About this topic
Summary

In Bayesian epistemology, an updating principle is a principle that specifies or puts restrictions on the changes in an agent’s belief state that follow (or should follow) some initial change in the agent’s belief state (usually – but maybe not always – as a result of the agent being exposed to new evidence). Although in other academic fields a great deal of the discussion regarding updating principles touches upon their empirical fit to the way people actually update their beliefs, much of the relevant philosophical literature is normative. The central questions are whether, why and in which contexts, obeying different updating principles is rationally required. In the simplest (but not uncommon) case, where the agent’s belief state can be represented by a single probability distribution over a set of propositions, and the initial change is that of learning a new proposition (represented as raising the probability of the learnt proposition to 1), the most popular updating rule is Bayesian Conditionalization. Richard Jeffrey offered a generalization of Bayesian Conditionalization, usually called “Jeffrey’s conditionalization”, to cases in which, although there is some initial change in the agent’s belief state, the probability of no proposition in the set is raised to 1. Others have introduced, discussed and explored the formal features of other updating principles. These principles are usually ones that either cover cases to which Jeffrey’s conditionalization does not apply (such as cases of “growing awareness” in which the initial change is represented as an addition of new propositions to the set or cases in which the agent’s initial belief set cannot be represented by a single probability distribution over a set of propositions) or constitute generalizations of or alternatives to Bayesian Conditionalization and Jeffrey’s Conditionalization in specific contexts (such as Adams’ conditionalization for the case of learning conditional probabilities, Imaging which – in some contexts – seem to fit better with other intuitive epistemic principles or different types of pooling methods for the case of learning other agents’ beliefs).

Key works

Jeffrey 1992 introduces the idea of probability kinematics and discusses its features. Some important discussions of Jeffrey’s rule include Field 1978, Fraassen 1980 and Skyrms 1987. Bradley 2005 introduces and discusses Adams’ Conditionalization. In Bradley 2017 Bradley also discusses growing awareness and the relation between belief updating and the updating of desires and preferences. “Imaging” was introduced and discussed in Lewis 1976 and Gardenfors 1982. Leitgeb 2017 discusses the relation between Imaging and different belief aggregation methods. Some discussions of different problems associated with updating imprecise probabilities are White 2009, Bradley & Steele 2014 and Joyce 2010.

Introductions Jeffrey 2004
Related categories

323 found
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1 — 50 / 323
  1. The Evidence Against Kronz.Peter Achinstein - 1992 - Philosophical Studies 67 (2):169-175.
    Frederick Kronz constructs interesting examples in an attempt to show deficiencies in my concept of evidence and the advantages in Carnap's positive relevance idea. His discussion raises general questions of importance in developing an adequate account of scientific evidence questions about the relationship between evidence and belief and the role of emphasis in determining evidence. His examples are challenging, but do they work?
  2. Probability and the Art of Judgement by Richard Jeffrey. [REVIEW]Ernest W. Adams - 1993 - Journal of Philosophy 90 (3):154-157.
  3. Probability and the Art of Judgement.Ernest W. Adams & Richard Jeffrey - 1993 - Journal of Philosophy 90 (3):154.
  4. The Ambiguity Aversion Literature: A Critical Assessment.Nabil I. Al-Najjar & Jonathan Weinstein - 2009 - Economics and Philosophy 25 (3):249-284.
    We provide a critical assessment of the ambiguity aversion literature, which we characterize in terms of the view that Ellsberg choices are rational responses to ambiguity, to be explained by relaxing Savage's Sure-Thing principle and adding an ambiguity-aversion postulate. First, admitting Ellsberg choices as rational leads to behaviour, such as sensitivity to irrelevant sunk cost, or aversion to information, which most economists would consider absurd or irrational. Second, we argue that the mathematical objects referred to as in the ambiguity aversion (...)
  5. Social Deliberation: Nash, Bayes, and the Partial Vindication of Gabriele Tarde.J. McKenzie Alexander - 2009 - Episteme 6 (2):164-184.
    At the very end of the 19th century, Gabriele Tarde wrote that all society was a product of imitation and innovation. This view regarding the development of society has, to a large extent, fallen out of favour, and especially so in those areas where the rational actor model looms large. I argue that this is unfortunate, as models of imitative learning, in some cases, agree better with what people actually do than more sophisticated models of learning. In this paper, I (...)
