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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  1. Deliberation and Confidence Change.Nora Heinzelmann & Stephan Hartmann - manuscript
    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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  2. Christianity & Science in Harmony?Robert W. P. Luk - 2021 - Science and Philosophy 9 (2):61-82.
    A worldview that does not involve religion or science seems to be incomplete. However, a worldview that includes both religion and science may arouse concern of incompatibility. This paper looks at the particular religion, Christianity, and proceeds to develop a worldview in which Christianity and Science are compatible with each other. The worldview may make use of some ideas of Christianity and may involve some author’s own ideas on Christianity. It is thought that Christianity and Science are in harmony in (...)
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  3. A Bayesian Solution to Hallsson's Puzzle.Thomas Mulligan - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy:1-14.
    Politics is rife with motivated cognition. People do not dispassionately engage with the evidence when they form political beliefs; they interpret it selectively, generating justifications for their desired conclusions and reasons why contrary evidence should be ignored. Moreover, research shows that epistemic ability (e.g. intelligence and familiarity with evidence) is correlated with motivated cognition. Bjørn Hallsson has pointed out that this raises a puzzle for the epistemology of disagreement. On the one hand, we typically think that epistemic ability in an (...)
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  4. The Fate of Explanatory Reasoning in the Age of Big Data.Frank Cabrera - 2021 - Philosophy and Technology 34 (4):645-665.
    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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  5. On Measurement Scales: Neither Ordinal Nor Interval?Cristian Larroulet Philippi - 2021 - Philosophy of Science 88 (5):929-939.
    There is a received view on measurement scales. It includes both a classification of scales and a set of prescriptions regarding measurement inferences. This article casts doubt on the adequacy of this received view. To do this, I propose an epistemic characterization of the ordinal/interval distinction, that is, one in terms of researchers’ beliefs. This novel characterization reveals the ordinal/interval distinction as too coarse grained and thus the received view as too restrictive of a framework for measurement research.
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  6. Normative Formal Epistemology as Modelling.Joe Roussos - forthcoming - The British Journal for the Philosophy of Science.
    I argue that normative formal epistemology (NFE) is best understood as modelling, in the sense that this is the reconstruction of its methodology on which NFE is doing best. I focus on Bayesianism and show that it has the characteristics of modelling. But modelling is a scientific enterprise, while NFE is normative. I thus develop an account of normative models on which they are idealised representations put to normative purposes. Normative assumptions, such as the transitivity of comparative credence, are characterised (...)
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  7. Scientific Realism and Empirical Confirmation: A Puzzle.Simon Allzén - 2021 - Studies in History and Philosophy of Science Part A 90:153-159.
    Scientific realism driven by inference to the best explanation (IBE) takes empirically confirmed objects to exist,independent,paceempiricism, of whether those objects are observable or not. This kind of realism, it has beenclaimed, does not need probabilistic reasoning to justify the claim that these objects exist. But I show that thereare scientific contexts in which a non-probabilistic IBE-driven realism leads to a puzzle. Since IBE can be appliedin scientific contexts in which empirical confirmation has not yet been reached, realists will in these (...)
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  8. Probability Logic.Niki Pfeifer - forthcoming - In M. Knauff & Wolfgang Spohn (eds.), Handbook of Rationality. Cambridge, MA, USA:
    This chapter presents probability logic as a rationality framework for human reasoning under uncertainty. Selected formal-normative aspects of probability logic are discussed in the light of experimental evidence. Specifically, probability logic is characterized as a generalization of bivalent truth-functional propositional logic (short “logic”), as being connexive, and as being nonmonotonic. The chapter discusses selected argument forms and associated uncertainty propagation rules. Throughout the chapter, the descriptive validity of probability logic is compared to logic, which was used as the gold standard (...)
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  9. Machine-Believers Learning Faiths & Knowledges: The New Gospel of Artificial Intelligence.Virgil W. Brower - 2021 - Internationales Jahrbuch Für Medienphilosophie 7 (1):97-121.
