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

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  1. Klaus Hartmann, Dietmar Koch & Klaus Bort (1990). Kategorie Und Kategorialität Historisch-Systematische Untersuchungen Zum Begriff der Kategorie Im Philosophischen Denken : Festschrift Für Klaus Hartmann Zum 65. Geburtstag.
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  2. Klaus Hartmann & Josef Simon (1986). Politische Philosophie. Revista Portuguesa de Filosofia 42 (1):222-223.
     
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  3. Nicolai Hartmann & Günther Patzig (1982). Symposium Zum Gedenken an Nicolai Hartmann.
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  4.  1
    Nicolai Hartmann (2014). Translator’s Introduction: Hartmann on the Mystery and Value of Art. In Aesthetics. De Gruyter
  5.  9
    Luc Bovens & Stephan Hartmann (2007). Special Issue on Bayesian Epistemology Edited by L. Bovens and S. Hartmann. Synthese 156 (3).
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  6.  6
    Alma Hartmann (1912). Chronologische Übersicht der Schriften von Eduard von Hartmann. Kant-Studien 17 (1-3):501-520.
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  7. Nicolai Hartmann & Ricardo Guillermo Maliandi (1967). Hartmann. Centro Editor de América Latina.
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  8. E. Hartmann (1940). Hartmann, M. u. Gerlach, W., Naturwissenschaftliche Erkenntnis und ihre Methoden. [REVIEW] Philosophisches Jahrbuch 53:477-478.
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  9. E. Hartmann (1934). Hartmann, N., Das Problem des geistigen Seins. [REVIEW] Philosophisches Jahrbuch 47:413-414.
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  10. E. Hartmann (1933). Hartmann, N., Zum Problem d. Realitätsgegebenheit. [REVIEW] Philosophisches Jahrbuch 46:231-232.
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  11. E. Von Hartmann (1901). v. Hartmann, Geschichte der Metaphysik. Kant-Studien 5 (1-3).
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  12. Gerhard Haselbach, Ernst Otto & G. Jnter Hartmann (1957). Beiträge Zur Einheit von Bildung Und Sprache Im Geistigen Sein. Festschrift Zum 80. Geburtstag von Ernst Otto. Hrsg. Von Gerhard Haselbach Und Günter Hartmann. [REVIEW] W. De Gruyter.
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  13.  29
    Luc Bovens & Stephan Hartmann (2003). Bayesian Epistemology. Oxford: Oxford University Press.
    Bovens and Hartmann provide a systematic guide to the use of probabilistic methods not just in epistemology, but also in philosophy of science, voting theory, ...
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  14.  43
    Luc Bovens & Stephan Hartmann (eds.) (2004). Bayesian Epistemology. OUP Oxford.
    Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information (...)
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  15. Stephan Hartmann & Jan Sprenger (2012). Judgment Aggregation and the Problem of Tracking the Truth. Synthese 187 (1):209-221.
    The aggregation of consistent individual judgments on logically interconnected propositions into a collective judgment on those propositions has recently drawn much attention. Seemingly reasonable aggregation procedures, such as propositionwise majority voting, cannot ensure an equally consistent collective conclusion. The literature on judgment aggregation refers to that problem as the discursive dilemma. In this paper, we motivate that many groups do not only want to reach a factually right conclusion, but also want to correctly evaluate the reasons for that conclusion. In (...)
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  16.  5
    Nicolai Hartmann, Frédéric Tremblay & Keith R. Peterson (forthcoming). The Megarian and the Aristotelian Concept of Possibility: A Contribution to the History of the Ontological Problem of Modality. Axiomathes:1-15.
    This is a translation of Nicolai Hartmann’s article “Der Megarische und der Aristotelische Möglichkeitsbegriff: ein Beitrag zur Geschichte des ontologischen Modalitätsproblems,” first published in 1937. In this article, Hartmann defends an interpretation of the Megarian conception of possibility, which found its clearest form in Diodorus Cronus’ expression of it and according to which “only what is actual is possible” or “something is possible only if it is actual.” Hartmann defends this interpretation against the then dominant Aristotelian conception (...)