  6. Belief Revision Conditionals: Basic Iterated Systems.Horacio Arló-Costa - 1999 - Annals of Pure and Applied Logic 96 (1-3):3-28.
    It is now well known that, on pain of triviality, the probability of a conditional cannot be identified with the corresponding conditional probability [25]. This surprising impossibility result has a qualitative counterpart. In fact, Peter Gärdenfors showed in [13] that believing ‘If A then B’ cannot be equated with the act of believing B on the supposition that A — as long as supposing obeys minimal Bayesian constraints. Recent work has shown that in spite of these negative results, the question (...)
  7. Iterative Probability Kinematics.Horacio Arló-Costa & Richmond Thomason - 2001 - Journal of Philosophical Logic 30 (5):479-524.
    Following the pioneer work of Bruno De Finetti [12], conditional probability spaces (allowing for conditioning with events of measure zero) have been studied since (at least) the 1950's. Perhaps the most salient axiomatizations are Karl Popper's in [31], and Alfred Renyi's in [33]. Nonstandard probability spaces [34] are a well know alternative to this approach. Vann McGee proposed in [30] a result relating both approaches by showing that the standard values of infinitesimal probability functions are representable as Popper functions, and (...)
  8. Dutch Strategies for Diachronic Rules: When Believers See the Sure Loss Coming.Brad Armendt - 1992 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992:217 - 229.
    Two criticisms of Dutch strategy arguments are discussed: One says that the arguments fail because agents who know the arguments can use that knowledge to avoid Dutch strategy vulnerability, even though they violate the norm in question. The second consists of cases alleged to be counterexamples to the norms that Dutch strategy arguments defend. The principle of Reflection and its Dutch strategy argument are discussed, but most attention is given to the rule of Conditionalization and to Jeffrey's rule for fallible (...)
  9. Is There a Dutch Book Argument for Probability Kinematics?Brad Armendt - 1980 - 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.
  10. Some Problems for Conditionalization and Reflection.Frank Arntzenius - 2003 - Journal of Philosophy 100 (7):356-370.
  11. Reichenbach's Posits Reposited.David Atkinson & Jeanne Peijnenburg - 2007 - Erkenntnis 69 (1):93 - 108.
    Reichenbach’s use of ‘posits’ to defend his frequentistic theory of probability has been criticized on the grounds that it makes unfalsifiable predictions. The justice of this criticism has blinded many to Reichenbach’s second use of a posit, one that can fruitfully be applied to current debates within epistemology. We show first that Reichenbach’s alternative type of posit creates a difficulty for epistemic foundationalists, and then that its use is equivalent to a particular kind of Jeffrey conditionalization. We conclude that, under (...)
  12. Probability All the Way Up.David Atkinson & Jeanne Peijnenburg - 2006 - Synthese 153 (2):187-197.
    Richard Jeffrey’s radical probabilism (‘probability all the way down’) is augmented by the claim that probability cannot be turned into certainty, except by data that logically exclude all alternatives. Once we start being uncertain, no amount of updating will free us from the treadmill of uncertainty. This claim is cast first in objectivist and then in subjectivist terms.
  13. Three Aspects of Typicality in Multiverse Cosmology.Feraz Azhar - unknown
    Extracting predictions from cosmological theories that describe a multiverse, for what we are likely to observe in our domain, is crucial to establishing the validity of these theories. One way to extract such predictions is from theory-generated probability distributions that allow for selection effects---generally expressed in terms of assumptions about anthropic conditionalization and how typical we are. In this paper, I urge three lessons about typicality in multiverse settings. Because it is difficult to characterize our observational situation in the multiverse, (...)
  14. Spectra of Conditionalization and Typicality in the Multiverse.Feraz Azhar - unknown
    An approach to testing theories describing a multiverse, that has gained interest of late, involves comparing theory-generated probability distributions over observables with their experimentally measured values. It is likely that such distributions, were we indeed able to calculate them unambiguously, will assign low probabilities to any such experimental measurements. An alternative to thereby rejecting these theories, is to conditionalize the distributions involved by restricting attention to domains of the multiverse in which we might arise. In order to elicit a crisp (...)
