Results for 'Bayesian decision theory'

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  1. (1 other version)Bayesian Decision Theory and Stochastic Independence.Philippe Mongin - 2017 - TARK 2017.
    Stochastic independence has a complex status in probability theory. It is not part of the definition of a probability measure, but it is nonetheless an essential property for the mathematical development of this theory. Bayesian decision theorists such as Savage can be criticized for being silent about stochastic independence. From their current preference axioms, they can derive no more than the definitional properties of a probability measure. In a new framework of twofold uncertainty, we introduce preference (...)
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  2. Decision theory, intelligent planning and counterfactuals.Michael John Shaffer - 2008 - Minds and Machines 19 (1):61-92.
    The ontology of decision theory has been subject to considerable debate in the past, and discussion of just how we ought to view decision problems has revealed more than one interesting problem, as well as suggested some novel modifications of classical decision theory. In this paper it will be argued that Bayesian, or evidential, decision-theoretic characterizations of decision situations fail to adequately account for knowledge concerning the causal connections between acts, states, and (...)
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  3. Decision Theory: Yes! Truth Conditions: No!Nate Charlow - 2016 - In Nate Charlow & Matthew Chrisman (eds.), Deontic Modality. New York, NY: Oxford University Press.
    This essay makes the case for, in the phrase of Angelika Kratzer, packing the fruits of the study of rational decision-making into our semantics for deontic modals—specifically, for parametrizing the truth-condition of a deontic modal to things like decision problems and decision theories. Then it knocks it down. While the fundamental relation of the semantic theory must relate deontic modals to things like decision problems and theories, this semantic relation cannot be intelligibly understood as representing (...)
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  4. Non-Ideal Decision Theory.Sven Neth - 2023 - Dissertation, University of California, Berkeley
    My dissertation is about Bayesian rationality for non-ideal agents. I show how to derive subjective probabilities from preferences using much weaker rationality assumptions than other standard representation theorems. I argue that non-ideal agents might be uncertain about how they will update on new information and consider two consequences of this uncertainty: such agents should sometimes reject free information and make choices which, taken together, yield sure loss. The upshot is that Bayesian rationality for non-ideal agents makes very different (...)
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  5. A Simpler and More Realistic Subjective Decision Theory.Haim Gaifman & Yang Liu - 2018 - Synthese 195 (10):4205--4241.
    In his classic book “the Foundations of Statistics” Savage developed a formal system of rational decision making. The system is based on (i) a set of possible states of the world, (ii) a set of consequences, (iii) a set of acts, which are functions from states to consequences, and (iv) a preference relation over the acts, which represents the preferences of an idealized rational agent. The goal and the culmination of the enterprise is a representation theorem: Any preference relation (...)
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  6.  99
    Predictive Minds Can Be Humean Minds.Frederik T. Junker, Jelle Bruineberg & Thor Grünbaum - forthcoming - British Journal for the Philosophy of Science.
    The predictive processing literature contains at least two different versions of the framework with different theoretical resources at their disposal. One version appeals to so-called optimistic priors to explain agents’ motivation to act (call this optimistic predictive processing). A more recent version appeals to expected free energy minimization to explain how agents can decide between different action policies (call this preference predictive processing). The difference between the two versions has not been properly appreciated, and they are not sufficiently separated in (...)
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  7. Incomplete Preference and Indeterminate Comparative Probabilities.Yang Liu - 2022 - British Journal for the Philosophy of Science 73 (3):795-810.
    The notion of comparative probability defined in Bayesian subjectivist theory stems from an intuitive idea that, for a given pair of events, one event may be considered “more probable” than the other. Yet it is conceivable that there are cases where it is indeterminate as to which event is more probable, due to, e.g., lack of robust statistical information. We take that these cases involve indeterminate comparative probabilities. This paper provides a Savage-style decision-theoretic foundation for indeterminate comparative (...)
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    A comparison of imprecise Bayesianism and Dempster–Shafer theory for automated decisions under ambiguity.Mantas Radzvilas, William Peden, Daniele Tortoli & Francesco De Pretis - forthcoming - Journal of Logic and Computation.
    Ambiguity occurs insofar as a reasoner lacks information about the relevant physical probabilities. There are objections to the application of standard Bayesian inductive logic and decision theory in contexts of significant ambiguity. A variety of alternative frameworks for reasoning under ambiguity have been proposed. Two of the most prominent are Imprecise Bayesianism and Dempster–Shafer theory. We compare these inductive logics with respect to the Ambiguity Dilemma, which is a problem that has been raised for Imprecise Bayesianism. (...)