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  17. Luc Bovens & Stephan Hartmann (2004). Bayesian Epistemology. Oxford University Press Uk.
    Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information (...)
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  18.  12
    Nicolai Hartmann & Keith R. Peterson (2012). How Is Critical Ontology Possible? Toward the Foundation of the General Theory of the Categories, Part One (1923). Axiomathes 22 (3):315-354.
    This is a translation of an early essay by the German philosopher Nicolai Hartmann (1882–1950). In this 1923 essay Hartmann presents many of the fundamental ideas of his new critical ontology. He summarizes some of the main points of his critique of neo-Kantian epistemology, and provides the point of departure for his new approach in an extensive criticism of the errors of the classical ontological tradition. Some of these errors concern the definition of an ontological category or principle, (...)
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  19.  13
    Ulrich Gähde & Stephan Hartmann (2005). Coherence, Truth and Testimony. Erkenntnis 63 (3).
    Special issue. With contributions by Luc Bovens and Stephan Hartmann, David Glass, Keith Lehrer, Erik Olsson, Tomoji Shogenji, Mark Siebel, and Paul Thagard.
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  20. Stephan Hartmann, Carlo Martini & Jan Sprenger (eds.) (2010). Formal Modeling in Social Epistemology. [REVIEW] Logic Journal of the IGPL (Special Issue).
    Special issue. With contributions by Rogier De Langhe and Matthias Greiff, Igor Douven and Alexander Riegler, Stephan Hartmann and Jan Sprenger, Carl Wagner, Paul Weirich, and Jesús Zamora Bonilla.
     
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  21. Josef Endres (forthcoming). Der Schichtengedanke bei N. Hartmann. Divus Thomas.
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  22. Josef Stallmach (1978). Dynamis und Energeia. Untersuchungen am Werk des Aristoteles zur Problemgeschichte von Möglichkeit und Wirklichkeit. Hain, Meisenheim/Glan 1959. Ansichsein. Untersuchung zum Verhältnis von Sein und Erkennen im Anschluß an Nicolai Hartmann. [REVIEW] Salzburger Jahrbuch für Philosophie 23:24-79.
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  23. Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann (2010). Who's Afraid of Nagelian Reduction? Erkenntnis 73 (3):393-412.
    We reconsider the Nagelian theory of reduction and argue that, contrary to a widely held view, it is the right analysis of intertheoretic reduction, since the alleged difficulties of the theory either vanish upon closer inspection or turn out to be substantive philosophical questions rather than knock-down arguments.
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  24. Stephan Hartmann & Luc Bovens (2005). Why There Cannot Be a Single Probabilistic Measure of Coherence. Erkenntnis 63 (3):361-374.
    Bayesian Coherence Theory of Justification or, for short, Bayesian Coherentism, is characterized by two theses, viz. (i) that our degree of confidence in the content of a set of propositions is positively affected by the coherence of the set, and (ii) that coherence can be characterized in probabilistic terms. There has been a longstanding question of how to construct a measure of coherence. We will show that Bayesian Coherentism cannot rest on a single measure of coherence, but requires a vector (...)
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  25.  51
    Luc Bovens & Stephan Hartmann (2003). Solving the Riddle of Coherence. Mind 112 (448):601-633.
    A coherent story is a story that fits together well. This notion plays a central role in the coherence theory of justification and has been proposed as a criterion for scientific theory choice. Many attempts have been made to give a probabilistic account of this notion. A proper account of coherence must not start from some partial intuitions, but should pay attention to the role that this notion is supposed to play within a particular context. Coherence is a property of (...)
