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Stephan Hartmann
Ludwig Maximilians Universität, München
  1. Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2003 - Oxford: 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 sources. (...)
  2. Models in Science.Roman Frigg & Stephan Hartmann - 2006 - In Edward N. 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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  3. Who’s Afraid of Nagelian Reduction?Foad Dizadji-Bahmani, Roman Frigg & 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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  4.  36
    Understanding (with) Toy Models.Alexander Reutlinger, Dominik Hangleiter & Stephan Hartmann - 2018 - British Journal for the Philosophy of Science 69 (4):1069-1099.
    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 concerns what the epistemic goal of toy modelling 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 article is to precisely articulate and to defend this (...)
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  5. 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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  6. Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2017 - 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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  7. Bayesian Philosophy of Science.Jan Sprenger & Stephan Hartmann - 2019 - Oxford University Press.
    Jan Sprenger and Stephan Hartmann offer a fresh approach to central topics in philosophy of science, including causation, explanation, evidence, and scientific models. Their Bayesian approach uses the concept of degrees of belief to explain and to elucidate manifold aspects of scientific reasoning.
     
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  8.  94
    Understanding (With) Toy Models.Alexander Reutlinger, Dominik Hangleiter & Stephan Hartmann - 2016 - 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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  9. 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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  10. Bayesian Epistemology.Stephan Hartmann & Jan Sprenger - 2010 - In Duncan Pritchard & Sven Bernecker (eds.), The Routledge Companion to Epistemology. London: Routledge. pp. 609-620.
    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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  11. 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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  12. 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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  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. 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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  15.  42
    Hawking Radiation and Analogue Experiments: A Bayesian Analysis.Radin Dardashti, Stephan Hartmann, Karim Thébault & Eric Winsberg - 2019 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 67:1-11.
    We present a Bayesian analysis of the epistemology of analogue experiments with particular reference to Hawking radiation. Provided such experiments can be externally validated via universality arguments, we prove that they are confirmatory in Bayesian terms. We then provide a formal model for the scaling behaviour of the confirmation measure for multiple distinct realisations of the analogue system and isolate a generic saturation feature. Finally, we demonstrate that different potential analogue realisations could provide different levels of confirmation. Our results thus (...)
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  16. 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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  17.  81
    Models and Stories in Hadron Physics.Stephan Hartmann - 1999 - In Margaret Morrison & Mary Morgan (eds.), Models as Mediators: Perspectives on Natural and Social Science. 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. Confirmation and Reduction: A Bayesian Account.Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann - 2011 - 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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  19. Consensual Decision-Making Among Epistemic Peers.Stephan Hartmann, Carlo Martini & Jan Sprenger - 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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  20.  98
    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.  49
    A New Garber-Style Solution to the Problem of Old Evidence.Stephan Hartmann & Branden Fitelson - 2015 - Philosophy of Science 82 (4):712-717.
    In this discussion note, we explain how to relax some of the standard assumptions made in Garber-style solutions to the Problem of Old Evidence. The result is a more general and explanatory Bayesian approach.
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  22. 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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  23.  78
    The No Miracles Argument Without the Base Rate Fallacy.Richard Dawid & Stephan Hartmann - 2018 - Synthese 195 (9):4063-4079.
    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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  24. An Impossibility Result for Coherence Rankings.Luc Bovens & Stephan Hartmann - 2006 - 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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  25.  47
    Learning From Conditionals.Benjamin Eva, Stephan Hartmann & Soroush Rafiee Rad - 2020 - Mind 129 (514):461-508.
    In this article, we address a major outstanding question of probabilistic Bayesian epistemology: how should a rational Bayesian agent update their beliefs upon learning an indicative conditional? A number of authors have recently contended that this question is fundamentally underdetermined by Bayesian norms, and hence that there is no single update procedure that rational agents are obliged to follow upon learning an indicative conditional. Here we resist this trend and argue that a core set of widely accepted Bayesian norms is (...)
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  26.  50
    When No Reason for is a Reason Against.Benjamin Eva & Stephan Hartmann - 2018 - Analysis 78 (3):426-431.
    We provide a Bayesian justification of the idea that, under certain conditions, the absence of an argument in favour of the truth of a hypothesis H constitutes a good argument against the truth of H.
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  27.  90
    Being Realist About Bayes, and the Predictive Processing Theory of Mind.Matteo Colombo, Lee Elkin & Stephan Hartmann - forthcoming - British Journal for the Philosophy of Science.
    Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this paper, we argue that this realist attitude is unwarranted. The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian approach (...)
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  28.  38
    Bayesian Argumentation and the Value of Logical Validity.Benjamin Eva & Stephan Hartmann - 2018 - Psychological Review 125 (5):806-821.