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  9. (1 other version)A comprehensive theory of induction and abstraction, part I.Cael L. Hasse -
    I present a solution to the epistemological or characterisation problem of induction. In part I, Bayesian Confirmation Theory (BCT) is discussed as a good contender for such a solution but with a fundamental explanatory gap (along with other well discussed problems); useful assigned probabilities like priors require substantive degrees of belief about the world. I assert that one does not have such substantive information about the world. Consequently, an explanation is needed for how one can be licensed to (...)
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  10. Countable Additivity, Idealization, and Conceptual Realism.Yang Liu - 2020 - Economics and Philosophy 36 (1):127-147.
    This paper addresses the issue of finite versus countable additivity in Bayesian probability and decision theory -- in particular, Savage's theory of subjective expected utility and personal probability. I show that Savage's reason for not requiring countable additivity in his theory is inconclusive. The assessment leads to an analysis of various highly idealised assumptions commonly adopted in Bayesian theory, where I argue that a healthy dose of, what I call, conceptual realism is often (...)
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  11. Fragmentation and logical omniscience.Adam Elga & Agustín Rayo - 2021 - Noûs 56 (3):716-741.
    It would be good to have a Bayesian decision theory that assesses our decisions and thinking according to everyday standards of rationality — standards that do not require logical omniscience (Garber 1983, Hacking 1967). To that end we develop a “fragmented” decision theory in which a single state of mind is represented by a family of credence functions, each associated with a distinct choice condition (Lewis 1982, Stalnaker 1984). The theory imposes a local coherence (...)
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  12. Statistical Inference and the Plethora of Probability Paradigms: A Principled Pluralism.Mark L. Taper, Gordon Brittan Jr & Prasanta S. Bandyopadhyay - manuscript
    The major competing statistical paradigms share a common remarkable but unremarked thread: in many of their inferential applications, different probability interpretations are combined. How this plays out in different theories of inference depends on the type of question asked. We distinguish four question types: confirmation, evidence, decision, and prediction. We show that Bayesian confirmation theory mixes what are intuitively “subjective” and “objective” interpretations of probability, whereas the likelihood-based account of evidence melds three conceptions of what constitutes an (...)
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  13. A forward looking decision rule for imprecise credences.Rohan Sud - 2014 - Philosophical Studies 167 (1):119-139.
    Adam Elga (Philosophers’ Imprint, 10(5), 1–11, 2010) presents a diachronic puzzle to supporters of imprecise credences and argues that no acceptable decision rule for imprecise credences can deliver the intuitively correct result. Elga concludes that agents should not hold imprecise credences. In this paper, I argue for a two-part thesis. First, I show that Elga’s argument is incomplete: there is an acceptable decision rule that delivers the intuitive result. Next, I repair the argument by offering a more elaborate (...)
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  14. From unreliable sources: Bayesian critique and normative modelling of HUMINT inferences.Aviezer Tucker - 2023 - Journal of Policing, Intelligence and Counter Terrorism 18:1-17.
    This paper applies Bayesian theories to critically analyse and offer reforms of intelligence analysis, collection, analysis, and decision making on the basis of Human Intelligence, Signals Intelligence, and Communication Intelligence. The article criticises the reliabilities of existing intelligence methodologies to demonstrate the need for Bayesian reforms. The proposed epistemic reform program for intelligence analysis should generate more reliable inferences. It distinguishes the transmission of knowledge from its generation, and consists of Bayesian three stages modular model for (...)
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  15. The Sure-thing Principle and P2.Yang Liu - 2017 - Economics Letters 159:221-223.
    This paper offers a fine analysis of different versions of the well known sure-thing principle. We show that Savage's formal formulation of the principle, i.e., his second postulate (P2), is strictly stronger than what is intended originally.
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  16. Homeostatic epistemology : reliability, coherence and coordination in a Bayesian virtue epistemology.Susannah Kate Devitt - 2013 - Dissertation,
    How do agents with limited cognitive capacities flourish in informationally impoverished or unexpected circumstances? Aristotle argued that human flourishing emerged from knowing about the world and our place within it. If he is right, then the virtuous processes that produce knowledge, best explain flourishing. Influenced by Aristotle, virtue epistemology defends an analysis of knowledge where beliefs are evaluated for their truth and the intellectual virtue or competences relied on in their creation. However, human flourishing may emerge from how degrees of (...)