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  26. Alan Hájek & Stephan Hartmann (2010). Bayesian Epistemology. In J. Dancy et al (ed.), A Companion to Epistemology. Blackwell
    Bayesianism is our leading theory of uncertainty. Epistemology is defined as the theory of knowledge. So “Bayesian Epistemology” may sound like an oxymoron. Bayesianism, after all, studies the properties and dynamics of degrees of belief, understood to be probabilities. Traditional epistemology, on the other hand, places the singularly non-probabilistic notion of knowledge at centre stage, and to the extent that it traffics in belief, that notion does not come in degrees. So how can there be a Bayesian epistemology?
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  27.  99
    Stephan Hartmann, Gabriella Pigozzi & Jan Sprenger (2010). Reliable Methods of Judgment Aggregation. Journal for Logic and Computation 20:603--617.
    The aggregation of consistent individual judgments on logically interconnected propositions into a collective judgment on the same propositions has recently drawn much attention. Seemingly reasonable aggregation procedures, such as propositionwise majority voting, cannot ensure an equally consistent collective conclusion. The literature on judgment aggregation refers to such a problem as the \textit{discursive dilemma}. In this paper we assume that the decision which the group is trying to reach is factually right or wrong. Hence, we address the question of how good (...)
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  28. Luc Bovens & Stephan Hartmann (2003). ``Solving the Riddle of Coherence". Mind 112 (448):601-634.
    A coherent story is a story that fits together well. This notion plays a central role in the coherence theory of justification and has been proposed as a criterion for scientific theory choice. Many attempts have been made to give a probabilistic account of this notion. A proper account of coherence must not start from some partial intuitions, but should pay attention to the role that this notion is supposed to play within a particular context. Coherence is a property of (...)
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  29. Stephan Hartmann (1996). The World as a Process: Simulations in the Natural and Social Sciences. In Rainer Hegselmann (ed.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View.
    Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...)
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  30.  42
    Stephan Hartmann & Luc Bovens (2003). Solving the Riddle of Coherence. Mind 112 (448):601-634.
    A coherent story is a story that fits together well. This notion plays a central role in the coherence theory of justification and has been proposed as a criterion for scientific theory choice. Many attempts have been made to give a probabilistic account of this notion. A proper account of coherence must not start from some partial intuitions, but should pay attention to the role that this notion is supposed to play within a particular context. Coherence is a property of (...)
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  31.  61
    Stephan Hartmann (1999). Models and Stories in Hadron Physics. In Margaret Morrison & Mary Morgan (eds.), Models as Mediators. 52--326.
    Fundamental theories are hard to come by. But even if we had them, they would be too complicated to apply. Quantum chromodynamics is a case in point. This theory is supposed to govern all strong interactions, but it is extremely hard to apply and test at energies where protons, neutrons and ions are the effective degrees of freedom. Instead, scientists typically use highly idealized models such as the MIT Bag Model or the Nambu Jona-Lasinio Model to account for phenomena in (...)
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  32. Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann (2011). Confirmation and Reduction: A Bayesian Account. Synthese 179 (2):321 - 338.
    Various scientific theories stand in a reductive relation to each other. In a recent article, we have argued that a generalized version of the Nagel-Schaffner model (GNS) is the right account of this relation. In this article, we present a Bayesian analysis of how GNS impacts on confirmation. We formalize the relation between the reducing and the reduced theory before and after the reduction using Bayesian networks, and thereby show that, post-reduction, the two theories are confirmatory of each other. We (...)
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  33.  82
    Stephan Hartmann (2001). Effective Field Theories, Reductionism and Scientific Explanation. Studies in History and Philosophy of Science Part B 32 (2):267-304.
    Effective field theories have been a very popular tool in quantum physics for almost two decades. And there are good reasons for this. I will argue that effective field theories share many of the advantages of both fundamental theories and phenomenological models, while avoiding their respective shortcomings. They are, for example, flexible enough to cover a wide range of phenomena, and concrete enough to provide a detailed story of the specific mechanisms at work at a given energy scale. So will (...)
