and Social Sciences. In Rainer Hegselmann (ed.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View.
    Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...)
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  21.  41
    Matteo Colombo, Stephan Hartmann & Robert van Iersel (2015). Models, Mechanisms, and Coherence. British Journal for the Philosophy of Science 66 (1):181-212.
    Life-science phenomena are often explained by specifying the mechanisms that bring them about. The new mechanistic philosophers have done much to substantiate this claim and to provide us with a better understanding of what mechanisms are and how they explain. Although there is disagreement among current mechanists on various issues, they share a common core position and a seeming commitment to some form of scientific realism. But is such a commitment necessary? Is it the best way to go about mechanistic (...)
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  22. Luc Bovens & Stephan Hartmann (2002). Bayesian Networks and the Problem of Unreliable Instruments. Philosophy of Science 69 (1):29-72.
    We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments. In particular, we consider (i) repeated measurements of a single test consequence of the hypothesis, (ii) measurements of multiple test consequences of the hypothesis, (iii) theoretical support for the reliability of the instrument, and (iv) calibration procedures. We evaluate these strategies on their relative merits under idealized conditions and show some surprising (...)
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  23.  54
    Stephan Hartmann (1999). Models and Stories in Hadron Physics. In Margaret Morrison & Mary Morgan (eds.), Models as Mediators. 52--326.
    Fundamental theories are hard to come by. But even if we had them, they would be too complicated to apply. Quantum chromodynamics is a case in point. This theory is supposed to govern all strong interactions, but it is extremely hard to apply and test at energies where protons, neutrons and ions are the effective degrees of freedom. Instead, scientists typically use highly idealized models such as the MIT Bag Model or the Nambu Jona-Lasinio Model to account for phenomena in (...)
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  24.  68
    Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann (2011). Confirmation and Reduction: A Bayesian Account. Synthese 179 (2):321 - 338.
    Various scientific theories stand in a reductive relation to each other. In a recent article, we have argued that a generalized version of the Nagel-Schaffner model (GNS) is the right account of this relation. In this article, we present a Bayesian analysis of how GNS impacts on confirmation. We formalize the relation between the reducing and the reduced theory before and after the reduction using Bayesian networks, and thereby show that, post-reduction, the two theories are confirmatory of each other. We (...)
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  25.  27
    Luc Bovens & Stephan Hartmann (2005). Why There Cannot Be a Single Probabilistic Measure of Coherence. Erkenntnis 63 (3):361-374.
    Bayesian Coherence Theory of Justification or, for short, Bayesian Coherentism, is characterized by two theses, viz. (i) that our degree of confidence in the content of a set of propositions is positively affected by the coherence of the set, and (ii) that coherence can be characterized in probabilistic terms. There has been a longstanding question of how to construct a measure of coherence. We will show that Bayesian Coherentism cannot rest on a single measure of coherence, but requires a vector (...)
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  26.  40
    Stephan Hartmann & Luc Bovens (2003). Solving the Riddle of Coherence. Mind 112 (448):601-634.
    A coherent story is a story that fits together well. This notion plays a central role in the coherence theory of justification and has been proposed as a criterion for scientific theory choice. Many attempts have been made to give a probabilistic account of this notion. A proper account of coherence must not start from some partial intuitions, but should pay attention to the role that this notion is supposed to play within a particular context. Coherence is a property of (...)
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  27.  62
    Stephan Hartmann (2001). Effective Field Theories, Reductionism and Scientific Explanation. Studies in History and Philosophy of Science Part B 32 (2):267-304.
    Effective field theories have been a very popular tool in quantum physics for almost two decades. And there are good reasons for this. I will argue that effective field theories share many of the advantages of both fundamental theories and phenomenological models, while avoiding their respective shortcomings. They are, for example, flexible enough to cover a wide range of phenomena, and concrete enough to provide a detailed story of the specific mechanisms at work at a given energy scale. So will (...)
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  28.  42
    Stephan Hartmann & Luc Bovens (2006). An Impossibility Result for Coherence Rankings. Philosophical Studies 128 (1):77-91.
    If we receive information from multiple independent and partially reliable information sources, then whether we are justified to believe these information items is affected by how reliable the sources are, by how well the information coheres with our background beliefs and by how internally coherent the information is. We consider the following question. Is coherence a separable determinant of our degree of belief, i.e. is it the case that the more coherent the new information is, the more justified we are (...)