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  17. Fragmentation, metalinguistic ignorance, and logical omniscience.Jens Christian Bjerring & Weng Hong Tang - 2023 - Philosophical Studies 180 (7):2129-2151.
    To reconcile the standard possible worlds model of knowledge with the intuition that ordinary agents fall far short of logical omniscience, a Stalnakerian strategy appeals to two components. The first is the idea that mathematical and logical knowledge is at bottom metalinguistic knowledge. The second is the idea that non-ideal minds are often fragmented. In this paper, we investigate this Stalnakerian reconciliation strategy and argue, ultimately, that it fails. We are not the first to complain about the Stalnakerian strategy. But (...)
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  18. Subjective Probabilities Need Not be Sharp.Jake Chandler - 2014 - Erkenntnis 79 (6):1273-1286.
    It is well known that classical, aka ‘sharp’, Bayesian decision theory, which models belief states as single probability functions, faces a number of serious difficulties with respect to its handling of agnosticism. These difficulties have led to the increasing popularity of so-called ‘imprecise’ models of decision-making, which represent belief states as sets of probability functions. In a recent paper, however, Adam Elga has argued in favour of a putative normative principle of sequential choice that he claims (...)
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  19. Why Be Random?Thomas Icard - 2021 - Mind 130 (517):111-139.
    When does it make sense to act randomly? A persuasive argument from Bayesian decision theory legitimizes randomization essentially only in tie-breaking situations. Rational behaviour in humans, non-human animals, and artificial agents, however, often seems indeterminate, even random. Moreover, rationales for randomized acts have been offered in a number of disciplines, including game theory, experimental design, and machine learning. A common way of accommodating some of these observations is by appeal to a decision-maker’s bounded computational resources. (...)
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  20. Philosophy as conceptual engineering: Inductive logic in Rudolf Carnap's scientific philosophy.Christopher F. French - 2015 - Dissertation, University of British Columbia
    My dissertation explores the ways in which Rudolf Carnap sought to make philosophy scientific by further developing recent interpretive efforts to explain Carnap’s mature philosophical work as a form of engineering. It does this by looking in detail at his philosophical practice in his most sustained mature project, his work on pure and applied inductive logic. I, first, specify the sort of engineering Carnap is engaged in as involving an engineering design problem and then draw out the complications of design (...)
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  21. Heart of DARCness.Yang Liu & Huw Price - 2019 - Australasian Journal of Philosophy 97 (1):136-150.
    There is a long-standing disagreement in the philosophy of probability and Bayesian decision theory about whether an agent can hold a meaningful credence about an upcoming action, while she deliberates about what to do. Can she believe that it is, say, 70% probable that she will do A, while she chooses whether to do A? No, say some philosophers, for Deliberation Crowds Out Prediction (DCOP), but others disagree. In this paper, we propose a valid core for DCOP, (...)
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  22. Distinction between Beliefs and Desires.Eleni Manolakaki - 2004 - Deucalion : A Journal for Philosophical Research and Critique 22 (1):21-41.
    The paper presents a criticism of the metaphor of Direction of Fit as a criterion for beliefs/desires. It sketches an alternative account of belief/desire distinction based on Bayesian Decision Theory.
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  23. BMF CP44: Information priorities for investment decision-making and fear during the crisis.A. I. S. D. L. Team - 2023 - Sm3D Portal.
    The current study is conducted to examine how investors’ information priorities for investment decision-making influence their fear during the crisis.
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  24. A dual approach to Bayesian inference and adaptive control.Leigh Tesfatsion - 1982 - Theory and Decision 14 (2):177-194.
    Probability updating via Bayes' rule often entails extensive informational and computational requirements. In consequence, relatively few practical applications of Bayesian adaptive control techniques have been attempted. This paper discusses an alternative approach to adaptive control, Bayesian in spirit, which shifts attention from the updating of probability distributions via transitional probability assessments to the direct updating of the criterion function, itself, via transitional utility assessments. Results are illustrated in terms of an adaptive reinvestment two-armed bandit problem.
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  25. Newcomb's problem.John Collins - unknown
    Newcomb’s problem is a decision puzzle whose difficulty and interest stem from the fact that the possible outcomes are probabilistically dependent on, yet causally independent of, the agent’s options. The problem is named for its inventor, the physicist William Newcomb, but first appeared in print in a 1969 paper by Robert Nozick [12]. Closely related to, though less well-known than, the Prisoners’ Dilemma, it has been the subject of intense debate in the philosophical literature. After three decades, the issues (...)