  15. A Theory of Epistemic Risk.Boris Babic - unknown
    I propose a general alethic theory of epistemic risk according to which the riskiness of an agent's credence function encodes their relative sensitivity to different types of graded error. After motivating and mathematically developing this approach, I show that the epistemic risk function is a scaled reflection of expected inaccuracy. This duality between risk and information enables us to explore the relationship between attitudes to epistemic risk, the choice of scoring rule in epistemic utility theory, and the selection of priors (...)
  16. Against Conditionalization.F. Bacchus, Mariam Thalos & H. E. Kyburg - 1990 - Synthese 85 (3):475 - 506.
  17. Against Conditionalization.Fahiem Bacchus, Henry E. Kyburg & Mariam Thalos - 1990 - Synthese 85 (3):475-506.
  18. Against Conditionalization.Fahiem Bacchus, Henry E. Kyburg Jr & Mariam Thalos - 1990 - Synthese 85 (3):475 - 506.
  19. Probabilistic Dynamic Belief Revision.Alexandru Baltag & Sonja Smets - 2008 - 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 (...)
  20. Keep 'Hoping' for Rationality: A Solution to the Backward Induction Paradox.Alexandru Baltag, Sonja Smets & Jonathan Alexander Zvesper - 2009 - Synthese 169 (2):301 - 333.
    We formalise a notion of dynamic rationality in terms of a logic of conditional beliefs on (doxastic) plausibility models. Similarly to other epistemic statements (e.g. negations of Moore sentences and of Muddy Children announcements), dynamic rationality changes its meaning after every act of learning, and it may become true after players learn it is false. Applying this to extensive games, we “simulate” the play of a game as a succession of dynamic updates of the original plausibility model: the epistemic situation (...)
  21. Updating: A Psychologically Basic Situation of Probability Revision.Jean Baratgin & Guy Politzer - 2010 - Thinking and Reasoning 16 (4):253-287.
    The Bayesian model has been used in psychology as the standard reference for the study of probability revision. In the first part of this paper we show that this traditional choice restricts the scope of the experimental investigation of revision to a stable universe. This is the case of a situation that, technically, is known as focusing. We argue that it is essential for a better understanding of human probability revision to consider another situation called updating (Katsuno & Mendelzon, 1992), (...)
  22. The Psychology of Dynamic Probability Judgment: Order Effect, Normative Theories, and Experimental Methodology.Jean Baratgin & Guy Politzer - 2007 - Mind and Society 6 (1):53-66.
    The Bayesian model is used in psychology as the reference for the study of dynamic probability judgment. The main limit induced by this model is that it confines the study of revision of degrees of belief to the sole situations of revision in which the universe is static (revising situations). However, it may happen that individuals have to revise their degrees of belief when the message they learn specifies a change of direction in the universe, which is considered as changing (...)
  23. How to Put Self-Locating Information in its Place.Paul Bartha - unknown
    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.
  24. Probabilistic Reasoning in Cosmology.Yann Benétreau-Dupin - 2015 - Dissertation, The University of Western Ontario
    Cosmology raises novel philosophical questions regarding the use of probabilities in inference. This work aims at identifying and assessing lines of arguments and problematic principles in probabilistic reasoning in cosmology. -/- The first, second, and third papers deal with the intersection of two distinct problems: accounting for selection effects, and representing ignorance or indifference in probabilistic inferences. These two problems meet in the cosmology literature when anthropic considerations are used to predict cosmological parameters by conditionalizing the distribution of, e.g., the (...)
  25. Sleeping Beauty and Self-Location: A Hybrid Model.Nick Bostrom - 2007 - Synthese 157 (1):59-78.
    The Sleeping Beauty problem is test stone for theories about self-locating belief, i.e. theories about how we should reasons when data or theories contain indexical information. Opinion on this problem is split between two camps, those who defend the "1/2 view" and those who advocate the "1/3 view". I argue that both these positions are mistaken. Instead, I propose a new "hybrid" model, which avoids the faults of the standard views while retaining their attractive properties. This model _appears_ to violate (...)
  26. On the Revision of Probabilistic Belief States.Craig Boutilier - 1995 - Notre Dame Journal of Formal Logic 36 (1):158-183.
    In this paper we describe two approaches to the revision of probability functions. We assume that a probabilistic state of belief is captured by a counterfactual probability or Popper function, the revision of which determines a new Popper function. We describe methods whereby the original function determines the nature of the revised function. The first is based on a probabilistic extension of Spohn's OCFs, whereas the second exploits the structure implicit in the Popper function itself. This stands in contrast with (...)
