About this topic
Summary Bayesian Reasoning includes issues related to: 1. the probabilistic logic of evidential support for hypotheses;  2. the logic of comparative belief, belief strengths, and belief updating as represented by classical probability functions; 3. the logic of decision as represented in terms of utilities, probabilities, and expected utility maximization, including ways in which this logic may represent comparative preferences among acts or states of affairs; 4. Bayesian probabilistic treatments of causal influence (e.g. via Bayes nets); 5. studies of relationships between human performance and models of reasoning and decision of a Bayesian kind (as described in 1-4 above).
Key works

Bayesian reasoning includes a wide variety of topics and issues. For introductory overviews of Bayesian confirmation theory and decision theory, among the best texts available are Skyrms 1966 and Hacking 2001; at a somewhat more advanced level Urbach & Howson 1993 is essential reading. Key sources for Bayesian probability and decision theory include Ramsey 1926Savage 1954Jeffrey 1965, and Joyce 1999. The classic treatment of Bayes nets is Pearl 1988Chater & Oaksford 2008 is an excellent collection of articles on Bayesian modeling of natural human reasoning. Also see the Stanford Encyclopedia of Philosophy (online, Zalta 2004) for helpful articles on various aspects of Bayesian reasoning: e.g. on Bayes' Theorem, Bayesian Epistemology, Inductive Logic, Decision Theory, etc.

Introductions Hájek 2007; Joyce 2008; Hawthorne 2011; Talbott 2006; Vineberg 2011; Weirich 2009; Hitchcock 2008.
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2513 found
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  1. Against Methodological Gambling.Borut Trpin - forthcoming - Erkenntnis:1-21.
    Should a scientist rely on methodological triangulation? Heesen et al. (Synthese 196(8):3067–3081, 2019) recently provided a convincing affirmative answer. However, their approach requires belief gambles if the evidence is discordant. We instead propose epistemically modest triangulation (EMT), according to which one should withhold judgement in such cases. We show that for a scientist in a methodologically diffident situation the expected utility of EMT is greater than that of Heesen et al.’s (2019) triangulation or that of using a single method. We (...)
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  2. “Adding Up” Reasons: Lessons for Reductive and Non-Reductive Approaches.Shyam Nair - forthcoming - Ethics.
    How do multiple reasons combine to support a conclusion about what to do or believe? This question raises two challenges: (1) how can we represent the strength of a reason? (2) how do the strengths of multiple reasons combine?. Analogous challenges about confirmation have been answered using probabilistic tools. Can reductive and non-reductive theories of reasons use these tools to answer their challenges? Yes, or more exactly: Reductive theories can answer both challenges. Non-reductive theories, with the help of a (new?) (...)
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  3. Settling the Unsettled: Roles for Belief.Elizabeth Jackson - forthcoming - Analysis.
    In Unsettled Thoughts, Julia Staffel argues that non-ideal thinkers should seek to approximate ideal Bayesian rationality. She argues that the more rational you are, the more benefits of rationality you will enjoy. After summarizing Staffel’s main results, this paper looks more closely at two issues that arise later in the book: the relationship between Bayesian rationality and other kinds of rationality, and the role that outright belief plays in addition to credence. Ultimately, I argue that there are several roles that (...)
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  4. 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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  5. Persistent Disagreement and Polarization in a Bayesian Setting.Michael Nielsen & Rush T. Stewart - 2021 - British Journal for the Philosophy of Science 72 (1):51-78.
    For two ideally rational agents, does learning a finite amount of shared evidence necessitate agreement? No. But does it at least guard against belief polarization, the case in which their opinions get further apart? No. OK, but are rational agents guaranteed to avoid polarization if they have access to an infinite, increasing stream of shared evidence? No.
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  6. Coherence & Confirmation: The Epistemic Limitations to the Impossibility Theorems.Ted Poston - forthcoming - Kriterion - Journal of Philosophy.
    It is a widespread intuition that the coherence of independent reports provides a powerful reason to believe that the reports are true. Formal results by Huemer (1997), Olsson (2002, 2005), and Bovens and Hartmann (2003) prove that, under certain conditions, coherence cannot increase the probability of the target claim. These formal results, known as ‘the impossibility theorems’ have been widely discussed in the literature. They are taken to have significant epistemic upshot. In particular, they are taken to show that reports (...)