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  34. Martin Hartmann & Axel Honneth (2006). Paradoxes of Capitalism. Constellations 13 (1):41-58.
  35.  32
    Luc Bovens & Stephan Hartmann (2005). Why There Cannot Be a Single Probabilistic Measure of Coherence. Erkenntnis 63 (3):361-374.
    Bayesian Coherence Theory of Justification or, for short, Bayesian Coherentism, is characterized by two theses, viz. (i) that our degree of confidence in the content of a set of propositions is positively affected by the coherence of the set, and (ii) that coherence can be characterized in probabilistic terms. There has been a longstanding question of how to construct a measure of coherence. We will show that Bayesian Coherentism cannot rest on a single measure of coherence, but requires a vector (...)
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  36.  43
    Stephan Hartmann & Jan Sprenger (2010). The Weight of Competence Under a Realistic Loss Function. Logic Journal of the IGPL 18 (2):346-352.
    In many scientific, economic and policy-related problems, pieces of information from different sources have to be aggregated. Typically, the sources are not equally competent. This raises the question of how the relative weights and competences should be related to arrive at an optimal final verdict. Our paper addresses this question under a more realistic perspective of measuring the practical loss implied by an inaccurate verdict.
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  37.  71
    Jan Sprenger, Carlo Martini & Stephan Hartmann (2009). Consensual Decision-Making Among Epistemic Peers. Episteme 6 (2):110-129.
    This paper focuses on the question of how to resolve disagreement and uses the Lehrer-Wagner model as a formal tool for investigating consensual decision-making. The main result consists in a general definition of when agents treat each other as epistemic peers (Kelly 2005; Elga 2007), and a theorem vindicating the “equal weight view” to resolve disagreement among epistemic peers. We apply our findings to an analysis of the impact of social network structures on group deliberation processes, and we demonstrate their (...)
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  38. Stephan Hartmann & Wouter Meijs (2012). Walter the Banker: The Conjunction Fallacy Reconsidered. [REVIEW] Synthese 184 (1):73-87.
    In a famous experiment by Tversky and Kahneman (Psychol Rev 90:293–315, 1983), featuring Linda the bank teller, the participants assign a higher probability to a conjunction of propositions than to one of the conjuncts, thereby seemingly committing a probabilistic fallacy. In this paper, we discuss a slightly different example featuring someone named Walter, who also happens to work at a bank, and argue that, in this example, it is rational to assign a higher probability to the conjunction of suitably chosen (...)
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  39. Luc Bovens & Stephan Hartmann (2002). Bayesian Networks and the Problem of Unreliable Instruments. Philosophy of Science 69 (1):29-72.
    We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments. In particular, we consider (i) repeated measurements of a single test consequence of the hypothesis, (ii) measurements of multiple test consequences of the hypothesis, (iii) theoretical support for the reliability of the instrument, and (iv) calibration procedures. We evaluate these strategies on their relative merits under idealized conditions and show some surprising (...)
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  40.  21
    F. Stadler, D. Dieks, W. Gonzales, S. Hartmann, T. Uebel & M. Weber (eds.) (2010). The Present Situation in the Philosophy of Science. 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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  41.  21
    Luc Bovens, Branden Fitelson, Stephan Hartmann & Josh Snyder (2002). Too Odd (Not) to Be True? A Reply to Olsson. British Journal for the Philosophy of Science 53 (4):539-563.
    Corroborating Testimony, Probability and Surprise’, Erik J. Olsson ascribes to L. Jonathan Cohen the claims that if two witnesses provide us with the same information, then the less probable the information is, the more confident we may be that the information is true (C), and the stronger the information is corroborated (C*). We question whether Cohen intends anything like claims (C) and (C*). Furthermore, he discusses the concurrence of witness reports within a context of independent witnesses, whereas the witnesses in (...)
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  42.  39
    Stephan Hartmann & Roman Frigg (2006). Models in Science. In Ed Zalta (ed.), The Stanford Encyclopedia of Philosophy. Stanford
    Models are of central importance in many scientific contexts. The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains are cases in point. (...)
