166 found
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  1. Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2003 - Oxford: Oxford University Press. Edited by Stephan Hartmann.
    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. Bayesian Philosophy of Science.Jan Sprenger & Stephan Hartmann - 2019 - Oxford and New York: Oxford University Press.
    How should we reason in science? Jan Sprenger and Stephan Hartmann offer a refreshing take on classical topics in philosophy of science, using a single key concept to explain and to elucidate manifold aspects of scientific reasoning. They present good arguments and good inferences as being characterized by their effect on our rational degrees of belief. Refuting the view that there is no place for subjective attitudes in 'objective science', Sprenger and Hartmann explain the value of convincing evidence in terms (...)
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  3. Being Realist about Bayes, and the Predictive Processing Theory of Mind.Matteo Colombo, Lee Elkin & Stephan Hartmann - 2021 - British Journal for the Philosophy of Science 72 (1):185-220.
    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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  4. 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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  5. 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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  6. 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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  7. 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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  8. 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.  12
    Testimony.Luc Bovens & Stephan Hartmann - 2003 - In Luc Bovens & Stephan Hartmann (eds.), Bayesian Epistemology. Oxford: Oxford University Press.
    Addresses ‘too-odd-not-to-be-true’ reasoning in the assessment of testimony. This is the curious phenomenon that an initially less plausible report from multiple independent witnesses may elicit more confidence than an initially more plausible report.
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  10. Bayesian Argumentation and the Value of Logical Validity.Benjamin Eva & Stephan Hartmann - unknown
    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 (i)utilizes a new class of Bayesian learning methods that are better suited to modelling dynamic and conditional inferences than (...)
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  11. The World as a Process: Simulations in the Natural and Social Sciences.Stephan Hartmann - 1996 - In Rainer Hegselmann et al (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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  12. 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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  13. Bayesian Epistemology.Stephan Hartmann & Jan Sprenger - 2010 - In Sven Bernecker & Duncan Pritchard (eds.), The Routledge Companion to Epistemology. New York: 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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  14.  84
    Bayes Nets and Rationality.Stephan Hartmann - 2021 - In Markus Knauff & Wolfgang Spohn (eds.), The Handbook of Rationality. London: MIT Press.
    Bayes nets are a powerful tool for researchers in statistics and artificial intelligence. This chapter demonstrates that they are also of much use for philosophers and psychologists interested in (Bayesian) rationality. To do so, we outline the general methodology of Bayes nets modeling in rationality research and illustrate it with several examples from the philosophy and psychology of reasoning and argumentation. Along the way, we discuss the normative foundations of Bayes nets modeling and address some of the methodological problems it (...)
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  15. 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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  16. Hawking radiation and analogue experiments: A Bayesian analysis.Radin Dardashti, Stephan Hartmann, Karim P. Y. 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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  17. The Open Systems View.Michael E. Cuffaro & Stephan Hartmann - 2023
    There is a deeply entrenched view in philosophy and physics, the closed systems view, according to which isolated systems are conceived of as fundamental. On this view, when a system is under the influence of its environment this is described in terms of a coupling between it and a separate system which taken together are isolated. We argue against this view, and in favor of the alternative open systems view, for which systems interacting with their environment are conceived of as (...)
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  18. 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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  19. 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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  20. Bayesian Epistemology.Alan Hájek & Stephan Hartmann - 1992 - In Jonathan Dancy & Ernest Sosa (eds.), A Companion to Epistemology. Malden, MA: Wiley-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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  21. 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, 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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  23. The No Miracles Argument without the Base Rate Fallacy.Richard Dawid & Stephan Hartmann - 2016 - 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. 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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  25. Confirmation and Reduction: a Bayesian Account.Foad Dizadji-Bahmani, Roman Frigg & Stephan Hartmann - 2010 - 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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  26. 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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  27. On the Origins of Old Evidence.Benjamin Eva & Stephan Hartmann - 2020 - Australasian Journal of Philosophy 98 (3):481-494.
    The problem of old evidence, first described by Glymour [1980], is still widely regarded as one of the most pressing foundational challenges to the Bayesian account of scientific reasoning. Many solutions have been proposed, but all of them have drawbacks and none is considered to be definitive. Here, we introduce and defend a new kind of solution, according to which hypotheses are confirmed when we become more confident that they provide the only way of accounting for the known evidence.
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  28. 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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  29. 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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  30.  73
    The Logic of Partial Supposition.Benjamin Eva & Stephan Hartmann - 2021 - Analysis (2):215-224.
    According to orthodoxy, there are two basic moods of supposition: indicative and subjunctive. The most popular formalizations of the corresponding norms of suppositional judgement are given by Bayesian conditionalization and Lewisian imaging, respectively. It is well known that Bayesian conditionalization can be generalized (via Jeffrey conditionalization) to provide a model for the norms of partial indicative supposition. This raises the question of whether imaging can likewise be generalized to model the norms of ‘partial subjunctive supposition’. The present article casts doubt (...)
