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One can think of belief in a binary way--you either believe something or you don't. One can also think of belief as something that comes in degrees--you can believe something to a number of different degrees. It has been popular in formal epistemology to think of beliefs in the latter way, as things which come in degrees, and to further maintain that such degrees of belief should should satisfy the probability axioms. Given this picture, it has been debated whether there are other normative constraints on what an agent's degrees of belief should be like. The probabilistic principles discussed in this area are largely proposals about what these further normative constraints on degrees of belief should be like.

Key works A classic description and defense of conditionalization can be found in Urbach & Howson 1993. An influential and critical discussion of Indifference Principles can be found in Van Fraassen 1989. Important discussions and applications of scoring rules are given in Oddie 1997 and Joyce 1998. An early and influential discussion of chance-credence principles is given by Lewis 1980. Reflection Principles were introduced and defended in Fraassen 1984 and van Fraassen 1995. Influential discussions of direct inference principles are given in Kyburg 1974 and Pollock 1990.
Introductions Good introductory discussions that cover many of the principles discussed in this section can be found in a number of places, including Urbach & Howson 1993Strevens manuscript and Weisberg 2011.
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Conditionalization
  1. Brad Armendt (1980). Is There a Dutch Book Argument for Probability Kinematics? Philosophy of Science 47 (4):583-588.
    Dutch Book arguments have been presented for static belief systems and for belief change by conditionalization. An argument is given here that a rule for belief change which under certain conditions violates probability kinematics will leave the agent open to a Dutch Book.
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  2. Frank Arntzenius (2003). Self-Locating Beliefs, Reflection, Conditionalization and Dutch Books. Journal of Philosophy 100:356-370.
  3. Frank Arntzenius (2003). Some Problems for Conditionalization and Reflection. Journal of Philosophy 100 (7):356-370.
  4. F. Bacchus, Mariam Thalos & H. E. Kyburg (1990). Against Conditionalization. Synthese 85 (3):475 - 506.
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  5. Fahiem Bacchus, Henry E. Kyburg Jr & Mariam Thalos (1990). Against Conditionalization. Synthese 85 (3):475 - 506.
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  6. Alexandru Baltag & Sonja Smets (2008). Probabilistic Dynamic Belief Revision. Synthese 165 (2):179 - 202.
    We investigate the discrete (finite) case of the Popper–Renyi theory of conditional probability, introducing discrete conditional probabilistic models for knowledge and conditional belief, and comparing them with the more standard plausibility models. We also consider a related notion, that of safe belief, which is a weak (non-negatively introspective) type of “knowledge”. We develop a probabilistic version of this concept (“degree of safety”) and we analyze its role in games. We completely axiomatize the logic of conditional belief, knowledge and safe belief (...)
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  7. Paul Bartha, How to Put Self-Locating Information in its Place.
    How can self-locating propositions be integrated into normal patterns of belief revision? Puzzles such as Sleeping Beauty seem to show that such propositions lead to violation of ordinary principles for reasoning with subjective probability, such as Conditionalization and Reflection. I show that sophisticated forms of Conditionalization and Reflection are not only compatible with self-locating propositions, but also indispensable in understanding how they can function as evidence in Sleeping Beauty and similar cases.
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  8. Paul Bartha & Christopher Hitchcock (1999). The Shooting-Room Paradox and Conditionalizing on Measurably Challenged Sets. Synthese 118 (3):403-437.
    We provide a solution to the well-known “Shooting-Room” paradox, developed by John Leslie in connection with his Doomsday Argument. In the “Shooting-Room” paradox, the death of an individual is contingent upon an event that has a 1/36 chance of occurring, yet the relative frequency of death in the relevant population is 0.9. There are two intuitively plausible arguments, one concluding that the appropriate subjective probability of death is 1/36, the other that this probability is 0.9. How are these two values (...)
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  9. Johan van Benthem, Jelle Gerbrandy & Barteld Kooi (2009). Dynamic Update with Probabilities. Studia Logica 93 (1):67 - 96.
    Current dynamic-epistemic logics model different types of information change in multi-agent scenarios. We generalize these logics to a probabilistic setting, obtaining a calculus for multi-agent update with three natural slots: prior probability on states, occurrence probabilities in the relevant process taking place, and observation probabilities of events. To match this update mechanism, we present a complete dynamic logic of information change with a probabilistic character. The completeness proof follows a compositional methodology that applies to a much larger class of dynamic-probabilistic (...)