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  29.  42
    Luc Bovens & Stephan Hartmann (2006). An Impossibility Result for Coherence Rankings. Philosophical Studies 128 (1):77-91.
    If we receive information from multiple independent and partially reliable information sources, then whether we are justified to believe these information items is affected by how reliable the sources are, by how well the information coheres with our background beliefs and by how internally coherent the information is. We consider the following question. Is coherence a separable determinant of our degree of belief, i.e. is it the case that the more coherent the new information is, the more justified we are (...)
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  30.  67
    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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  31.  78
    Stephan Hartmann & Wouter Meijs (2012). Walter the Banker: The Conjunction Fallacy Reconsidered. [REVIEW] Synthese 184 (1):73-87.
    In a famous experiment by Tversky and Kahneman (Psychol Rev 90:293–315, 1983), featuring Linda the bank teller, the participants assign a higher probability to a conjunction of propositions than to one of the conjuncts, thereby seemingly committing a probabilistic fallacy. In this paper, we discuss a slightly different example featuring someone named Walter, who also happens to work at a bank, and argue that, in this example, it is rational to assign a higher probability to the conjunction of suitably chosen (...)
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  32. Stephan Hartmann (2000). Review Of: James T. Cushing, Philosophical Concepts in Physics: The Historical Relation Between Philosophy and Scientific Theories. Erkenntnis 52 (1):133-137.
    This book successfully achieves to serve two different purposes. On the one hand, it is a readable physics-based introduction into the philosophy of science, written in an informal and accessible style. The author, himself a professor of physics at the University of Notre Dame and active in the philosophy of science for almost twenty years, carefully develops his metatheoretical arguments on a solid basis provided by an extensive survey along the lines of the historical development of physics. On the other (...)
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  33.  37
    Stephan Hartmann & Jan Sprenger (2010). The Weight of Competence Under a Realistic Loss Function. Logic Journal of the IGPL 18 (2):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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  34.  49
    Rogier De Langhe, Stephan Hartmann & Jan Sprenger (2014). Introduction: The Progress of Science. Studies in History and Philosophy of Science Part A 46:54.
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  35. Patrick Suppes & Stephan Hartmann (2010). Entanglement, Upper Probabilities and Decoherence in Quantum Mechanics. In M. Suaráz et al (ed.), EPSA Philosophical Issues in the Sciences: Launch of the European Philosophy of Science Association. Springer 93--103.
    Quantum mechanical entangled configurations of particles that do not satisfy Bell’s inequalities, or equivalently, do not have a joint probability distribution, are familiar in the foundational literature of quantum mechanics. Nonexistence of a joint probability measure for the correlations predicted by quantum mechanics is itself equivalent to the nonexistence of local hidden variables that account for the correlations (for a proof of this equivalence, see Suppes and Zanotti, 1981). From a philosophical standpoint it is natural to ask what sort of (...)
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  36. Sebastian Lutz & Stephan Hartmann (2010). Conventional and Objective Invariance: Debs and Redhead on Symmetry. [REVIEW] Metascience 19:15-23.
    This review is a critical discussion of three main claims in Debs and Redhead’s thought-provoking book Objectivity, Invariance, and Convention. These claims are: (i) Social acts impinge upon formal aspects of scientific representation; (ii) symmetries introduce the need for conventional choice; (iii) perspectival symmetry is a necessary and sufficient condition for objectivity, while symmetry simpliciter fails to be necessary.
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  37.  23
    Stephan Hartmann & Ludwig Fahrbach (2005). Normativität und Bayesianismus. In B. Gesang (ed.), Deskriptive oder normative Wissenschaftstheorie. Ontos-Verlag 177-204.
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  38.  79
    Stephan Hartmann (1995). Models as a Tool for Theory Construction: Some Strategies of Preliminary Physics. In William Herfel et al (ed.), Theories and Models in Scientific Processes. Rodopi
    Theoretical models are an important tool for many aspects of scientific activity. They are used, i.a., to structure data, to apply theories or even to construct new theories. But what exactly is a model? It turns out that there is no proper definition of the term "model" that covers all these aspects. Thus, I restrict myself here to evaluate the function of models in the research process while using "model" in the loose way physicists do. To this end, I distinguish (...)
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  39. Lefteris Farmakis & Stephan Hartmann (2005). Review of Inference to the Best Explanation by Peter Lipton. [REVIEW] Notre Dame Philosophical Reviews 2005 (6).