    One is occasionally reminded of Foucault's proclamation in a 1970 interview that "perhaps, one day this century will be known as Deleuzian." Less often is one compelled to update and restart with a supplementary counter-proclamation of the mathematician, David Lindley: "the twenty-first century would be a Bayesian era..." The verb tenses of both are conspicuous. // To critically attend to what is today often feared and demonized, but also revered, deployed, and commonly referred to as algorithm(s), one cannot avoid the (...)
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  10. Settling the Unsettled: Roles for Belief.Elizabeth Jackson - 2021 - Analysis 81 (2):359-368.
    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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  11. The Problem of Model Selection and Scientific Realism.Stanislav Larski - unknown
    This thesis has two goals. Firstly, we consider the problem of model selection for the purposes of prediction. In modern science predictive mathematical models are ubiquitous and can be found in such diverse fields as weather forecasting, economics, ecology, mathematical psychology, sociology, etc. It is often the case that for a given domain of inquiry there are several plausible models, and the issue then is how to discriminate between them – this is the problem of model selection. We consider approaches (...)
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  12. Probabilities with Gaps and Gluts.Dominik Klein, Ondrej Majer & Soroush Rafiee Rad - 2021 - Journal of Philosophical Logic 50 (5):1107-1141.
    Belnap-Dunn logic, sometimes also known as First Degree Entailment, is a four-valued propositional logic that complements the classical truth values of True and False with two non-classical truth values Neither and Both. The latter two are to account for the possibility of the available information being incomplete or providing contradictory evidence. In this paper, we present a probabilistic extension of BD that permits agents to have probabilistic beliefs about the truth and falsity of a proposition. We provide a sound and (...)
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  13. “Adding Up” Reasons: Lessons for Reductive and Nonreductive Approaches.Shyam Nair - 2021 - Ethics 132 (1):38-88.
    How do multiple reasons combine to support a conclusion about what to do or believe? This question raises two challenges: How can we represent the strength of a reason? How do the strengths of multiple reasons combine? Analogous challenges about confirmation have been answered using probabilistic tools. Can reductive and nonreductive theories of reasons use these tools to answer their challenges? Yes, or more exactly: reductive theories can answer both challenges. Nonreductive theories, with the help of a result in confirmation (...)
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  14. A Battle in the Statistics Wars: A Simulation-Based Comparison of Bayesian, Frequentist and Williamsonian Methodologies.Mantas Radzvilas, William Peden & Francesco De Pretis - 2021 - Synthese 199 (5-6):13689-13748.
    The debates between Bayesian, frequentist, and other methodologies of statistics have tended to focus on conceptual justifications, sociological arguments, or mathematical proofs of their long run properties. Both Bayesian statistics and frequentist (“classical”) statistics have strong cases on these grounds. In this article, we instead approach the debates in the “Statistics Wars” from a largely unexplored angle: simulations of different methodologies’ performance in the short to medium run. We conducted a large number of simulations using a straightforward decision problem based (...)
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  15. A Bayesian Analysis of Debunking Arguments in Ethics.Shang Long Yeo - forthcoming - Philosophical Studies:1-20.
    Debunking arguments in ethics contend that our moral beliefs have dubious evolutionary, cultural, or psychological origins – hence concluding that we should doubt such beliefs. Debates about debunking are often couched in coarse-grained terms – about whether our moral beliefs are justified or not, for instance. In this paper, I propose a more detailed Bayesian analysis of debunking arguments, which proceeds in the fine-grained framework of rational confidence. Such analysis promises several payoffs: it highlights how debunking arguments don’t affect all (...)
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  16. Evidentialism, Inertia, and Imprecise Probability.William Peden - forthcoming - The British Journal for the Philosophy of Science:1-23.
    Evidentialists say that a necessary condition of sound epistemic reasoning is that our beliefs reflect only our evidence. This thesis arguably conflicts with standard Bayesianism, due to the importance of prior probabilities in the latter. Some evidentialists have responded by modelling belief-states using imprecise probabilities (Joyce 2005). However, Roger White (2010) and Aron Vallinder (2018) argue that this Imprecise Bayesianism is incompatible with evidentialism due to “inertia”, where Imprecise Bayesian agents become stuck in a state of ambivalence towards hypotheses. Additionally, (...)