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  10. Isabel Guerra Bobo (2013). On Quantum Conditional Probability. Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 28 (1):115-137.
    We argue that quantum theory does not allow for a generalization of the notion of classical conditional probability by showing that the probability defined by the Lüders rule, standardly interpreted in the literature as the quantum-mechanical conditionalization rule, cannot be interpreted as such.Argumentamos que la teoría cuántica no admite una generalización de la noción clásica de probabilidad condicionada. Mostramos que la probabilidad definida por la regla de Lüders, interpretada generalmente como la regla de condicionalización mecánico-cuántica, no puede ser interpretada como (...)
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  11. Giacomo Bonanno (2005). A Simple Modal Logic for Belief Revision. Synthese 147 (2):193 - 228.
    We propose a modal logic based on three operators, representing intial beliefs, information and revised beliefs. Three simple axioms are used to provide a sound and complete axiomatization of the qualitative part of Bayes’ rule. Some theorems of this logic are derived concerning the interaction between current beliefs and future beliefs. Information flows and iterated revision are also discussed.
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  12. Darren Bradley (2013). Dynamic Beliefs and the Passage of Time. In A. Capone & N. Feit (eds.), Attitudes De Se. University of Chicago.
    How should our beliefs change over time? Much has been written about how our beliefs should change in the light of new evidence. But that is not the question I’m asking. Sometimes our beliefs change without new evidence. I previously believed it was Sunday. I now believe it’s Monday. In this paper I discuss the implications of such beliefs for philosophy of language. I will argue that we need to allow for ‘dynamic’ beliefs, that we need new norms of belief (...)
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  13. Darren Bradley (2011). Confirmation in a Branching World: The Everett Interpretation and Sleeping Beauty. British Journal for the Philosophy of Science 62 (2):323-342.
    Sometimes we learn what the world is like, and sometimes we learn where in the world we are. Are there any interesting differences between the two kinds of cases? The main aim of this article is to argue that learning where we are in the world brings into view the same kind of observation selection effects that operate when sampling from a population. I will first explain what observation selection effects are ( Section 1 ) and how they are relevant (...)
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  14. Darren Bradley (2011). Self-Location is No Problem for Conditionalization. Synthese 182 (3):393-411.
    How do temporal and eternal beliefs interact? I argue that acquiring a temporal belief should have no effect on eternal beliefs for an important range of cases. Thus, I oppose the popular view that new norms of belief change must be introduced for cases where the only change is the passing of time. I defend this position from the purported counter-examples of the Prisoner and Sleeping Beauty. I distinguish two importantly different ways in which temporal beliefs can be acquired and (...)
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  15. Darren Bradley (2010). Conditionalization and Belief De Se. Dialectica 64 (2):247-250.
    Colin Howson (1995 ) offers a counter-example to the rule of conditionalization. I will argue that the counter-example doesn't hit its target. The problem is that Howson mis-describes the total evidence the agent has. In particular, Howson overlooks how the restriction that the agent learn 'E and nothing else' interacts with the de se evidence 'I have learnt E'.
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  16. Darren Bradley (2007). Bayesianism And Self-Locating Beliefs. Dissertation, Stanford University
    How should we update our beliefs when we learn new evidence? Bayesian confirmation theory provides a widely accepted and well understood answer – we should conditionalize. But this theory has a problem with self-locating beliefs, beliefs that tell you where you are in the world, as opposed to what the world is like. To see the problem, consider your current belief that it is January. You might be absolutely, 100%, sure that it is January. But you will soon believe it (...)
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  17. Darren Bradley & Hannes Leitgeb (2006). When Betting Odds and Credences Come Apart: More Worries for Dutch Book Arguments. Analysis 66 (290):119–127.
    If an agent believes that the probability of E being true is 1/2, should she accept a bet on E at even odds or better? Yes, but only given certain conditions. This paper is about what those conditions are. In particular, we think that there is a condition that has been overlooked so far in the literature. We discovered it in response to a paper by Hitchcock (2004) in which he argues for the 1/3 answer to the Sleeping Beauty problem. (...)
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  18. Richard Bradley (2005). Radical Probabilism and Bayesian Conditioning. Philosophy of Science 72 (2):342-364.
