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

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  1. Luc Bovens & Stephan Hartmann (2007). Special Issue on Bayesian Epistemology Edited by L. Bovens and S. Hartmann. Synthese 156 (3).score: 600.0
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  2. Stephan Hartmann, Carlo Martini & Jan Sprenger (eds.) (2010). Formal Modeling in Social Epistemology. [REVIEW] Logic Journal of the IGPL (special issue).score: 540.0
    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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  3. Ulrich Gähde & Stephan Hartmann (2005). Coherence, Truth and Testimony. Erkenntnis 63 (3).score: 540.0
    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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  4. Stephan Hartmann & Jan Sprenger (2012). Judgment Aggregation and the Problem of Tracking the Truth. Synthese 187 (1):209-221.score: 360.0
    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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  5. Luc Bovens & Stephan Hartmann (eds.) (2004). Bayesian Epistemology. OUP Oxford.score: 360.0
    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. Luc Bovens & Stephan Hartmann (2003). Bayesian Epistemology. Oxford: Oxford University Press.score: 360.0
    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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  7. 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.score: 300.0
    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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  8. Alan Hájek & Stephan Hartmann (2010). Bayesian Epistemology. In J. Dancy et al (ed.), A Companion to Epistemology. Blackwell.score: 300.0
    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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  9. Lefteris Farmakis & Stephan Hartmann (2005). Review of Inference to the Best Explanation by Peter Lipton. [REVIEW] Notre Dame Philosophical Reviews 2005 (6).score: 300.0
  10. Stephan Hartmann & Jan Sprenger (forthcoming). Bayesian Epistemology. In Duncan Pritchard & Sven Bernecker (eds.), Routledge Companion to Epistemology. Routledge.score: 300.0
    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. Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann (2010). Who's Afraid of Nagelian Reduction? Erkenntnis 73 (3):393-412.score: 300.0
    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. Sebastian Lutz & Stephan Hartmann (2010). Conventional and Objective Invariance: Debs and Redhead on Symmetry. [REVIEW] Metascience 19:15-23.score: 300.0
    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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  13. Stephan Hartmann (2000). Review of J. Cushing: Philosophical Concepts in Physics. [REVIEW] Erkenntnis 52:133-137.score: 300.0
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  14. 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.score: 300.0
    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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  15. Alan Hájek & Stephan Hartmann, Bayesian Epistemology.score: 300.0
    According to one view, there cannot: Bayesianism fails to do justice to essential aspects of knowledge and belief, and as such it cannot provide a genuine epistemology at all. According to another view, Bayesianism should supersede traditional epistemology: where the latter has been mired in endless debates over skepticism and Gettierology, Bayesianism offers the epistemologist a thriving research program. We will advocate a more moderate view: Bayesianism can illuminate various long­standing problems of epistemology, while not addressing all of them; and (...)
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  16. Stephan Hartmann, Luc Bovens & Carl Hoefer (eds.) (2008). Nancy Cartwright's Philosophy of Science. Routledge.score: 300.0
    Nancy Cartwright is one of the most distinguished and influential contemporary philosophers of science. Despite the profound impact of her work, until now there has not been 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 (...)
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  17. Rolf Haenni & Stephan Hartmann (2006). Modeling Partially Reliable Information Sources: A General Approach Based on Dempster-Shafer Theory. Information Fusion 7:361-379.score: 300.0
    Combining testimonial reports from independent and partially reliable information sources is an important 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, 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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  18. Stephan Hartmann & Jonah N. Schupbach (2010). Review of Michael Strevens, Depth: An Account of Scientific Explanation. [REVIEW] Notre Dame Philosophical Reviews 2010 (6).score: 300.0
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  19. Stephan Hartmann (2008). Modeling High-Temperature Superconductors: Correspondence at Bay? In Lena Soler (ed.), Rethinking Scientific Change. Stabilities, Ruptures, Incommensurabilities? Springer. 107--128.score: 300.0
    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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  20. Stephan Hartmann (1997). Verfolgt Die Elementarteilchenphysik Ein Reduktionistisches Programm? In Georg Meggle (ed.), Perspectives in Analytical Philosophy. de Gruyter.score: 300.0
    Die Elementarteilchenphysik gilt weithin als eine Teildisziplin der Physik, die ein reduktionistisches Programm par excellence verfolgt. In dieser Arbeit soll versucht werden, unter Berücksichtigung einer Analyse neuerer Methoden der Elementarteilchenphysik, die Berechtigung dieser Behauptung zu klären. Die Reduktionismusproblemtik läßt sich in ontologische, epistemologische und methodologische Aspekte untergliedern.