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  17. Updating Incoherent Credences - Extending the Dutch Strategy Argument for Conditionalization.Glauber De Bona & Julia Staffel - forthcoming - Philosophy and Phenomenological Research.
    In this paper, we ask: how should an agent who has incoherent credences update when they learn new evidence? The standard Bayesian answer for coherent agents is that they should conditionalize; however, this updating rule is not defined for incoherent starting credences. We show how one of the main arguments for conditionalization, the Dutch strategy argument, can be extended to devise a target property for updating plans that can apply to them regardless of whether the agent starts out with coherent (...)
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  18. On the Best Accuracy Arguments for Probabilism.Michael Nielsen - forthcoming - Philosophy of Science.
    In a recent paper, Pettigrew (2021) reports a generalization of the celebrated accuracy-dominance theorem due to Predd et al. (2009). But Pettigrew’s proof is incorrect. I will explain the mistakes and provide a correct proof.
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  19. Bayesian Reasoning’s Power to Challenge Religion and Empirically Justify Atheism.Richard Carrier - 2021 - Socio-Historical Examination of Religion and Ministry 3 (1):75-95.
    Bayes’ Theorem is a simple mathematical equation that can model every empirical argument. Accordingly, once understood it can be used to analyze, criticize, or improve any argument in matters of fact. By extension, it can substantially improve an overall argument for atheism by revealing that god apologetics generally operates through the omission of evidence, and how every argument for there being a god becomes an argument against there being a god once you reintroduce all the pertinent evidence that the original (...)
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  20. Frá skoðunum til trúnaðar og aftur til baka: Yfirlit um bayesíska þekkingarfræði [English title: "From Belief to Credence and Back Again: An Overview of Bayesian Epistemology"].Finnur Dellsén - 2017 - Hugur 28:146-162.
    English abstract: This paper discusses the delicate relationship between traditional epistemology and the increasingly influential probabilistic (or ‘Bayesian’) approach to epistemology. The paper introduces some of the key ideas of probabilistic epistemology, including credences or degrees of belief, Bayes’ theorem, conditionalization, and the Dutch Book argument. The tension between traditional and probabilistic epistemology is brought out by considering the lottery and preface paradoxes as they relate to rational (binary) belief and credence respectively. It is then argued that this tension can (...)
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  21. Time's Arrow and Self-Locating Probability.Eddy Keming Chen - forthcoming - Philosophy and Phenomenological Research.
    One of the most difficult problems in the foundations of physics is what gives rise to the arrow of time. Since the fundamental dynamical laws of physics are (essentially) symmetric in time, the explanation for time's arrow must come from elsewhere. A promising explanation introduces a special cosmological initial condition, now called the Past Hypothesis: the universe started in a low-entropy state. Unfortunately, in a universe where there are many copies of us (in the distant ''past'' or the distant ''future''), (...)
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  22. A Conflict Between Indexical Credal Transparency and Relevance Confirmation.Joel Pust - 2021 - Philosophy of Science 88 (3):385-397.
    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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  23. There is Cause to Randomize.Cristian Larroulet Philippi - forthcoming - Philosophy of Science.
    While practitioners think highly of randomized studies, some philosophers argue that there is no epistemic reason to randomize. Here I show that their arguments do not entail their conclusion. Moreover, I provide novel reasons for randomizing in the context of interventional studies. The overall discussion provides a unified framework for assessing baseline balance, one that holds for interventional and observational studies alike. The upshot: practitioners’ strong preference for randomized studies can be defended in some cases, while still offering a nuanced (...)
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  24. Personal Identity and Its Properties.Eldar Sarajlic - 2021 - Philosophy and Public Issues - Filosofia E Questioni Pubbliche 10 (2):193-233.
    In this paper, I offer a conceptual framework for understanding and evaluating personal identity claims. I analyze ontological and political properties of personal identity separately, arguing that their conceptual (if not practical) separation is necessary for a proper evaluation of different identity claims. I use probability theory to bypass some of the logical difficulties in conceptualizing personal identity and discuss a case of transitional identification. Finally, I outline the guidelines for a justified liberal policy of recognition.