    Richard Jeffrey espoused an antifoundationalist variant of Bayesian thinking that he termed ‘Radical Probabilism’. Radical Probabilism denies both the existence of an ideal, unbiased starting point for our attempts to learn about the world and the dogma of classical Bayesianism that the only justified change of belief is one based on the learning of certainties. Probabilistic judgment is basic and irreducible. Bayesian conditioning is appropriate when interaction with the environment yields new certainty of belief in some proposition but leaves one’s (...)
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  19. Richard Bradley (2005). Radical Probabilism and Bayesian Conditioning. Philosophy of Science 72 (2):342-364.
  20. Aaron Bronfman (forthcoming). Conditionalization and Not Knowing That One Knows. Erkenntnis:1-22.
    Bayesian Conditionalization is a widely used proposal for how to update one’s beliefs upon the receipt of new evidence. This is in part because of its attention to the totality of one’s evidence, which often includes facts about what one’s new evidence is and how one has come to have it. However, an increasingly popular position in epistemology holds that one may gain new evidence, construed as knowledge, without being in a position to know that one has gained this evidence. (...)
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  21. Peter M. Brown (1976). Conditionalization and Expected Utility. Philosophy of Science 43 (3):415-419.
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  22. Jeffrey Bub (1977). Von Neumann's Projection Postulate as a Probability Conditionalization Rule in Quantum Mechanics. Journal of Philosophical Logic 6 (1):381 - 390.
  23. Lara Buchak (2014). Learning Not to Be Naïve: A Comment on the Exchange Between Perrine/Wykstra and Draper. In Trent Dougherty & Justin McBrayer (eds.), Skeptical Theism: New Essays. Oxford University Press.
    Does postulating skeptical theism undermine the claim that evil strongly confirms atheism over theism? According to Perrine and Wykstra, it does undermine the claim, because evil is no more likely on atheism than on skeptical theism. According to Draper, it does not undermine the claim, because evil is much more likely on atheism than on theism in general. I show that the probability facts alone do not resolve their disagreement, which ultimately rests on which updating procedure – conditionalizing or updating (...)
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  24. David Chalmers (forthcoming). Frontloading and Fregean Sense: Reply to Neta, Schroeter, and Stanley. Analysis.
    A reply to Ram Neta, Laura Schroeter, and Jason Stanley, for a symposium in Analysis on Constructing the World.
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  25. David Christensen (1992). Confirmational Holism and Bayesian Epistemology. Philosophy of Science 59 (4):540-557.
    Much contemporary epistemology is informed by a kind of confirmational holism, and a consequent rejection of the assumption that all confirmation rests on experiential certainties. Another prominent theme is that belief comes in degrees, and that rationality requires apportioning one's degrees of belief reasonably. Bayesian confirmation models based on Jeffrey Conditionalization attempt to bring together these two appealing strands. I argue, however, that these models cannot account for a certain aspect of confirmation that would be accounted for in any adequate (...)
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  26. Jeffrey Conditionalization (2013). Cognitive Penetration 12—16 (See Also 'Objections to Dogmatism and/or Phenomenal Conservatism'). In Chris Tucker (ed.), Seemings and Justification: New Essays on Dogmatism and Phenomenal Conservatism. Oup Usa. 2--355.
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  27. H. A. Costa, J. Collins & I. Levi (1995). Desire-as-Belief Implies Opinionation or Indifference. Analysis 55 (1):2-5.
    The anti- Humean proposal of constructing desire as belief about what would be good must be abandoned on pain of triviality. Our central result shows that if an agent's belief- desire state is represented by Jeffrey's expected value theory enriched with the Desire as Belief Thesis (DAB), then, provided that three pairwise inconsistent propositions receive non- zero probability, the agent must view with indifference any proposition whose probability is greater than zero. Unlike previous results against DAB our Opinionation or Indifference (...)
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  28. Eleonora Cresto (2010). Belief and Contextual Acceptance. Synthese 177 (1):41-66.
    I develop a strategy for representing epistemic states and epistemic changes that seeks to be sensitive to the difference between voluntary and involuntary aspects of our epistemic life, as well as to the role of pragmatic factors in epistemology. The model relies on a particular understanding of the distinction between full belief and acceptance , which makes room for the idea that our reasoning on both practical and theoretical matters typically proceeds in a contextual way. Within this framework, I discuss (...)
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  29. Persi Diaconis & Sandy L. Zabell (1982). Updating Subjective Probability. Journal of the American Statistical Association 77 (380):822-830.