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  7. Fake News and Epistemic Vice: Combating a Uniquely Noxious Market.Megan Fritts & Frank Cabrera - forthcoming - Journal of the American Philosophical Association.
    The topic of fake news has received increased attention from philosophers since the term became a favorite of politicians (Habgood-Coote 2016; Dentith 2016). Notably missing from the conversation, however, is a discussion of fake news and conspiracy theory media as a market. This paper will take as its starting point the account of noxious markets put forward by Debra Satz (2010), and will argue that there is a pro tanto moral reason to restrict the market for fake news. Specifically, we (...)
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  8. A Modesty Proposal.Jennifer Rose Carr - 2019 - Synthese 198 (4):3581-3601.
    Accuracy-first epistemology aims to show that the norms of epistemic rationality can be derived from the effective pursuit of accuracy. This paper explores the prospects within accuracy-first epistemology for vindicating “modesty”: the thesis that ideal rationality permits uncertainty about one’s own rationality. I argue that accuracy-first epistemology faces serious challenges in accommodating three forms of modesty: uncertainty about what priors are rational, uncertainty about whether one’s update policy is rational, and uncertainty about what one’s evidence is. I argue that the (...)
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  9. Correlation Isn’T Good Enough: Causal Explanation and Big Data. [REVIEW]Frank Cabrera - forthcoming - Metascience:1-4.
    A review of Gary Smith and Jay Cordes: The Phantom Pattern Problem: The Mirage of Big Data. New York: Oxford University Press, 2020.
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  10. Conditionals and the Hierarchy of Causal Queries.Niels Skovgaard-Olsen, Simon Stephan & Michael Waldmann - 2021 - Journal of Experimental Psychology: General 1:xx.
    Recent studies indicate that indicative conditionals like "If people wear masks, the spread of Covid-19 will be diminished" require a probabilistic dependency between their antecedents and consequents to be acceptable (Skovgaard-Olsen et al., 2016). But it is easy to make the slip from this claim to the thesis that indicative conditionals are acceptable only if this probabilistic dependency results from a causal relation between antecedent and consequent. According to Pearl (2009), understanding a causal relation involves multiple, hierarchically organized conceptual dimensions: (...)
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  11. "Objective Purport, Relational Confirmation, and the Presumption of Moral Objectivism: A Probabilistic Argument From Moral Experience".Tanner Hammond - forthcoming - Southwest Philosophy Review.
    All else being equal, can granting the objective purport of moral experience support a presumption in favor of some form of moral objectivism? Don Loeb (2007) has argued that even if we grant that moral experience appears to present us with a realm of objective moral fact—something he denies we have reason to do in the first place—the objective purport of moral experience cannot by itself provide even prima facie support for moral objectivism. In this paper, I contend against Loeb (...)
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  12. Bayesianism and Self-Doubt.Darren Bradley - forthcoming - Synthese:1-19.
    How should we respond to evidence when our evidence indicates that we are rationally impaired? I will defend a novel answer based on the analogy between self-doubt and memory loss. To believe that one is now impaired and previously was not is to believe that one’s epistemic position has deteriorated. Memory loss is also a form of epistemic deterioration. I argue that agents who suffer from epistemic deterioration should return to the priors they had at an earlier time. I develop (...)
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  13. Learning as Hypothesis Testing: Learning Conditional and Probabilistic Information.Jonathan Vandenburgh - manuscript
    Complex constraints like conditionals ('If A, then B') and probabilistic constraints ('The probability that A is p') pose problems for Bayesian theories of learning. Since these propositions do not express constraints on outcomes, agents cannot simply conditionalize on the new information. Furthermore, a natural extension of conditionalization, relative information minimization, leads to many counterintuitive predictions, evidenced by the sundowners problem and the Judy Benjamin problem. Building on the notion of a `paradigm shift' and empirical research in psychology and economics, I (...)
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  14. Evil and the god of indifference.László Bernáth & Daniel Kodaj - 2020 - International Journal for Philosophy of Religion 88 (3):259-272.
    The evidential problem of evil involves a rarely discussed challenge, namely the challenge of defending theism against the hypothesis of a morally indifferent creator. Our argument uses a Bayesian framework and it starts by showing that if the only alternative to classical theism is naturalistic atheism, then fine-tuning can render theism virtually certain, even in the face of evil. But if the alternatives include the hypothesis of a morally indifferent creator, theism is defeated even if the fine-tuning premise is accepted. (...)