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  26. How did that individual make that perceptual decision?David A. Booth - 2018 - Behavioral and Brain Sciences 41:E226.
    Suboptimality of decision making needs no explanation. High level accounts of suboptimality in diverse tasks cannot add up to a mechanistic theory of perceptual decision making. Mental processes operate on the contents of information brought by the experimenter and the participant to the task, not on the amount of information in the stimuli without regard to physical and social context.
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  27. Evidential Probabilities and Credences.Anna-Maria Asunta Eder - 2023 - British Journal for the Philosophy of Science 74 (1):1 -23.
    Enjoying great popularity in decision theory, epistemology, and philosophy of science, Bayesianism as understood here is fundamentally concerned with epistemically ideal rationality. It assumes a tight connection between evidential probability and ideally rational credence, and usually interprets evidential probability in terms of such credence. Timothy Williamson challenges Bayesianism by arguing that evidential probabilities cannot be adequately interpreted as the credences of an ideal agent. From this and his assumption that evidential probabilities cannot be interpreted as the actual credences (...)
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  28. The Dr. Psycho Paradox and Newcomb’s Problem.Michael Clark & Nicholas Shackel - 2006 - Erkenntnis 64 (1):85 - 100.
    Nicholas Rescher claims that rational decision theory “may leave us in the lurch”, because there are two apparently acceptable ways of applying “the standard machinery of expected-value analysis” to his Dr. Psycho paradox which recommend contradictory actions. He detects a similar contradiction in Newcomb’s problem. We consider his claims from the point of view of both Bayesian decision theory and causal decision theory. In Dr. Psycho and in Newcomb’s Problem, Rescher has used premisses (...)
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  29. Ramsey and Joyce on Deliberation and Prediction.Yang Liu & Huw Price - 2020 - Synthese 197:4365-4386.
    Can an agent deliberating about an action A hold a meaningful credence that she will do A? 'No', say some authors, for 'Deliberation Crowds Out Prediction' (DCOP). Others disagree, but we argue here that such disagreements are often terminological. We explain why DCOP holds in a Ramseyian operationalist model of credence, but show that it is trivial to extend this model so that DCOP fails. We then discuss a model due to Joyce, and show that Joyce's rejection of DCOP rests (...)
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  30. The Enigma Of Probability.Nick Ergodos - 2014 - Journal of Cognition and Neuroethics 2 (1):37-71.
    Using “brute reason” I will show why there can be only one valid interpretation of probability. The valid interpretation turns out to be a further refinement of Popper’s Propensity interpretation of probability. Via some famous probability puzzles and new thought experiments I will show how all other interpretations of probability fail, in particular the Bayesian interpretations, while these puzzles do not present any difficulties for the interpretation proposed here. In addition, the new interpretation casts doubt on some concepts often (...)
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  31. Low attention impairs optimal incorporation of prior knowledge in perceptual decisions.Jorge Morales, Guillermo Solovey, Brian Maniscalco, Dobromir Rahnev, Floris P. de Lange & Hakwan Lau - 2015 - Attention, Perception, and Psychophysics 77 (6):2021-2036.
    When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the (...)
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  32. Reliable color misrepresentation and color vision (in print), Special Issue: Brogaard, B. and French, R. (Eds).Dimitria Gatzia - forthcoming - Synthese.
    Tracking theories of mental representation posit a privileged relation between color representations and the color properties of objects. Tracking theories of mental representation have been used to motivate color realism as they posit that the function of color vision is to represent the colors of objects. It has been argued that tracking theories have a major flaw, namely they cannot account for reliable misrepresentation. It has further been suggested that reliable color misrepresentation is a live possibility. In this chapter, I (...)
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  33. A conditional expected utility model for myopic decision makers.Leigh Tesfatsion - 1980 - Theory and Decision 12 (2):185-206.
    An expected utility model of individual choice is formulated which allows the decision maker to specify his available actions in the form of controls (partial contingency plans) and to simultaneously choose goals and controls in end-mean pairs. It is shown that the Savage expected utility model, the Marschak- Radner team model, the Bayesian statistical decision model, and the standard optimal control model can be viewed as special cases of this goal-control expected utility model.
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  34. The Logical Consistency of Simultaneous Agnostic Hypothesis Tests.Julio Michael Stern - 2016 - Entropy 8 (256):1-22.