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  30. M. Richard Diaz (1980). Deductive Completeness and Conditionalization in Systems of Weak Implication. Notre Dame Journal of Formal Logic 21 (1):119-130.
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  31. Zoltan Domotor (1980). Probability Kinematics and Representation of Belief Change. Philosophy of Science 47 (3):384-403.
    Bayesian, Jeffrey and Field conditionals are compared and it is shown why the last two cannot be reduced to the first. Maximum relative entropy is used in two kinds of justification of the Field conditional and the dispensability of entropy principles in general is discussed.
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  32. Frank Doring (2000). Conditional Probability and Dutch Books. Philosophy of Science 67 (3):391 - 409.
    There is no set Δ of probability axioms that meets the following three desiderata: (1) Δ is vindicated by a Dutch book theorem; (2) Δ does not imply regularity (and thus allows, among other things, updating by conditionalization); (3) Δ constrains the conditional probability q(·,z) even when the unconditional probability p(z) (=q(z,T)) equals 0. This has significant consequences for Bayesian epistemology, some of which are discussed.
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  33. Frank Doring (1999). Why Bayesian Psychology Is Incomplete. Philosophy of Science 66 (S1):S379 - S389.
    Bayesian psychology, in what is perhaps its most familiar version, is incomplete: Jeffrey conditionalization is not a complete account of rational belief change. Jeffrey conditionalization is sensitive to the order in which the evidence arrives. This order effect can be so pronounced as to call for a belief adjustment that cannot be understood as an assimilation of incoming evidence by Jeffrey's rule. Hartry Field's reparameterization of Jeffrey's rule avoids the order effect but fails as an account of how new evidence (...)
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  34. Jon Dorling (1992). Bayesian Conditionalization Resolves Positivist/Realist Disputes. Journal of Philosophy 89 (7):362-382.
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  35. Igor Douven (1999). Inference to the Best Explanation Made Coherent. Philosophy of Science 66 (Supplement):S424-S435.
    Van Fraassen (1989) argues that Inference to the Best Explanation is incoherent in the sense that adopting it as a rule for belief change will make one susceptible to a dynamic Dutch book. The present paper argues against this. A strategy is described that allows us to infer to the best explanation free of charge.
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  36. Kai Draper (2013). The Evidential Relevance of Self-Locating Information. Philosophical Studies 166 (1):185-202.
    Philosophical interest in the role of self-locating information in the confirmation of hypotheses has intensified in virtue of the Sleeping Beauty problem. If the correct solution to that problem is 1/3, various attractive views on confirmation and probabilistic reasoning appear to be undermined; and some writers have used the problem as a basis for rejecting some of those views. My interest here is in two such views. One of them is the thesis that self-locating information cannot be evidentially relevant to (...)
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  37. Kai Draper (2010). Evidence Without Priors. Philo 13 (1):18-22.
    I argue that it is possible to acquire evidence that has no probability, not even zero, prior to its acquisition. If I am right then, contrary to certain Bayesian models of confirmation, conditionalization is not the only possible basis upon which a rational agent will alter her credence in some hypothesis in response to new evidence. My conclusion follows from certain analyses of the Sleeping Beauty problem. Because those analyses are controversial, however, I alter the Sleeping Beauty scenario to generate (...)
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  38. Jeffrey Dunn, Bayesian Epistemology and Having Evidence.
    Bayesian Epistemology is a general framework for thinking about agents who have beliefs that come in degrees. Theories in this framework give accounts of rational belief and rational belief change, which share two key features: (i) rational belief states are represented with probability functions, and (ii) rational belief change results from the acquisition of evidence. This dissertation focuses specifically on the second feature. I pose the Evidence Question: What is it to have evidence? Before addressing this question we must have (...)
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  39. Antony Eagle (ed.) (2010). Philosophy of Probability: Contemporary Readings. Routledge.
    Philosophy of Probability: Contemporary Readings is the first anthology to collect essential readings in this important area of philosophy. Featuring the work of leading philosophers in the field such as Carnap, Hájek, Jeffrey, Joyce, Lewis, Loewer, Popper, Ramsey, van Fraassen, von Mises, and many others, the book looks in depth at the following key topics: subjective probability and credence probability updating: conditionalization and reflection Bayesian confirmation theory classical, logical, and evidential probability frequentism physical probability: propensities and objective chances. The book (...)
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  40. John Earman (1992). Bayes or Bust? Bradford.
    There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes's original paper to contemporary formal learning theory. (...)