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  15. Conditional Learning Through Causal Models.Jonathan Vandenburgh - 2020 - Synthese.
    Conditional learning, where agents learn a conditional sentence ‘If A, then B,’ is difficult to incorporate into existing Bayesian models of learning. This is because conditional learning is not uniform: in some cases, learning a conditional requires decreasing the probability of the antecedent, while in other cases, the antecedent probability stays constant or increases. I argue that how one learns a conditional depends on the causal structure relating the antecedent and the consequent, leading to a causal model of conditional learning. (...)
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  16. Tightening the Statistical Resurrection Argument.Jude Arnout Durieux - manuscript
    McGrew & McGrew make a solid statistical case for the historicity of the resurrection. This article fills two lacunae in the argument given there, and repairs a conceptual error (making the first lacuna irrelevant in the process).
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  17. Coherence, Truthfulness, and Efficiency in Communication.Sherrilyn Roush - manuscript
    Why should we make our beliefs consistent or, more generally, probabilistically coherent? That it will prevent sure losses in betting and that it will maximize one’s chances of having accurate beliefs are popular answers. However, these justifications are self-centered, focused on the consequences of our coherence for ourselves. I argue that incoherence has consequences for others because it is liable to mislead others, to false beliefs about one’s beliefs and false expectations about one’s behavior. I argue that the moral obligation (...)
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  18. A Conflict Between Indexical Credal Transparency & Relevance Confirmation.Joel Pust - forthcoming - Philosophy of Science.
    According to the probabilistic relevance account of confirmation, E confirms H relative to background knowledge K just in case P(H/K&E) > P(H/K). This requires an inequality between the rational degree of belief in H determined relative to two bodies of total knowledge which are such that one (K&E) includes the other (K) as a proper part. In this paper, I argue that it is quite plausible that there are no two possible bodies of total knowledge for ideally rational agents meeting (...)
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  19. The Pursuit of Knowledge and the Problem of the Unconceived Alternatives.Fabio Sterpetti & Marta Bertolaso - 2020 - Topoi 39 (4):881-892.
    In the process of scientific discovery, knowledge ampliation is pursued by means of non-deductive inferences. When ampliative reasoning is performed, probabilities cannot be assigned objectively. One of the reasons is that we face the problem of the unconceived alternatives: we are unable to explore the space of all the possible alternatives to a given hypothesis, because we do not know how this space is shaped. So, if we want to adequately account for the process of knowledge ampliation, we need to (...)
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  20. Distention for Sets of Probabilities.Rush T. Stewart & Michael Nielsen - manuscript
    A prominent pillar of Bayesian philosophy is that, relative to just a few constraints, priors “wash out” in the limit. Bayesians often appeal to such asymptotic results as a defense against charges of excessive subjectivity. But, as Seidenfeld and coauthors observe, what happens in the short run is often of greater interest than what happens in the limit. They use this point as one motivation for investigating the counterintuitive short run phenomenon of dilation since, it is alleged, “dilation contrasts with (...)
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  21. Time-Slice Rationality and Self-Locating Belief.David Builes - 2020 - Philosophical Studies 177 (10):3033-3049.
    The epistemology of self-locating belief concerns itself with how rational agents ought to respond to certain kinds of indexical information. I argue that those who endorse the thesis of Time-Slice Rationality ought to endorse a particular view about the epistemology of self-locating belief, according to which ‘essentially indexical’ information is never evidentially relevant to non-indexical matters. I close by offering some independent motivations for endorsing Time-Slice Rationality in the context of the epistemology of self-locating belief.
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  22. Erratum to: The Jeffreys–Lindley paradox and discovery criteria in high energy physics.Robert D. Cousins - 2020 - Synthese 197 (8):3643-3643.
  23. Evidence and Explanation in Cicero's On Divination.Frank Cabrera - 2020 - Studies in History and Philosophy of Science Part A 82:34-43.
    In this paper, I examine Cicero’s oft-neglected De Divinatione, a dialogue investigating the legitimacy of the practice of divination. First, I offer a novel analysis of the main arguments for divination given by Quintus, highlighting the fact that he employs two logically distinct argument forms. Next, I turn to the first of the main arguments against divination given by Marcus. Here I show, with the help of modern probabilistic tools, that Marcus’ skeptical response is far from the decisive, proto-naturalistic assault (...)
