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Confirmation and reduction: a Bayesian accountSynthese 179 (2): 321-328. 2010.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…Read more
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49Scientific ModelsIn Sahotra Sarkar et al (ed.), The Philosophy of Science: An Encyclopedia, Vol. 2, Routledge. 2005.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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17Modeling High-Temperature Superconductivity: Correspondence at Bay?In Lena Soler (ed.), Rethinking Scientific Change. Stabilities, Ruptures, Incommensurabilities?, Springer. pp. 107-128. 2008.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 empirical 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…Read more
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29Welfarism and the Assessments of Social Decision RulesIn Jerome Lang & Ulle Endriss (eds.), Computational Social Choice 2006, University of Amsterdam. 2006.The choice of a social decision rule for a federal assembly affects the welfare distribution within the federation. But which decision rules can be recommended on welfarist grounds? In this paper, we focus on two welfarist desiderata, viz. (i) maximizing the expected utility of the whole federation and (ii) equalizing the expected utilities of people from dif- ferent states in the federation. We consider the European Union as an example, set up a probabilistic model of decision making and explor…Read more
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51Belief expansion, contextual fit and the reliability of information sourcesIn Varol Akman, Paolo Bouquet, Richmond Thomason & Roger A. Young (eds.), Modeling and Using Context, volume 2116 of, Springer-verlag. 1999.We develop a probabilistic criterion for belief expansion that is sensitive to the degree of contextual fit of the new information to our belief set as well as to the reliability of our information source. We contrast our approach with the success postulate in AGM-style belief revision and show how the idealizations in our approach can be relaxed by invoking Bayesian-Network models.
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82Models as a Tool for Theory Construction: Some Strategies of Preliminary PhysicsIn William Herfel et al (ed.), Theories and Models in Scientific Processes, Rodopi. pp. 49-67. 1995.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 four kinds of models. These ar…Read more
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60Mechanisms, coherence, and theory choice in the cognitive neurosciencesIn Peter McLaughlin, Peter Machamer & Rick Grush (eds.), Theory and Method in the Neurosciences, Pittsburgh University Press. 2001.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 foo…Read more
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4How to expand your beliefs in an uncertain world: a probabilistic modelIn Gabriele Kern-Isberner, Thomas Lukasiewicz & Emil Weydert (eds.), Ki-2001 Workshop: Uncertainty in Artificial Intellligence. 2001.
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18How to Expand Your Beliefs in an Uncertain World: A Probabilistic ModelIn Gabriele Kern-Isberner, Thomas Lukasiewicz & Emil Weydert (eds.), Ki-2001 Workshop: Uncertainty in Artificial Intellligence. 2001.Suppose that we acquire various items of information from various sources and that our degree of confidence in the content of the information set is sufficiently high to believe the information. Now a new item of information is being presented by a new information source. Are we justified to add this new item of information to what we already believe? Consider the following parable: “I go to a lecture about wildlife in Greenland which was supposed to be delivered by an expert in the field…Read more
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8How to expand your beliefs in an uncertain world: a probabilistic modelIn Gabriele Kern-Isberner, Thomas Lukasiewicz & Emil Weydert (eds.), Ki-2001 Workshop: Uncertainty in Artificial Intellligence. 2001.Suppose that we acquire various items of information from various sources and that our degree of confidence in the content of the information set is sufficiently high to believe the information. Now a new item of information is being presented by a new information source. Are we justified to add this new item of information to what we already believe? Consider the following parable: “I go to a lecture about wildlife in Greenland which was supposed to be delivered by an expert in the field…Read more
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A probabilistic theory of the coherence of an information setIn Stephan Hartmann & Luc Bovens (eds.), Argument and Analysis: a Selection of Papers Contributed to the Sections of the 4th International Congress of the Society for An, . 2001.
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2Special issue on Bayesian epistemology edited by L. Bovens and S. HartmannSynthese 156 (3). 2007.
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2Book review: inference to the best explanation by P. Lipton (review)Notre Dame Philosophical Reviews. 2005.