  27. “P and I Will Believe That Not-P”: Diachronic Constraints on Rational Belief.Luc Bovens - 1995 - Mind 104 (416):737-760.
    I provide a taxonomy of the various circumstances under which one might reasonably say "P and I will believe that not-P" or violate the Reflection Principle.
  28. The Future Variant of Moore's Paradox.Luc Bovens - 1995 - In Jaakko Hintikka (ed.), The British Tradition in 20th Century Philosophy: Proceedings of the 17th International Wittgenstein-Symposium. Vienna, Austria: Hölder-Pichler-Tempsky.
    I provide a taxonomy of the various circumstances under which one might reasonably say "P and I will believe that not-P" or violate the Reflection Principle.
  29. Monty Hall Drives a Wedge Between Judy Benjamin and the Sleeping Beauty: A Reply to Bovens.Luc Bovens & Jose-Luis Ferreira - 2010 - Analysis 70 (3):473 - 481.
    In “Judy Benjamin is a Sleeping Beauty” (2010) Bovens recognises a certain similarity between the Sleeping Beauty (SB) and the Judy Benjamin (JB). But he does not recognise the dissimilarity between underlying protocols (as spelled out in Shafer (1985). Protocols are expressed in conditional probability tables that spell out the probability of coming to learn various propositions conditional on the actual state of the world. The principle of total evidence requires that we not update on the content of the proposition (...)
  30. Bets on Hats: On Dutch Books Against Groups, Degrees of Belief as Betting Rates, and Group-Reflection.Luc Bovens & Wlodek Rabinowicz - 2011 - Episteme 8 (3):281-300.
    The Puzzle of the Hats is a puzzle in social epistemology. It describes a situation in which a group of rational agents with common priors and common goals seems vulnerable to a Dutch book if they are exposed to different information and make decisions independently. Situations in which this happens involve violations of what might be called the Group-Reflection Principle. As it turns out, the Dutch book is flawed. It is based on the betting interpretation of the subjective probabilities, but (...)
  31. The Puzzle of the Hats.Luc Bovens & Wlodek Rabinowicz - 2010 - Synthese 172 (1):57-78.
    The Puzzle of the Hats is a betting arrangement which seems to show that a Dutch book can be made against a group of rational players with common priors who act in the common interest and have full trust in the other players’ rationality. But we show that appearances are misleading—no such Dutch book can be made. There are four morals. First, what can be learned from the puzzle is that there is a class of situations in which credences and (...)
  32. Self-Locating Belief and Updating on Learning.Darren Bradley - forthcoming - Mind.
    Beliefs that locate you in space or time are self-locating beliefs. These cause a problem for Bayesian models of belief. Miriam Schoenfield (2016) offers a solution – that on learning x, agents should update on the fact that they learned x. I will argue that Schoenfield’s suggestion does not solve the problem.
  33. Self-Location is No Problem for Conditionalization.Darren Bradley - 2011 - 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 (...)
  34. Conditionalization and Belief De Se.Darren Bradley - 2010 - 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'.
  35. Bayesianism And Self-Locating Beliefs.Darren Bradley - 2007 - 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 (...)
  36. Proposition-Valued Random Variables as Information.Richard Bradley - 2010 - Synthese 175 (1):17 - 38.
    The notion of a proposition as a set of possible worlds or states occupies central stage in probability theory, semantics and epistemology, where it serves as the fundamental unit both of information and meaning. But this fact should not blind us to the existence of prospects with a different structure. In the paper I examine the use of random variables—in particular, proposition-valued random variables— in these fields and argue that we need a general account of rational attitude formation with respect (...)
  37. The Kinematics of Belief and Desire.Richard Bradley - 2007 - Synthese 156 (3):513-535.
    Richard Jeffrey regarded the version of Bayesian decision theory he floated in ‘The Logic of Decision’ and the idea of a probability kinematics—a generalisation of Bayesian conditioning to contexts in which the evidence is ‘uncertain’—as his two most important contributions to philosophy. This paper aims to connect them by developing kinematical models for the study of preference change and practical deliberation. Preference change is treated in a manner analogous to Jeffrey’s handling of belief change: not as mechanical outputs of combinations (...)