    According to the Bayesian paradigm in the psychology of reasoning, the norms by which everyday human cognition is best evaluated are probabilistic rather than logical in character. Recently, the Bayesian paradigm has been applied to the domain of argumentation, where the fundamental norms are traditionally assumed to be logical. Here, we present a major generalisation of extant Bayesian approaches to argumentation that utilizes a new class of Bayesian learning methods that are better suited to modelling dynamic and conditional inferences than (...)
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  29. 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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  30. 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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  31. 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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  32.  66
    On the Emergence of Descriptive Norms.Ryan Muldoon, Chiara Lisciandra, Cristina Bicchieri, Stephan Hartmann & Jan Sprenger - 2014 - Politics, Philosophy and Economics 13 (1):3-22.
    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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  33. 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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  34. Reliable Methods of Judgment Aggregation.Stephan Hartmann, Gabriella Pigozzi & Jan Sprenger - 2007 - 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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  35. Nancy Cartwright’s Philosophy of Science.Stephan Hartmann, Luc Bovens & Carl Hoefer - 2008 - Routledge.
    Nancy Cartwright is one of the most distinguished and influential contemporary philosophers of science. Despite the profound impact of her work, there is neither a systematic exposition of Cartwright’s philosophy of science nor a collection of articles that contains in-depth discussions of the major themes of her philosophy. This book is devoted to a critical assessment of Cartwright’s philosophy of science and contains contributions from Cartwright's champions and critics. Broken into three parts, the book begins by addressing Cartwright's views on (...)
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  36.  70
    The Present Situation in the Philosophy of Science.Thomas Uebel, Stephan Hartmann, Wenceslao Gonzalez, Marcel Weber, Dennis Dieks & Friedrich Stadler (eds.) - 2010 - Springer.
    This volume is a serious attempt to open up the subject of European philosophy of science to real thought, and provide the structural basis for the ...
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  37. 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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  38. Introduction.Claus Beisbart & Stephan Hartmann - 2011 - In Claus Beisbart & Stephan Hartmann (eds.), Probabilities in Physics. Oxford University Press.
  39.  16
    Bayesian Cognitive Science, Monopoly, and Neglected Frameworks.Matteo Colombo & Stephan Hartmann - 2015 - British Journal for the Philosophy of Science 68 (2):451–484.
    A widely shared view in the cognitive sciences is that discovering and assessing explanations of cognitive phenomena whose production involves uncertainty should be done in a Bayesian framework. One assumption supporting this modelling choice is that Bayes provides the best approach for representing uncertainty. However, it is unclear that Bayes possesses special epistemic virtues over alternative modelling frameworks, since a systematic comparison has yet to be attempted. Currently, it is then premature to assert that cognitive phenomena involving uncertainty are best (...)
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  40. Entanglement, Upper Probabilities and Decoherence in Quantum Mechanics.Patrick Suppes & Stephan Hartmann - 2009 - In M. Suaráz (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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  41.  94
    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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  42.  20
    Philosophical Concepts in Physics. The Historical Relation Between Philosophy and Scientific Theories.Stephan Hartmann - 2000 - Erkenntnis 52 (1):133-137.
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  43. Mathematics and Statistics in the Social Sciences.Stephan Hartmann & Jan Sprenger - 2011 - In Ian C. Jarvie & Jesus Zamora-Bonilla (eds.), The SAGE Handbook of the Philosophy of Social Sciences. Sage Publications. pp. 594-612.
  44. 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.  22
    Voting, Deliberation and Truth.Stephan Hartmann & Soroush Rafiee Rad - 2018 - Synthese 195 (3):1273-1293.
    There are various ways to reach a group decision on a factual yes–no question. One way is to vote and decide what the majority votes for. This procedure receives some epistemological support from the Condorcet Jury Theorem. Alternatively, the group members may prefer to deliberate and will eventually reach a decision that everybody endorses—a consensus. While the latter procedure has the advantage that it makes everybody happy, it has the disadvantage that it is difficult to implement, especially for larger groups. (...)
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  46.  41
    Confirmation by Explanation: A Bayesian Justification of IBE.Marko Tesic, Benjamin Eva & Stephan Hartmann - unknown
    We provide a novel Bayesian justification of inference to the best explanation. More specifically, we present conditions under which explanatory considerations can provide a significant confirmatory boost for hypotheses that provide the best explanation of the relevant evidence. Furthermore, we show that the proposed Bayesian model of IBE is able to deal naturally with the best known criticisms of IBE such as van Fraassen?s?bad lot? argument.
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  47.  90
    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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  48.  25
    A New Solution to the Problem of Old Evidence.Stephan Hartmann - unknown
    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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  49. Coherence and the Role of Specificity: A Response to Meijs and Douven.Luc Bovens & Stephan Hartmann - 2005 - Mind 114 (454):365-369.
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  50.  76
    Editorial: Formal Epistemology Meets Experimental Philosophy. [REVIEW]Stephan Hartmann, Chiara Lisciandra & Edouard Machery - 2013 - Synthese 190 (8):1333-1335.
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