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  41. John Earman (1992). Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory. Mit Press.
  42. Kenny Easwaran (2013). Expected Accuracy Supports Conditionalization—and Conglomerability and Reflection. Philosophy of Science 80 (1):119-142.
  43. Kenny Easwaran (2011). Bayesianism II: Applications and Criticisms. Philosophy Compass 6 (5):321-332.
    In the first paper, I discussed the basic claims of Bayesianism (that degrees of belief are important, that they obey the axioms of probability theory, and that they are rationally updated by either standard or Jeffrey conditionalization) and the arguments that are often used to support them. In this paper, I will discuss some applications these ideas have had in confirmation theory, epistemol- ogy, and statistics, and criticisms of these applications.
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  44. Kenny Easwaran (2011). Varieties of Conditional Probability. In Prasanta Bandyopadhyay & Malcolm Forster (eds.), Handbook for Philosophy of Statistics. North Holland.
    I consider the notions of logical probability, degree of belief, and objective chance, and argue that a different formalism for conditional probability is appropriate for each.
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  45. Ellery Eells, Brian Skyrms & Ernest W. Adams (eds.) (1994). Probability and Conditionals: Belief Revision and Rational Decision. Cambridge University Press.
    This is a 'state of the art' collection of essays on the relation between probabilities, especially conditional probabilities, and conditionals. It provides new negative results which sharply limit the ways conditionals can be related to conditional probabilities. There are also positive ideas and results which will open up new areas of research. The collection is intended to honour Ernest W. Adams, whose seminal work is largely responsible for creating this area of inquiry. As well as describing, evaluating, and applying Adams' (...)
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  46. Don Fallis (2002). Goldman on Probabilistic Inference. Philosophical Studies 109 (3):223 - 240.
    In his recent book, Knowledge in a Social World, Alvin Goldman claims to have established that if a reasoner starts with accurate estimates of the reliability of new evidence and conditionalizes on this evidence, then this reasoner is objectively likely to end up closer to the truth. In this paper, I argue that Goldman's result is not nearly as philosophically significant as he would have us believe. First, accurately estimating the reliability of evidence – in the sense that Goldman requires (...)
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  47. Damien Fennell & Nancy Cartwright (2010). Does Roush Show That Evidence Should Be Probable? Synthese 175 (3):289 - 310.
    This paper critically analyzes Sherrilyn Roush's (Tracking truth: knowledge, evidence and science, 2005) definition of evidence and especially her powerful defence that in the ideal, a claim should be probable to be evidence for anything. We suggest that Roush treats not one sense of 'evidence' but three: relevance, leveraging and grounds for knowledge; and that different parts of her argument fare differently with respect to different senses. For relevance, we argue that probable evidence is sufficient but not necessary for Roush's (...)
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  48. Eduardo Fermé & Ricardo Rodríguez (2006). DFT and Belief Revision. Análisis Filosófico 26 (2):373-393.
    Alchourrón devoted his last years to the analysis of the notion of defeasible conditionalization. He developed a formal system capturing the essentials of this notion. His definition of the defeasible conditional is given in terms of strict implication operator and a modal operator f which is interpreted as a revision function at the language level. In this paper, we will point out that this underlying revision function is more general than the well known AGM revision [4]. In addition, we will (...)
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  49. Hartry Field (1978). A Note on Jeffrey Conditionalization. Philosophy of Science 45 (3):361-367.
    Bayesian decision theory can be viewed as the core of psychological theory for idealized agents. To get a complete psychological theory for such agents, you have to supplement it with input and output laws. On a Bayesian theory that employs strict conditionalization, the input laws are easy to give. On a Bayesian theory that employs Jeffrey conditionalization, there appears to be a considerable problem with giving the input laws. However, Jeffrey conditionalization can be reformulated so that the problem disappears, and (...)
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  50. Jesse Fitts (2014). Chalmers on the Objects of Credence. Philosophical Studies 170 (2):343-358.
    Chalmers (Mind 120(479): 587–636, 2011a) presents an argument against “referentialism” (and for his own view) that employs Bayesianism. He aims to make progress in a debate over the objects of belief, which seems to be at a standstill between referentialists and non-referentialists. Chalmers’ argument, in sketch, is that Bayesianism is incompatible with referentialism, and natural attempts to salvage the theory, Chalmers contends, requires giving up referentialism. Given the power and success of Bayesianism, the incompatibility is prima facie evidence against referentialism. (...)
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