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  24. The Fate of Explanatory Reasoning in the Age of Big Data.Frank Cabrera - forthcoming - Philosophy and Technology:1-21.
    In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In section 3, I argue that these methodological claims are in tension with a (...)
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  25. Evidence and Inductive Inference.Nevin Climenhaga - 2021 - In Maria Lasonen-Aarnio & Clayton Littlejohn (eds.), The Routledge Handbook of the Philosophy of Evidence. Routledge.
    This chapter presents a typology of the different kinds of inductive inferences we can draw from our evidence, based on the explanatory relationship between evidence and conclusion. Drawing on the literature on graphical models of explanation, I divide inductive inferences into (a) downwards inferences, which proceed from cause to effect, (b) upwards inferences, which proceed from effect to cause, and (c) sideways inferences, which proceed first from effect to cause and then from that cause to an additional effect. I further (...)
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  26. Kevin McCain and Ted Poston’s Best Explanations.Frank Cabrera - 2020 - International Journal for the Study of Skepticism 10 (2):1-10.
    In this critical notice, I focus my attention on the chapters that deal with the explanationist response to skepticism.
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  27. Bayesian Recalibration: A Generalization.Sherrilyn Roush - manuscript
    This develops a framework for second-order conditionalization on statements about one's own epistemic reliability. It is the generalization of the framework of "Second-Guessing" (2009) to the case where the subject is uncertain about her reliability. See also "Epistemic Self-Doubt" (2017).
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  28. Scientific Self-Correction: The Bayesian Way.Felipe Romero & Jan Sprenger - 2020 - Synthese:1-21.
    The enduring replication crisis in many scientific disciplines casts doubt on the ability of science to estimate effect sizes accurately, and in a wider sense, to self-correct its findings and to produce reliable knowledge. We investigate the merits of a particular countermeasure—replacing null hypothesis significance testing with Bayesian inference—in the context of the meta-analytic aggregation of effect sizes. In particular, we elaborate on the advantages of this Bayesian reform proposal under conditions of publication bias and other methodological imperfections that are (...)
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  29. Epistemic Self-Doubt.Sherrilyn Roush - 2017 - Stanford Encyclopedia of Philosophy.
    When we get evidence that tells us our belief-forming mechanisms may not be reliable this presents a thorny set of questions about whether and how to revise our original belief. This article analyzes aspects of the problem and a variety of approaches to its solution.
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  30. A Strike Against a Striking Principle.Dan Baras - 2020 - Philosophical Studies 177 (6):1501-1514.
    Several authors believe that there are certain facts that are striking and cry out for explanation—for instance, a coin that is tossed many times and lands in the alternating sequence HTHTHTHTHTHT…. According to this view, we have prima facie reason to believe that such facts are not the result of chance. I call this view the striking principle. Based on this principle, some have argued for far-reaching conclusions, such as that our universe was created by intelligent design, that there are (...)
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  31. Conditional Degree of Belief and Bayesian Inference.Jan Sprenger - 2020 - Philosophy of Science 87 (2):319-335.
    Why are conditional degrees of belief in an observation E, given a statistical hypothesis H, aligned with the objective probabilities expressed by H? After showing that standard replies are not satisfactory, I develop a suppositional analysis of conditional degree of belief, transferring Ramsey’s classical proposal to statistical inference. The analysis saves the alignment, explains the role of chance-credence coordination, and rebuts the charge of arbitrary assessment of evidence in Bayesian inference. Finally, I explore the implications of this analysis for Bayesian (...)
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  32. A Paradox of Evidential Equivalence.David Builes - 2020 - Mind 129 (513):113-127.
    Our evidence can be about different subject matters. In fact, necessarily equivalent pieces of evidence can be about different subject matters. Does the hyperintensionality of ‘aboutness’ engender any hyperintensionality at the level of rational credence? In this paper, I present a case which seems to suggest that the answer is ‘yes’. In particular, I argue that our intuitive notions of independent evidence and inadmissible evidence are sensitive to aboutness in a hyperintensional way. We are thus left with a paradox. While (...)
