Results for 'Hartmann Stephan'

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  1.  10
    Special Issue on Bayesian Epistemology Edited by L. Bovens and S. Hartmann.Luc Bovens & Stephan Hartmann - 2007 - Synthese 156 (3).
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  2.  13
    Coherence, Truth and Testimony.Ulrich Gähde & Stephan Hartmann - 2005 - 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. Formal Modeling in Social Epistemology. [REVIEW]Stephan Hartmann, Carlo Martini & Jan Sprenger (eds.) - 2010 - 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.  43
    Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2003 - 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.  46
    Bayesian Epistemology.Luc Bovens & Stephan Hartmann (eds.) - 2004 - Oxford University Press.
    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. Judgment Aggregation and the Problem of Tracking the Truth.Stephan Hartmann & Jan Sprenger - 2012 - 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. Why There Cannot Be a Single Probabilistic Measure of Coherence.Luc Bovens & Stephan Hartmann - 2005 - 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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  8. Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2015 - British Journal for the Philosophy of Science 68 (2).
    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. ``Solving the Riddle of Coherence".Luc Bovens & Stephan Hartmann - 2003 - 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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  10. Bayesian Epistemology.Alan Hájek & Stephan Hartmann - 2010 - 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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  11.  95
    Solving the Riddle of Coherence.Stephan Hartmann & Luc Bovens - 2003 - 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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  12.  88
    Effective Field Theories, Reductionism and Scientific Explanation.Stephan Hartmann - 2001 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 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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  13. The World as a Process: Simulations in the Natural and Social Sciences.Stephan Hartmann - 1996 - 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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  14. Reliable Methods of Judgment Aggregation.Stephan Hartmann, Gabriella Pigozzi & Jan Sprenger - 2010 - 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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  15.  73
    The No Alternatives Argument.Richard Dawid, Stephan Hartmann & Jan Sprenger - 2015 - 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.  32
    Understanding (With) Toy Models.Alexander Reutlinger, Dominik Hangleiter & Stephan Hartmann - forthcoming - British Journal for the Philosophy of Science:axx005.
    Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models is that it is an unsettled question what the epistemic goal of toy modeling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this paper is to (...)
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  17.  65
    Models and Stories in Hadron Physics.Stephan Hartmann - 1999 - In Margaret Morrison & Mary Morgan (eds.), Models as Mediators. pp. 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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  18. An Impossibility Result for Coherence Rankings.Luc Bovens & Stephan Hartmann - 2005 - 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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  19. Who's Afraid of Nagelian Reduction?Stephan Hartmann - 2010 - 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. 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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  20.  61
    Models, Mechanisms, and Coherence.Matteo Colombo, Stephan Hartmann & Robert van Iersel - 2015 - 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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  21.  72
    Consensual Decision-Making Among Epistemic Peers.Jan Sprenger, Carlo Martini & Stephan Hartmann - 2009 - 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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  22. Bayesian Networks and the Problem of Unreliable Instruments.Luc Bovens & Stephan Hartmann - 2002 - 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.  44
    The Weight of Competence Under a Realistic Loss Function.Stephan Hartmann & Jan Sprenger - 2010 - 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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  24.  99
    Models as a Tool for Theory Construction: Some Strategies of Preliminary Physics.Stephan Hartmann - 1995 - In William Herfel, Władysław Krajewski, Ilkka Niiniluoto & Ryszard Wójcicki (eds.), Theories and Models in Scientific Processes. Rodopi. pp. 49-67.
    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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  25. Walter the Banker: The Conjunction Fallacy Reconsidered. [REVIEW]Stephan Hartmann & Wouter Meijs - 2012 - 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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  26.  56
    Too Odd (Not) to Be True? A Reply to Olsson.Luc Bovens, Branden Fitelson, Stephan Hartmann & Josh Snyder - 2002 - 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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  27. Montague Reduction, Confirmation, and the Syntax-Semantics Relation.Stephan Hartmann & Kristina Liefke - manuscript
    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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  28.  56
    Models in Science.Stephan Hartmann & Roman Frigg - 2006 - 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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  29.  5
    Who’s Afraid of Nagelian Reduction?Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann - 2010 - Erkenntnis 73 (3):393-412.
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  30. Models and Simluations.Roman Frigg, Stephan Hartmann & Cyrille Imbert - 2009 - Synthese 169 (3).
    Special issue. With contributions by Anouk Barberouse, Sarah Francescelli and Cyrille Imbert, Robert Batterman, Roman Frigg and Julian Reiss, Axel Gelfert, Till Grüne-Yanoff, Paul Humphreys, James Mattingly and Walter Warwick, Matthew Parker, Wendy Parker, Dirk Schlimm, and Eric Winsberg.
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  31.  33
    Coherence, Belief Expansion and Bayesian Networks.Luc Bovens & Stephan Hartmann - 2000 - 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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  32.  46
    On Correspondence.Stephan Hartmann - 2002 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 33 (1):79-94.