    Simultaneous hypothesis tests can fail to provide results that meet logical requirements. For example, if A and B are two statements such that A implies B, there exist tests that, based on the same data, reject B but not A. Such outcomes are generally inconvenient to statisticians (who want to communicate the results to practitioners in a simple fashion) and non-statisticians (confused by conflicting pieces of information). Based on this inconvenience, one might want to use tests that satisfy logical requirements. (...)
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  35. Modality, expected utility, and hypothesis testing.WooJin Chung & Salvador Mascarenhas - 2023 - Synthese 202 (1):1-40.
    We introduce an expected-value theory of linguistic modality that makes reference to expected utility and a likelihood-based confirmation measure for deontics and epistemics, respectively. The account is a probabilistic semantics for deontics and epistemics, yet it proposes that deontics and epistemics share a common core modal semantics, as in traditional possible-worlds analysis of modality. We argue that this account is not only theoretically advantageous, but also has far-reaching empirical consequences. In particular, we predict modal versions of reasoning fallacies from (...)
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  36. Building Compressed Causal Models of the World.David Kinney & Tania Lombrozo - forthcoming - Cognitive Psychology.
    A given causal system can be represented in a variety of ways. How do agents determine which variables to include in their causal representations, and at what level of granularity? Using techniques from Bayesian networks, information theory, and decision theory, we develop a formal theory according to which causal representations reflect a trade-off between compression and informativeness, where the optimal trade-off depends on the decision-theoretic value of information for a given agent in a given (...)
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  37. Cognitive Constructivism, Eigen-Solutions, and Sharp Statistical Hypotheses.Julio Michael Stern - 2007 - Cybernetics and Human Knowing 14 (1):9-36.
    In this paper epistemological, ontological and sociological questions concerning the statistical significance of sharp hypotheses in scientific research are investigated within the framework provided by Cognitive Constructivism and the FBST (Full Bayesian Significance Test). The constructivist framework is contrasted with the traditional epistemological settings for orthodox Bayesian and frequentist statistics provided by Decision Theory and Falsificationism.
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  38. Mechanizmy predykcyjne i ich normatywność [Predictive mechanisms and their normativity].Michał Piekarski - 2020 - Warszawa, Polska: Liberi Libri.
    The aim of this study is to justify the belief that there are biological normative mechanisms that fulfill non-trivial causal roles in the explanations (as formulated by researchers) of actions and behaviors present in specific systems. One example of such mechanisms is the predictive mechanisms described and explained by predictive processing (hereinafter PP), which (1) guide actions and (2) shape causal transitions between states that have specific content and fulfillment conditions (e.g. mental states). Therefore, I am guided by a specific (...)
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  39. Evidential Decision Theory and the Ostrich.Yoaav Isaacs & Ben Levinstein - 2024 - Philosophers' Imprint 24 (1).
    Evidential Decision Theory is flawed, but its flaws are not fully understood. David Lewis (1981) famously charged that EDT recommends an irrational policy of managing the news and “commends the ostrich as rational”. Lewis was right, but the case he appealed to—Newcomb’s Problem—does not demonstrate his conclusion. Indeed, decision theories other than EDT, such as Committal Decision Theory and Functional Decision Theory, agree with EDT's verdicts in Newcomb’s Problem, but their flaws, whatever they (...)
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  40. Causal Decision Theory and Decision Instability.Brad Armendt - 2019 - Journal of Philosophy 116 (5):263-277.
    The problem of the man who met death in Damascus appeared in the infancy of the theory of rational choice known as causal decision theory. A straightforward, unadorned version of causal decision theory is presented here and applied, along with Brian Skyrms’ deliberation dynamics, to Death in Damascus and similar problems. Decision instability is a fascinating topic, but not a source of difficulty for causal decision theory. Andy Egan’s purported counterexample to causal (...)
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  41. Causal decision theory, context, and determinism.Calum McNamara - 2024 - Philosophy and Phenomenological Research 109 (1):226-260.
    The classic formulation of causal decision theory (CDT) appeals to counterfactuals. It says that you should aim to choose an option that would have a good outcome, were you to choose it. However, this version of CDT faces trouble if the laws of nature are deterministic. After all, the standard theory of counterfactuals says that, if the laws are deterministic, then if anything—including the choice you make—were different in the present, either the laws would be violated or (...)
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  42. Counterfactual Decision Theory.Brian Hedden - 2023 - Mind 132 (527):730-761.
    I defend counterfactual decision theory, which says that you should evaluate an action in terms of which outcomes would likely obtain were you to perform it. Counterfactual decision theory has traditionally been subsumed under causal decision theory as a particular formulation of the latter. This is a mistake. Counterfactual decision theory is importantly different from, and superior to, causal decision theory, properly so called. Causation and counterfactuals come apart in three (...)