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  21. Stephan Hartmann, Rainer Müller & Hartmut Wiesner (1998). Bose-Einstein-Kondensation Ultrakalter Atome. In W. Schneider (ed.), Wege in der Physikdidaktik, Band IV. Palm & Enke.score: 300.0
    Am 14. Juli 1995 berichteten die angesehene Wissenschaftszeitschrift Science sowie die berühmte amerikanische Tageszeitung New York Times – auf dem Titelblatt – gleichzeitig über die erstmalige experimentelle Erzeugung eines Bose-Einstein-Kondensates aus einem Gas schwach wechselwirkender Alkaliatome am Joint Institute for Laboratory Astrophy- sics (JILA) in Boulder/Colorado (USA). Was war an dieser Leistung so bedeutsam, dass man sich entschloss, sie auf jene Weise bekannt zu geben?
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  22. Vincenzo Crupi & Stephan Hartmann (2010). Formal and Empirical Methods in Philosophy of Science. In Friedrich Stadler et al (ed.), The Present Situation in the Philosophy of Science. Springer. 87--98.score: 300.0
    This essay addresses the methodology of philosophy of science and illustrates how formal and empirical methods can be fruitfully combined. Special emphasis is given to the application of experimental methods to confirmation theory and to recent work on the conjunction fallacy, a key topic in the rationality debate arising from research in cognitive psychology. Several other issue can be studied in this way. In the concluding section, a brief outline is provided of three further examples.
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  23. 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.score: 300.0
    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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  24. Stephan Hartmann, Gabriella Pigozzi & Jan Sprenger (2010). Reliable Methods of Judgment Aggregation. Journal for Logic and Computation 20:603--617.score: 300.0
    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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  25. Stephan Hartmann (2001). Effective Field Theories, Reductionism and Scientific Explanation. Studies in History and Philosophy of Science Part B 32 (2):267-304.score: 300.0
    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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  26. Stephan Hartmann (1998). Idealization in Quantum Field Theory. In Niall Shanks (ed.), Idealization in Contemporary Physics. 99-122.score: 300.0
    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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  27. Jan Sprenger, Carlo Martini & Stephan Hartmann (2009). Consensual Decision-Making Among Epistemic Peers. Episteme 6 (2):110-129.score: 300.0
    This paper focuses on the question of how to resolve disagreement and uses the Lehrer-Wagner model as a formal tool for investigating consensual decision-making. The main result consists in a general definition of when agents treat each other as epistemic peers (Kelly 2005; Elga 2007), and a theorem vindicating the “equal weight view” to resolve disagreement among epistemic peers. We apply our findings to an analysis of the impact of social network structures on group deliberation processes, and we demonstrate their (...)
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  28. Dennis Dieks, Wenceslao Gonzalo, Thomas Uebel, Stephan Hartmann & Marcel Weber (eds.) (2011). Explanation, Prediction, and Confirmation. Springer.score: 300.0
    This volume, the second in the Springer series Philosophy of Science in a European Perspective, contains selected papers from the workshops organised by the ESF ...
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  29. Roman Frigg & Stephan Hartmann (2005). Scientific Models. In Sahotra Sarkar et al (ed.), The Philosophy of Science: An Encyclopedia, Vol. 2. Routledge.score: 300.0
    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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  30. Luc Bovens & Stephan Hartmann (2005). Why There Cannot Be a Single Probabilistic Measure of Coherence. Erkenntnis 63 (3):361-374.score: 300.0
    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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  31. Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann (2011). Confirmation and Reduction: A Bayesian Account. Synthese 179 (2):321 - 338.score: 300.0
    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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  32. Roman Frigg, Stephan Hartmann & Cyrille Imbert (2009). Models and Simluations. Synthese 169 (3).score: 300.0
    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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  33. Stephan Hartmann (2008). Modeling in Philosophy of Science. In W. K. Essler & M. Frauchiger (eds.), Representation, Evidence, and Justification: Themes From Suppes. Ontos Verlag. 1--95.score: 300.0
    Models are a principle instrument of modern science. They are built, applied, tested, compared, revised and interpreted in an expansive scientific literature. Throughout this paper, I will argue that models are also a valuable tool for the philosopher of science. In particular, I will discuss how the methodology of Bayesian Networks can elucidate two central problems in the philosophy of science. The first thesis I will explore is the variety-of-evidence thesis, which argues that the more varied the supporting evidence, the (...)