    This paper is an essay review of Steven French and Harmke Kamminga (eds.), Correspondence, Invariance and Heuristics. Essays in Honour of Heinz Post (Dordrecht: Kluwer, 1993). I distinguish a varity of correspondence relations between scientific theories (exemplified by cases from the book under review) and examine how one can make sense of the the prevailing continuity in scientific theorizing.
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  33.  2
    Effective Field Theories, Reductionism and Scientific Explanation.Stephan Hartmann - 2001 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 32 (2):267-304.
  34.  35
    Editorial: Formal Epistemology Meets Experimental Philosophy. [REVIEW]Stephan Hartmann, Chiara Lisciandra & Edouard Machery - 2013 - Synthese 190 (8):1333-1335.
  35. Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2005 - Studia Logica 81 (2):289-292.
     
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  36.  64
    Imprecise Probabilities in Quantum Mechanics.Stephan Hartmann - forthcoming - In Colleen E. Crangle, Adolfo García de la Sienra & Helen E. Longino (eds.), Foundations and Methods from Mathematics to Neuroscience. CSLI Publications.
  37.  7
    How Does Philosophy of Science Make a Difference in the World We Live In?Stephan Hartmann, Stathis Psillos & Roman Frigg - 2017 - Balkan Journal of Philosophy 9 (1):79-82.
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  38. Conventional and Objective Invariance: Debs and Redhead on Symmetry. [REVIEW]Sebastian Lutz & Stephan Hartmann - 2010 - 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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  39.  76
    Idealization in Quantum Field Theory.Stephan Hartmann - 1998 - In Niall Shanks (ed.), Idealization in Contemporary Physics. pp. 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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  40.  37
    Confirmation Via Analogue Simulation: A Bayesian Analysis.Radin Dardashti, Stephan Hartmann, Karim P. Y. Thebault & Eric Winsberg - unknown
    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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  41. Modelle.Stephan Hartmann & Daniela Bailer-Jones - 2010 - In Hans Jörg Sandkühler (ed.), Enzyklopädie Philosophie. Meiner Verlag. pp. 1627-1632.
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  42. Entanglement, Upper Probabilities and Decoherence in Quantum Mechanics.Patrick Suppes & Stephan Hartmann - 2010 - In M. Suaráz et al (ed.), EPSA Philosophical Issues in the Sciences: Launch of the European Philosophy of Science Association. Springer. pp. 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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  43.  33
    The No Miracles Argument Without the Base Rate Fallacy.Richard Dawid & Stephan Hartmann - forthcoming - Synthese:1-17.
    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 NMA. The other version, which resembles the form in which the argument was initially presented by Putnam and Boyd, remains unaffected by his line of reasoning. We provide a formal reconstruction of that version of the NMA and show that it is valid. Finally, (...)
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  44. Review Of: James T. Cushing, Philosophical Concepts in Physics: The Historical Relation Between Philosophy and Scientific Theories.Stephan Hartmann - 2000 - 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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  45. Coherence and the Role of Specificity: A Response to Meijs and Douven.Luc Bovens & Stephan Hartmann - 2005 - Mind 114 (454):365-369.
  46. Bayesian Epistemology.Stephan Hartmann & Jan Sprenger - forthcoming - 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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  47.  69
    Scientific Models.Stephan Hartmann & Roman Frigg - 2005 - In SarkarSahotra (ed.), The Philosophy of Science: An Encyclopedia, Vol. 2. Routledge.
    Models are of central importance in many scientific contexts. The roles the MIT bag model of the nucleon, the billiard ball model of a gas, the Bohr model of the atom, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka- Volterra model of predator-prey interaction, agent-based and evolutionary models of social interaction, or general equilibrium models of markets play in their respective domains are cases in point.
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  48.  76
    Modeling High-Temperature Superconductors: Correspondence at Bay?Stephan Hartmann - 2008 - In Lena Soler (ed.), Rethinking Scientific Change. Stabilities, Ruptures, Incommensurabilities? Springer. pp. 107--128.
    How does a predecessor theory relate to its successor? According to Heinz Post’s General Correspondence Principle, the successor theory has to account for the em- pirical success of its predecessor. After a critical discussion of this principle, I outline and discuss various kinds of correspondence relations that hold between successive scientific theories. I then look in some detail at a case study from contemporary physics: the various proposals for a theory of high-temperature superconductivity. The aim of this case study is (...)
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  49.  97
    Modeling Partially Reliable Information Sources: A General Approach Based on Dempster-Shafer Theory.Stephan Hartmann & Rolf Haenni - 2006 - Information Fusion 7:361-379.
    Combining testimonial reports from independent and partially reliable information sources is an important epistemological problem of uncertain reasoning. Within the framework of Dempster–Shafer theory, we propose a general model of partially reliable sources, which includes several previously known results as special cases. The paper reproduces these results on the basis of a comprehensive model taxonomy. This gives a number of new insights and thereby contributes to a better understanding of this important application of reasoning with uncertain and incomplete information.
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  50.  14
    Too Odd (Not) to Be True: A Reply to Olsson.Stephan Hartmann, Luc Bovens, Branden Fitelson & Josh Snyder - 2002 - British Journal for the Philosophy of Science 53 (4):539-563.
    In ‘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 (...)
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