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  33. Literal Perceptual Inference.Alex Kiefer - 2017 - In Thomas Metzinger & Wanja Wiese (eds.), Philosophy and predictive processing. Frankfurt, Germany:
    In this paper, I argue that theories of perception that appeal to Helmholtz’s idea of unconscious inference (“Helmholtzian” theories) should be taken literally, i.e. that the inferences appealed to in such theories are inferences in the full sense of the term, as employed elsewhere in philosophy and in ordinary discourse. -/- In the course of the argument, I consider constraints on inference based on the idea that inference is a deliberate acton, and on the idea that inferences depend on the (...)
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  34. Probabilism for Stochastic Theories.Jeremy Steeger - 2019 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 66:34–44.
    I defend an analog of probabilism that characterizes rationally coherent estimates for chances. Specifically, I demonstrate the following accuracy-dominance result for stochastic theories in the C*-algebraic framework: supposing an assignment of chance values is possible if and only if it is given by a pure state on a given algebra, your estimates for chances avoid accuracy-dominance if and only if they are given by a state on that algebra. When your estimates avoid accuracy-dominance (roughly: when you cannot guarantee that other (...)
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  35. The Epistemology of Disagreement: Why Not Bayesianism?Thomas Mulligan - forthcoming - Episteme.
    Disagreement is a ubiquitous feature of human life, and philosophers have dutifully attended to it. One important question related to disagreement is epistemological: How does a rational person change her beliefs (if at all) in light of disagreement from others? The typical methodology for answering this question is to endorse a steadfast or conciliatory disagreement norm (and not both) on a priori grounds and selected intuitive cases. In this paper, I argue that this methodology is misguided. Instead, a thoroughgoingly Bayesian (...)
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  36. Self‐Locating Evidence and the Metaphysics of Time.David Builes - 2019 - Philosophy and Phenomenological Research 99 (2):478-490.
    I argue that different views in the metaphysics of time make different observational predictions in both classical and relativistic cases. Because different views in the metaphysics of time differ over which facts are merely indexical facts, they make different observational predictions about certain self-locating propositions. I argue for this thesis by distinguishing the three main updating procedures that apply in cases of self-locating uncertainty, and I present a series of cases which cumulatively show that every one of these updating procedures (...)
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  37. Mary and Fátima: A Modest C-Inductive Argument for Catholicism.Tyler Dalton Mcnabb & Joseph E. Blado - 2020 - Perichoresis 18 (5):55-65.
    C-Inductive arguments are arguments that increase the probability of a hypothesis. This can be contrasted with what is called a P-Inductive argument. A P-inductive argument is an argument that shows the overall probability of a hypothesis to be more probable than not. In this paper, we put forth a C-inductive argument for the truth of the Catholic hypothesis (CH). Roughly, we take CH to be the hypothesis that the core creedal beliefs found within the Catholic Tradition are true. Specifically, we (...)
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  38. Why is Bayesian Confirmation Theory Rarely Practiced?Robert W. P. Luk - 2019 - Science and Philosophy 7 (1):3-20.
    Bayesian confirmation theory is a leading theory to decide the confirmation/refutation of a hypothesis based on probability calculus. While it may be much discussed in philosophy of science, is it actually practiced in terms of hypothesis testing by scientists? Since the assignment of some of the probabilities in the theory is open to debate and the risk of making the wrong decision is unknown, many scientists do not use the theory in hypothesis testing. Instead, they use alternative statistical tests that (...)
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  39. Bayesian Philosophy of Science.Jan Sprenger & Stephan Hartmann - 2019 - Oxford University Press.
    Jan Sprenger and Stephan Hartmann offer a fresh approach to central topics in philosophy of science, including causation, explanation, evidence, and scientific models. Their Bayesian approach uses the concept of degrees of belief to explain and to elucidate manifold aspects of scientific reasoning.
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  40. Decision Theory with a Human Face. [REVIEW]David Strohmaier - 2019 - Philosophical Psychology 32 (3):432-435.
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  41. Biased Belief in the Bayesian Brain: A Deeper Look at the Evidence.Ben M. Tappin & Stephen Gadsby - 2019 - Consciousness and Cognition 68:107-114.
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  42. A Theory of Bayesian Groups.Franz Dietrich - 2019 - Noûs 53 (3):708-736.
    A group is often construed as one agent with its own probabilistic beliefs (credences), which are obtained by aggregating those of the individuals, for instance through averaging. In their celebrated “Groupthink”, Russell et al. (2015) require group credences to undergo Bayesian revision whenever new information is learnt, i.e., whenever individual credences undergo Bayesian revision based on this information. To obtain a fully Bayesian group, one should often extend this requirement to non-public or even private information (learnt by not all or (...)