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  43. Roman Frigg, Stephan Hartmann & Cyrille Imbert (2009). Models and Simluations. 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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  44.  89
    Stephan Hartmann (1995). Models as a Tool for Theory Construction: Some Strategies of Preliminary Physics. In William Herfel et al (ed.), Theories and Models in Scientific Processes. Rodopi
    Theoretical models are an important tool for many aspects of scientific activity. They are used, i.a., to structure data, to apply theories or even to construct new theories. But what exactly is a model? It turns out that there is no proper definition of the term "model" that covers all these aspects. Thus, I restrict myself here to evaluate the function of models in the research process while using "model" in the loose way physicists do. To this end, I distinguish (...)
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  45.  32
    Luc Bovens & Stephan Hartmann (2000). Coherence, Belief Expansion and Bayesian Networks. In BaralC (ed.), Proceedings of the 8th International Workshop on Non-Monotonic Reasoning, NMR'2000.
    We construct a probabilistic coherence measure for information sets which determines a partial coherence ordering. This measure is applied in constructing a criterion for expanding our beliefs in the face of new information. A number of idealizations are being made which can be relaxed by an appeal to Bayesian Networks.
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  46.  43
    Stephan Hartmann (2002). On Correspondence. Studies in History and Philosophy of Science Part B 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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  47.  85
    Luc Bovens & Stephan Hartmann (2005). Why There Cannot Be a Single Probabilistic Measure of Coherence. Erkenntnis 63 (3):361-374.
    Bayesian Coherence Theory of Justification or, for short, Bayesian Coherentism, is characterized by two theses, viz. (i) that our degree of confidence in the content of a set of propositions is positively affected by the coherence of the set, and (ii) that coherence can be characterized in probabilistic terms. There has been a longstanding question of how to construct a measure of coherence. We will show that Bayesian Coherentism cannot rest on a single measure of coherence, but requires a vector (...)
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  48.  75
    Stephan Hartmann (1998). Idealization in Quantum Field Theory. In Niall Shanks (ed.), Idealization in Contemporary Physics. 99-122.
    This paper explores various functions of idealizations in quantum field theory. To this end it is important to first distinguish between different kinds of theories and models of or inspired by quantum field theory. Idealizations have pragmatic and cognitive functions. Analyzing a case-study from hadron physics, I demonstrate the virtues of studying highly idealized models for exploring the features of theories with an extremely rich structure such as quantum field theory and for gaining some understanding of the physical processes in (...)
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  49. Stephan Hartmann (2000). Review Of: James T. Cushing, Philosophical Concepts in Physics: The Historical Relation Between Philosophy and Scientific Theories. Erkenntnis 52 (1):133-137.
    This book successfully achieves to serve two different purposes. On the one hand, it is a readable physics-based introduction into the philosophy of science, written in an informal and accessible style. The author, himself a professor of physics at the University of Notre Dame and active in the philosophy of science for almost twenty years, carefully develops his metatheoretical arguments on a solid basis provided by an extensive survey along the lines of the historical development of physics. On the other (...)
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  50. Patrick Suppes & Stephan Hartmann (2010). Entanglement, Upper Probabilities and Decoherence in Quantum Mechanics. In M. Suaráz et al (ed.), EPSA Philosophical Issues in the Sciences: Launch of the European Philosophy of Science Association. Springer 93--103.
    Quantum mechanical entangled configurations of particles that do not satisfy Bell’s inequalities, or equivalently, do not have a joint probability distribution, are familiar in the foundational literature of quantum mechanics. Nonexistence of a joint probability measure for the correlations predicted by quantum mechanics is itself equivalent to the nonexistence of local hidden variables that account for the correlations (for a proof of this equivalence, see Suppes and Zanotti, 1981). From a philosophical standpoint it is natural to ask what sort of (...)
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