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  34. Dov Gabbay, Stephan Hartmann & John Woods (eds.) (forthcoming). Handbook of the History and Philosophy of Logic, Vol. 10: Inductive Logic. Elsevier.score: 300.0
  35. Luc Bovens & Stephan Hartmann (2003). Solving the Riddle of Coherence. Mind 112 (448):601-633.score: 300.0
    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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  36. Rogier De Langhe, Stephan Hartmann & Jan Sprenger (forthcoming). The Progress of Science. Studies in History and Philosophy of Science Part A.score: 300.0
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  37. Stephan Hartmann (2001). Vacuum. In H. Gründer (ed.), Historisches Wörterbuch der Philosophie. Schwabe.score: 300.0
    Vacuum (leer, frei) bezeichnete bis zum 19. Jahrhundert allein den körperlosen Raum. Unter dem Einfluss physikalischer (Feld-) Theorien meint der Terminus inzwischen diejenige residuale physische Entiät, die einen vorgegebenen Raum ausfüllt bzw. im Prinzip ausfüllen würde, nachdem alles, was mit physikalischen Mitteln entfernt werden kann, aus dem Raum entfernt wurde. Theorien über das V. sind daher eng mit Theorien über die Struktur des Raumes, die Bewegung, die physikalischen Gegenstände und deren Wechselwirkungen verbunden. In der Quantentheorie bezeichnet V. den Zustand niedrigster (...)
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  38. Stephan Hartmann & Wouter Meijs (2012). Walter the Banker: The Conjunction Fallacy Reconsidered. [REVIEW] Synthese 184 (1):73-87.score: 300.0
    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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  39. Mark Colyvan & Stephan Hartmann (2010). Reduction and the Special Sciences. Erkenntnis 73:3 (special issue).score: 300.0
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  40. Stephan Hartmann & Jürgen Mittelstrass (2000). Physics is Part of Culture and the Basis of Technology. In DPG (ed.), Physics - Physics Research: Topics, Significance and Prospects. DPG.score: 300.0
    Fundamental aspects of modern life owe their existence to the achievements of scientific reason. In other words, science is an integral element of the modern world and simultaneously the epitome of the rational nature of a technical culture that makes up the essence of the modern world. Without science, the modern world would lose its very nature and modern society its future. Right from the start, physics forms the core of European scientific development. It is the original paradigm of science, (...)
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  41. Stephan Hartmann (2005). Transdisziplinarität – Eine Herausforderung Für Die Wissenschaftstheorie. In Gereon Wolters & Martin Carrier (eds.), Homo Sapiens und Homo Faber. de Gruyter. 335--343.score: 300.0
    Die zeitgenössische Wissenschaftstheorie leidet unter ähnlichen Problemen wie die Wissenschaften, mit denen sie sich befasst. So nimmt auch in der Wissenschaftstheorie die Spezialisierung stark zu, und bei vielen der behandelten Fragestellungen geht es einzig um Detailprobleme, die sich aus einem sich verselbständigenden Diskussionszusammenhang entwickelt haben, wobei der Bezug zur jeweiligen Ausgangsfrage und die größere philosophische Perspektive leicht aus den Augen verloren geht.
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  42. Stephan Hartmann (2001). Mechanisms, Coherence, and Theory Choice in the Cognitive Neurosciences. In Peter McLaughlin, Peter Machamer & Rick Grush (eds.), Theory and Method in the Neurosciences. Pittsburgh University Press.score: 300.0
    Let me first state that I like Antti Revonsuo’s discussion of the various methodological and interpretational problems in neuroscience. It shows how careful and methodologically reflected scientists have to proceed in this fascinating field of research. I have nothing to add here. Furthermore, I am very sympathetic towards Revonsuo’s general proposal to call for a Philosophy of Neuroscience that stresses foundational issues, but also focuses on methodological and explanatory strategies. In a footnote of his paper, Revonsuo complains – as many (...)
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  43. Stephan Hartmann & Luc Bovens (2003). Solving the Riddle of Coherence. Mind 112 (448):601-634.score: 300.0
    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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  44. Stephan Hartmann & Roman Frigg (2006). Models in Science. In Ed Zalta (ed.), The Stanford Encyclopedia of Philosophy. Stanford.score: 300.0
    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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  45. Luc Bovens & Stephan Hartmann (2006). An Impossibility Result for Coherence Rankings. Philosophical Studies 128 (1):77-91.score: 300.0
    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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  46. 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.score: 300.0
    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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  47. Stephan Hartmann (1999). Models and Stories in Hadron Physics. In Margaret Morrison & Mary Morgan (eds.), Models as Mediators. 52--326.score: 300.0
    Fundamental theories are hard to come by. But even if we had them, they would be too complicated to apply. Quantum chromodynamics (QCD) 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 (...)
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  48. Luc Bovens & Stephan Hartmann (2002). Bayesian Networks and the Problem of Unreliable Instruments. Philosophy of Science 69 (1):29-72.score: 300.0
    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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  49. Stephan Hartmann, Carl Hoefer & Luc Bovens (eds.) (2008). Nancy Cartwright's Philosophy of Science. Routledge.score: 300.0
    Nancy Cartwright is one of the most distinguished and influential contemporary philosophers of science. Despite the profound impact of her work, until now there has not been 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 (...)
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  50. Stephan Hartmann (2010). Who's Afraid of Nagelian Reduction? Erkenntnis 73 (3):393 - 412.score: 300.0
    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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