  38. A Unified Bayesian Decision Theory.Richard Bradley - 2007 - Theory and Decision 63 (3):233-263,.
    This paper provides new foundations for Bayesian Decision Theory based on a representation theorem for preferences defined on a set of prospects containing both factual and conditional possibilities. This use of a rich set of prospects not only provides a framework within which the main theoretical claims of Savage, Ramsey, Jeffrey and others can be stated and compared, but also allows for the postulation of an extended Bayesian model of rational belief and desire from which they can be derived as (...)
  39. Radical Probabilism and Bayesian Conditioning.Richard Bradley - 2005 - 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 (...)
  40. An Accuracy‐Dominance Argument for Conditionalization.R. A. Briggs & Richard Pettigrew - forthcoming - Noûs.
  41. Distorted Reflection.Rachael Briggs - 2009 - Philosophical Review 118 (1):59-85.
    Diachronic Dutch book arguments seem to support both conditionalization and Bas van Fraassen's Reflection principle. But the Reflection principle is vulnerable to numerous counterexamples. This essay addresses two questions: first, under what circumstances should an agent obey Reflection, and second, should the counterexamples to Reflection make us doubt the Dutch book for conditionalization? In response to the first question, this essay formulates a new "Qualified Reflection" principle, which states that an agent should obey Reflection only if he or she is (...)
  42. Conditionalization and Not Knowing That One Knows.Aaron Bronfman - 2014 - Erkenntnis 79 (4):871-892.
    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. (...)
  43. Conditionalization and Expected Utility.Peter M. Brown - 1976 - Philosophy of Science 43 (3):415-419.
  44. Von Neumann's Projection Postulate as a Probability Conditionalization Rule in Quantum Mechanics.Jeffrey Bub - 1977 - Journal of Philosophical Logic 6 (1):381 - 390.
  45. Learning Not to Be Naïve: A Comment on the Exchange Between Perrine/Wykstra and Draper.Lara Buchak - 2014 - 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 (...)
  46. Self‐Locating Evidence and the Metaphysics of Time.David Builes - forthcoming - Philosophy and Phenomenological Research.
    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 (...)
  47. Theoretical Omniscience: Old Evidence or New Theory.André C. R. Martins - unknown
    I will show that, in the Problem of Old Evidence, unless a rational agent has a property I will call theoretical omniscience (a stronger version of logical omniscience), a problem with non-commutativity of the learning theories follows. Therefore, scientists, when trying to behave as close to rationality as possible, should behave in a way close to the counterfactual strategy. The concept of theoretical omniscience will be applied to the problem of Jeffrey conditionalization, as an example, and we will see that (...)
  48. Preference Change.Anaïs Cadilhac, Nicholas Asher, Alex Lascarides & Farah Benamara - 2015 - Journal of Logic, Language and Information 24 (3):267-288.
    Most models of rational action assume that all possible states and actions are pre-defined and that preferences change only when beliefs do. But several decision and game problems lack these features, calling for a dynamic model of preferences: preferences can change when unforeseen possibilities come to light or when there is no specifiable or measurable change in belief. We propose a formally precise dynamic model of preferences that extends an existing static model. Our axioms for updating preferences preserve consistency while (...)
  49. Don’T Stop Believing.Jennifer Rose Carr - 2015 - Canadian Journal of Philosophy 45 (5):744-766.
    It’s been argued that there are no diachronic norms of epistemic rationality. These arguments come partly in response to certain kinds of counterexamples to Conditionalization, but are mainly motivated by a form of internalism that appears to be in tension with any sort of diachronic coherence requirements. I argue that there are, in fact, fundamentally diachronic norms of rationality. And this is to reject at least a strong version of internalism. But I suggest a replacement for Conditionalization that salvages internalist (...)
  50. Higher-Order Beliefs and the Undermining Problem for Bayesianism.Lisa Cassell - forthcoming - Acta Analytica:1-17.
    Jonathan Weisberg has argued that Bayesianism’s rigid updating rules make Bayesian updating incompatible with undermining defeat. In this paper, I argue that when we attend to the higher-order beliefs we must ascribe to agents in the kinds of cases Weisberg considers, the problem he raises disappears. Once we acknowledge the importance of higher-order beliefs to the undermining story, we are led to a different understanding of how these cases arise. And on this different understanding of things, the rigid nature of (...)
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