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8Assessing the status of the common cause principleIn Thomas Uebel (ed.), New Directions in the Philosophy of Science, Springer. pp. 433-442. 2014.The Common Cause Principle, stating that correlations are either consequences of a direct causal link between the correlated events or are due to a common cause, is assessed from the perspective of its viability and it is argued that at present we do not have strictly empirical evidence that could be interpreted as disconfirming the principle. In particular it is not known whether spacelike correlations predicted by quantum field theory can be explained by properly localized common causes, and E…Read more
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3Experimental realism defended: how inference to the most likely cause might be soundIn Stephan Hartmann, Luc Bovens & Carl Hoefer (eds.), Nancy Cartwright’s Philosophy of Science, Routledge. 2008.
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Open Systems: Physics, Metaphysics, and Methodology (2025: Oxford University Press) (edited book)Oxford University Press. forthcoming.
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106Models as a Tool for Theory Construction: Some Strategies of Preliminary PhysicsIn William Herfel et al (ed.), Theories and Models in Scientific Processes, Rodopi. pp. 49-67. 1995.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 four kinds of models. These ar…Read more
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8ReliabilityIn Luc Bovens & Stephan Hartmann (eds.), Bayesian Epistemology, Oxford University Press. 2003.Introduces different interpretations of witness reliability into the models and constructs Bayesian-Network representations. Applies the models to Condorcet-style jury voting and Tversky and Kahneman’s Linda puzzle.
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35Mechanisms, Coherence, and Theory Choice in the Cognitive NeurosciencesIn Peter Machamer et al (ed.), Theory and Method in the Neurosciences., . 2001.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.2 In a fo…Read more
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11Bayesian networks in philosophyIn Benedikt Löwe, Wolfgang Malzkorn & Thoralf Räsch (eds.), Foundations of the Formal Sciences Ii: Applications of Mathematical Logic in Philosophy and Linguistics, Springer Verlag. pp. 39-46. 2003.There is a long philosophical tradition of addressing questions in philosophy of science and epistemology by means of the tools of Bayesian probability theory (see Earman (1992) and Howson and Urbach (1993)). In the late '70s, an axiomatic approach to conditional independence was developed within a Bayesian framework. This approach in conjunction with developments in graph theory are the two pillars of the theory of Bayesian Networks, which is a theory of probabilistic reasoning in artific…Read more
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7IntroductionIn Luc Bovens & Stephan Hartmann (eds.), Bayesian Epistemology, Oxford University Press. 2003.Introduces the topic of information-gathering from multiple independent sources through some well-known Genesis stories.
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7ConfirmationIn Luc Bovens & Stephan Hartmann (eds.), Bayesian Epistemology, Oxford University Press. 2003.Applies the models to the confirmation of scientific hypotheses by means of partially reliable test instruments. Shows that the variety-of-evidence thesis is false under certain plausible interpretations and assesses the Duhem–Quine thesis for positively relevant versus independent hypotheses and auxiliaries.
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8Models and stories in Hadron physicsIn Mary S. Morgan & Margaret Morrison (eds.), Models as Mediators: Perspectives on Natural and Social Science, Cambridge University Press. pp. 326-346. 1999.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 in this domain, to…Read more
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84Models in scienceIn Ed Zalta (ed.), Stanford Encyclopedia of Philosophy, Stanford Encyclopedia of Philosophy. 2012.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.…Read more
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10InformationIn Luc Bovens & Stephan Hartmann (eds.), Bayesian Epistemology, Oxford University Press. 2003.Shows how the degree of confidence that information from multiple sources is a function of the plausibility and the coherence of the information as well as of the reliability of the sources. There is a long-standing question in epistemology about how to construct a measure that yields a coherence ordering over sets of propositions and there are various proposals in the literature. Presents an impossibility result to the effect that there cannot exist such a measure. This has implications for the…Read more
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15TestimonyIn Luc Bovens & Stephan Hartmann (eds.), Bayesian Epistemology, Oxford University Press. 2003.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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6CoherenceIn Luc Bovens & Stephan Hartmann (eds.), Bayesian Epistemology, Oxford University Press. 2003.Shows how to construct a coherence quasi-ordering that respects the claim that the more coherent a set of propositions is, the greater the degree of confidence ought to be in its content, ceteris paribus. Applies this result to the problem of scientific-theory choice.
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14EpilogueIn Luc Bovens & Stephan Hartmann (eds.), Bayesian Epistemology, Oxford University Press. 2003.Presents some general reflections on the role and the challenges of probabilistic modelling in philosophy.
Munich, BY, Germany
Areas of Interest
Philosophy of Social Science |
Philosophy of Computing and Information |