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  43. A Defense of Belief-Credence Dualism.Elizabeth Jackson - 2018 - In João Luis Pereira Ourique (ed.), Proceedings of the Fifth Conference of the Brazilian Society of Analytic Philosophy. Pelotas, Brazil: pp. 77-78.
    I defend belief-credence dualism, the view that we have both beliefs and credences and both attitudes are equally fundamental. First, I explain belief, credence, and three views on their relationship. Then, I argue for dualism. I do so first by painting a picture of the mind on which belief and credence are two cognitive tools that we use for different purposes. Finally, I respond to two objections to dualism. I conclude that dualism is a promising view, and one that both (...)
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  44. String Theory, Non-Empirical Theory Assessment, and the Context of Pursuit.Frank Cabrera - 2018 - Synthese:1-29.
    In this paper, I offer an analysis of the radical disagreement over the adequacy of string theory. The prominence of string theory despite its notorious lack of empirical support is sometimes explained as a troubling case of science gone awry, driven largely by sociological mechanisms such as groupthink (e.g. Smolin 2006). Others, such as Dawid (2013), explain the controversy by positing a methodological revolution of sorts, according to which string theorists have quietly turned to nonempirical methods of theory assessment given (...)
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  45. The Perils of Parsimony.William Roche - 2018 - Journal of Philosophy 115 (9):485-505.
    It is widely thought in philosophy and elsewhere that parsimony is a theoretical virtue in that if T1 is more parsimonious than T2, then T1 is preferable to T2, other things being equal. This thesis admits of many distinct precisifications. I focus on a relatively weak precisification on which preferability is a matter of probability, and argue that it is false. This is problematic for various alternative precisifications, and even for Inference to the Best Explanation as standardly understood.
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  46. Intuition & Calibration.Jonathan M. Weinberg, Stephen Crowley, Chad Gonnerman, Ian Vandewalker & Stacey Swain - 2012 - Essays in Philosophy 13 (1):15.
    The practice of appealing to esoteric intuitions, long standard in analytic philosophy, has recently fallen on hard times. Various recent empirical results have suggested that philosophers are not currently able to distinguish good intuitions from bad. This paper evaluates one possible type of approach to this problematic methodological situation: calibration. Both critiquing and building on an argument from Robert Cummins, the paper explores what possible avenues may exist for the calibration of philosophical intuitions. It is argued that no good options (...)
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  47. The Objectivity of Subjective Bayesianism.Jan Sprenger - 2018 - European Journal for Philosophy of Science 8 (3):539-558.
    Subjective Bayesianism is a major school of uncertain reasoning and statistical inference. It is often criticized for a lack of objectivity: it opens the door to the influence of values and biases, evidence judgments can vary substantially between scientists, it is not suited for informing policy decisions. My paper rebuts these concerns by connecting the debates on scientific objectivity and statistical method. First, I show that the above concerns arise equally for standard frequentist inference with null hypothesis significance tests. Second, (...)
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  48. Carnap's Foundation of Probability Theory.D. van Dantzig - 1949 - Synthese 8 (1):459-470.
  49. De Se Beliefs and Centred Uncertainty.Silvia Milano - 2018 - Dissertation, London School of Economics and Political Science
    What kind of thing do you believe when you believe that you are in a certain place, that it is a certain time, and that you are a certain individual? What happens if you get lost, or lose track of the time? Can you ever be unsure of your own identity? These are the kind of questions considered in my thesis. Beliefs about where, when and who you are are what are called in the literature de se, or self-locating beliefs. (...)
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  50. Subjective Probability and its Dynamics.Alan Hajek & Julia Staffel - forthcoming - In Markus Knauff & Wolfgang Spohn (eds.), MIT Handbook of Rationality. MIT Press.
    This chapter is a philosophical survey of some leading approaches in formal epistemology in the so-called ‘Bayesian’ tradition. According to them, a rational agent’s degrees of belief—credences—at a time are representable with probability functions. We also canvas various further putative ‘synchronic’ rationality norms on credences. We then consider ‘diachronic’ norms that are thought to constrain how credences should respond to evidence. We discuss some of the main lines of recent debate, and conclude with some prospects for future research.
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