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  43. Tournament decision theory.Abelard Podgorski - 2020 - Noûs 56 (1):176-203.
    The dispute in philosophical decision theory between causalists and evidentialists remains unsettled. Many are attracted to the causal view’s endorsement of a species of dominance reasoning, and to the intuitive verdicts it gets on a range of cases with the structure of the infamous Newcomb’s Problem. But it also faces a rising wave of purported counterexamples and theoretical challenges. In this paper I will describe a novel decision theory which saves what is appealing about the causal (...)
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  44. Decision theory for agents with incomplete preferences.Adam Bales, Daniel Cohen & Toby Handfield - 2014 - Australasian Journal of Philosophy 92 (3):453-70.
    Orthodox decision theory gives no advice to agents who hold two goods to be incommensurate in value because such agents will have incomplete preferences. According to standard treatments, rationality requires complete preferences, so such agents are irrational. Experience shows, however, that incomplete preferences are ubiquitous in ordinary life. In this paper, we aim to do two things: (1) show that there is a good case for revising decision theory so as to allow it to apply non-vacuously (...)
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  45. Decision Theory.Lara Buchak - 2016 - In Alan Hájek & Christopher Hitchcock (eds.), The Oxford Handbook of Probability and Philosophy. Oxford: Oxford University Press.
    Decision theory has at its core a set of mathematical theorems that connect rational preferences to functions with certain structural properties. The components of these theorems, as well as their bearing on questions surrounding rationality, can be interpreted in a variety of ways. Philosophy’s current interest in decision theory represents a convergence of two very different lines of thought, one concerned with the question of how one ought to act, and the other concerned with the question (...)
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  46. Decision theory and de minimis risk.Martin Smith - 2024 - Erkenntnis 89 (6):2169-2192.
    A de minimis risk is defined as a risk that is so small that it may be legitimately ignored when making a decision. While ignoring small risks is common in our day-to-day decision making, attempts to introduce the notion of a de minimis risk into the framework of decision theory have run up against a series of well-known difficulties. In this paper, I will develop an enriched decision theoretic framework that is capable of overcoming two (...)
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  47. Causal Decision Theory: A Counterexample.Arif Ahmed - 2013 - Philosophical Review 122 (2):289-306.
    The essay presents a novel counterexample to Causal Decision Theory (CDT). Its interest is that it generates a case in which CDT violates the very principles that motivated it in the first place. The essay argues that the objection applies to all extant formulations of CDT and that the only way out for that theory is a modification of it that entails incompatibilism. The essay invites the reader to find this consequence of CDT a reason to reject (...)
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  48. Counterfactual Decision Theory Is Causal Decision Theory.J. Dmitri Gallow - 2024 - Pacific Philosophical Quarterly 105 (1):115-156.
    The role of causation and counterfactuals in causal decision theory is vexed and disputed. Recently, Brian Hedden (2023) argues that we should abandon causal decision theory in favour of an alternative: counterfactual decision theory. I argue that, pace Hedden, counterfactual decision theory is not a competitor to, but rather a version of, causal decision theory – the most popular version by far. I provide textual evidence that the founding fathers of (...)
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  49. Vagueness and Imprecise Credence.Anna Mahtani - 2019 - In Richard Dietz (ed.), Vagueness and Rationality in Language Use and Cognition. Springer Verlag. pp. 7-30.
    In this paper I investigate an alternative to imprecise probabilism. Imprecise probabilism is a popular revision of orthodox Bayesianism: while the orthodox Bayesian claims that a rational agent’s belief-state can be represented by a single credence function, the imprecise probabilist claims instead that a rational agent’s belief-state can be represented by a set of such functions. The alternative that I put forward in this paper is to claim that the expression ‘credence’ is vague, and then apply the theory (...)
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  50. Wishing, Decision Theory, and Two-Dimensional Content.Kyle Blumberg - 2023 - Journal of Philosophy 120 (2):61-93.
    This paper is about two requirements on wish reports whose interaction motivates a novel semantics for these ascriptions. The first requirement concerns the ambiguities that arise when determiner phrases, such as definite descriptions, interact with ‘wish’. More specifically, several theorists have recently argued that attitude ascriptions featuring counterfactual attitude verbs license interpretations on which the determiner phrase is interpreted relative to the subject’s beliefs. The second requirement involves the fact that desire reports in general require decision-theoretic notions for their (...)
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