  40.  10
    Kristina Liefke & Stephan Hartmann, Montague Reduction, Confirmation, and the Syntax-Semantics Relation.
    Intertheoretic relations are an important topic in the philosophy of science. However, since their classical discussion by Ernest Nagel, such relations have mostly been restricted to relations between pairs of theories in the natural sciences. In this paper, we present a model of a new type of intertheoretic relation, called 'Montague Reduction', which is assumed in Montague's framework for the analysis and interpretation of natural language syntax. To motivate the adoption of our new model, we show that this model extends (...)
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  41. 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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  42.  36
    Stephan Hartmann & Roman Frigg (2006). Models in Science. In Ed Zalta (ed.), The Stanford Encyclopedia of Philosophy. Stanford
    Models are of central importance in many scientific contexts. The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains are cases in point. (...)
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  43.  70
    Luc Bovens & Stephan Hartmann (2005). Why There Cannot Be a Single Probabilistic Measure of Coherence. Erkenntnis 63 (3):361-374.
    Bayesian Coherence Theory of Justification or, for short, Bayesian Coherentism, is characterized by two theses, viz. (i) that our degree of confidence in the content of a set of propositions is positively affected by the coherence of the set, and (ii) that coherence can be characterized in probabilistic terms. There has been a longstanding question of how to construct a measure of coherence. We will show that Bayesian Coherentism cannot rest on a single measure of coherence, but requires a vector (...)
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  44.  9
    Stephan Hartmann, A New Solution to the Problem of Old Evidence.
    The Problem of Old Evidence has troubled Bayesians ever since Clark Glymour first presented it in 1980. Several solutions have been proposed, but all of them have drawbacks and none of them is considered to be the definite solution. In this article, I propose a new solution which combines several old ideas with a new one. It circumvents the crucial omniscience problem in an elegant way and leads to a considerable confirmation of the hypothesis in question.
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  45.  18
    Stephan Hartmann & Jan Sprenger (2011). Mathematics and Statistics in the Social Sciences. In Ian C. Jarvie & Jesus Zamora-Bonilla (eds.), The SAGE Handbook of the Philosophy of Social Sciences. Sage Publications 594-612.
  46.  18
    Luc Bovens, Branden Fitelson, Stephan Hartmann & Josh Snyder (2002). Too Odd (Not) to Be True? A Reply to Olsson. British Journal for the Philosophy of Science 53 (4):539-563.
    Corroborating Testimony, Probability and Surprise’, Erik J. Olsson ascribes to L. Jonathan Cohen the claims that if two witnesses provide us with the same information, then the less probable the information is, the more confident we may be that the information is true (C), and the stronger the information is corroborated (C*). We question whether Cohen intends anything like claims (C) and (C*). Furthermore, he discusses the concurrence of witness reports within a context of independent witnesses, whereas the witnesses in (...)
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  47.  86
    Luc Bovens & Stephan Hartmann (2005). Coherence and the Role of Specificity: A Response to Meijs and Douven. Mind 114 (454):365-369.
  48.  27
    Luc Bovens & Stephan Hartmann (2000). Coherence, Belief Expansion and Bayesian Networks. In BaralC (ed.), Proceedings of the 8th International Workshop on Non-Monotonic Reasoning, NMR'2000.
    We construct a probabilistic coherence measure for information sets which determines a partial coherence ordering. This measure is applied in constructing a criterion for expanding our beliefs in the face of new information. A number of idealizations are being made which can be relaxed by an appeal to Bayesian Networks.
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  49. Stephan Hartmann & Daniela Bailer-Jones (2010). Modelle. In Hans Jörg Sandkühler (ed.), Enzyklopädie Philosophie. Meiner Verlag 1627-1632.
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  50.  64
    Stephan Hartmann (1998). Idealization in Quantum Field Theory. In Niall Shanks (ed.), Idealization in Contemporary Physics. 99-122.
    This paper explores various functions of idealizations in quantum field theory. To this end it is important to first distinguish between different kinds of theories and models of or inspired by quantum field theory. Idealizations have pragmatic and cognitive functions. Analyzing a case-study from hadron physics, I demonstrate the virtues of studying highly idealized models for exploring the features of theories with an extremely rich structure such as quantum field theory and for gaining some understanding of the physical processes in (...)
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