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  25. Fine‐Tuning, Weird Sorts of Atheism and Evidential Favouring.Tamaz Tokhadze - forthcoming - Analytic Philosophy.
    This paper defends a novel sceptical response to the fine-tuning argument for the existence of God (FTA). According to this response, even if FTA can establish, what I call, the confirmation proposition: ‘fine-tuning confirms the God hypothesis’, there is no reason to think that a strengthening of FTA can establish the evidence-favouring proposition: ‘fine-tuning favours the God hypothesis over its competitors’. My argument is that, any criteria for the explanation of fine-tuning that permit us to take the God hypothesis seriously (...)
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  26. Scoring, Truthlikeness, and Value.Igor Douven - 2021 - Synthese 199 (3-4):8281-8298.
    There is an ongoing debate about which rule we ought to use for scoring probability estimates. Much of this debate has been premised on scoring-rule monism, according to which there is exactly one best scoring rule. In previous work, I have argued against this position. The argument given there was based on purely a priori considerations, notably the intuition that scoring rules should be sensitive to truthlikeness relations if, and only if, such relations are present among whichever hypotheses are at (...)
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  27. 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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  28. 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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  29. 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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  30. 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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  31. 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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  32. On the Truth-Convergence of Open-Minded Bayesianism.Tom F. Sterkenburg & Rianne de Heide - forthcoming - Review of Symbolic Logic:1-37.
    Wenmackers and Romeijn [38] formalize ideas going back to Shimony [33] and Putnam [28] into an open-minded Bayesian inductive logic, that can dynamically incorporate statistical hypotheses proposed in the course of the learning process. In this paper, we show that Wenmackers and Romeijn’s proposal does not preserve the classical Bayesian consistency guarantee of merger with the true hypothesis. We diagnose the problem, and offer a forward-looking open-minded Bayesians that does preserve a version of this guarantee.
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  33. 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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  34. Correlation Isn’T Good Enough: Causal Explanation and Big Data. [REVIEW]Frank Cabrera - 2021 - Metascience 30 (2):335-338.
    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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  35. 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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  36. Why `Might'?Giorgio Sbardolini - manuscript
    Why do we use epistemic modals like 'might'? According to Factualism, the function of 'might' is to exchange information about state-of-affairs in the modal universe. As an alternative to Factualism, this paper offers a game-theoretic rationale for epistemic possibility operators in a Bayesian setting. The background picture is one whereby communication facilitates coordination, but coordination could fail if there's too much uncertainty, since the players' ability to share a belief is undermined. However, 'might' and related expressions can be used to (...)
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  37. "Objective Purport, Relational Confirmation, and the Presumption of Moral Objectivism: A Probabilistic Argument From Moral Experience".Tanner Hammond - 2021 - Southwest Philosophy Review 37 (1).
    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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  38. Bayesianism and Self-Doubt.Darren Bradley - 2020 - Synthese 199 (1-2):2225-2243.
    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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  39. 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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  40. 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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  41. Probabilistic Stability, Agm Revision Operators and Maximum Entropy.Krzysztof Mierzewski - forthcoming - Review of Symbolic Logic:1-34.
    Several authors have investigated the question of whether canonical logic-based accounts of belief revision, and especially the theory of AGM revision operators, are compatible with the dynamics of Bayesian conditioning. Here we show that Leitgeb's stability rule for acceptance, which has been offered as a possible solution to the Lottery paradox, allows to bridge AGM revision and Bayesian update: using the stability rule, we prove that AGM revision operators emerge from Bayesian conditioning by an application of the principle of maximum (...)
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  42. Conditional Learning Through Causal Models.Jonathan Vandenburgh - 2020 - Synthese (1-2):2415-2437.
    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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  43. 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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  44. 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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  45. 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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  46. Distention for Sets of Probabilities.Rush T. Stewart & Michael Nielsen - forthcoming - Philosophy of Science.
    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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  47. 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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  48. Erratum to: The Jeffreys–Lindley paradox and discovery criteria in high energy physics.Robert D. Cousins - 2020 - Synthese 197 (8):3643-3643.
  49. 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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  50. 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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