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  31. Deliberation and confidence change.Nora Heinzelmann & Stephan Hartmann - 2022 - Synthese 200 (1):1-13.
    We argue that social deliberation may increase an agent’s confidence and credence under certain circumstances. An agent considers a proposition H and assigns a probability to it. However, she is not fully confident that she herself is reliable in this assignment. She then endorses H during deliberation with another person, expecting him to raise serious objections. To her surprise, however, the other person does not raise any objections to H. How should her attitudes toward H change? It seems plausible that (...)
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  32. 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. The Myside Bias in Argument Evaluation: A Bayesian Model.Edoardo Baccini & Stephan Hartmann - 2022 - Proceedings of the Annual Meeting of the Cognitive Science Society 44:1512-1518.
    The "myside bias'' in evaluating arguments is an empirically well-confirmed phenomenon that consists of overweighting arguments that endorse one's beliefs or attack alternative beliefs while underweighting arguments that attack one's beliefs or defend alternative beliefs. This paper makes two contributions: First, it proposes a probabilistic model that adequately captures three salient features of myside bias in argument evaluation. Second, it provides a Bayesian justification of this model, thus showing that myside bias has a rational Bayesian explanation under certain conditions.
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  34.  80
    (1 other version)Models in science.Stephan Hartmann & Roman Frigg - 2014 - In Edward N. Zalta (ed.), The Stanford Encyclopedia of Philosophy. Stanford, CA: The Metaphysics Research Lab.
    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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  35.  99
    When no Reason for is a Reason against.Benjamin Eva & Stephan Hartmann - 2017 - 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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  36. 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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  37.  84
    Probabilities in Physics.Claus Beisbart & Stephan Hartmann (eds.) - 2011 - Oxford, GB: Oxford University Press.
    This volume is the first to provide a philosophical appraisal of probabilities in all of physics.
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  38. Idealization in Quantum Field Theory.Stephan Hartmann - 1990 - 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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  39. 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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  40.  48
    Conditionals and Testimony.Stephan Hartmann, Peter J. Collins, Karolina Krzyżanowska, Gregory Wheeler & Ulrike Hahn - 2020 - Cognitive Psychology 122.
    Conditionals and conditional reasoning have been a long-standing focus of research across a number of disciplines, ranging from psychology through linguistics to philosophy. But almost no work has concerned itself with the question of how hearing or reading a conditional changes our beliefs. Given that we acquire much—perhaps most—of what we believe through the testimony of others, the simple matter of acquiring conditionals via others’ assertion of a conditional seems integral to any full understanding of the conditional and conditional reasoning. (...)
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  41. Nancy Cartwright’s Philosophy of Science.Stephan Hartmann, Luc Bovens & Carl Hoefer (eds.) - 2008 - New York: 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 (...)
  42. 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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  43. 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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  44. Confirmation by Explanation: A Bayesian Justification of IBE.Marko Tesic, Benjamin Eva & Stephan Hartmann - manuscript
    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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  45. 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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  46.  7
    (1 other version)Models and stories in Hadron physics.Stephan Hartmann - 1999 - In Mary S. Morgan & Margaret Morrison (eds.), Models as Mediators: Perspectives on Natural and Social Science. Cambridge University Press. pp. 326-346.
    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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  47. Voting, deliberation and truth.Stephan Hartmann & Soroush Rafiee Rad - 2016 - Synthese 195 (3):1-21.
    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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  48.  18
    Introduction.Claus Beisbart & Stephan Hartmann - 2011 - In Claus Beisbart & Stephan Hartmann (eds.), Probabilities in Physics. Oxford, GB: Oxford University Press.
    Many results of modern physics—those of quantum mechanics, for instance—come in a probabilistic guise. But what do probabilistic statements in physics mean? Are probabilities matters of objective fact and part of the furniture of the world, as objectivists think? Or do they only express ignorance or belief, as Bayesians suggest? And how are probabilistic hypotheses justified and supported by empirical evidence? Finally, what does the probabilistic nature of physics imply for our understanding of the world? This volume is the first (...)
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  49. 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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    A New Approach to Testimonial Conditionals.Stephan Hartmann & Ulrike Hahn - 2020 - In Stephan Hartmann & Ulrike Hahn (eds.), CogSci 2020 Proceedings. Toronto, Ontario, Kanada: pp. 981–986.
    Conditionals pervade every aspect of our thinking, from the mundane and everyday such as ‘if you eat too much cheese, you will have nightmares’ to the most fundamental concerns as in ‘if global warming isn’t halted, sea levels will rise dramatically’. Many decades of research have focussed on the semantics of conditionals and how people reason from conditionals in everyday life. Here it has been rather overlooked how we come to such conditionals in the first place. In many cases, they (...)
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