The Development of Subjective Bayesianism

Joyce, James M.

doi:10.1016/b978-0-444-52936-7.50012-4

Cited by 35 publications

(20 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Though POI has a checkered past [Joyce, 2009], its defenders continue to see it as the only hope for a Bayesianism that does not degenerate into a systematic logic of unsupported personal superstition. When asked to justify POI , its defenders typically invoke the maximum entropy (MaxEnt) principle [Jaynes, 2003].…”

Section: The Need For Imprecise Credencesmentioning

confidence: 99%

A Defense of Imprecise Credences in Inference and Decision Making1

Joyce

2010

Philosophical Perspectives

Self Cite

210

118

View full text Add to dashboard Cite

Some Bayesians have suggested that beliefs based on ambiguous or incomplete evidence are best represented by families of probability functions. I spend the first half of this essay outlining one version of this imprecise model of belief , and spend the second half defending the model against recent objections, raised by Roger White and others, which concern the phenomenon of probabilistic dilation. Dilation occurs when learning some definite fact forces a person's beliefs about an event to shift from a sharp, point-valued subjective probability to an imprecise spread of probabilities. Some commentators find dilation disturbing, both from an epistemic and a decision-theoretic perspective, and place the blame on the use of sets of probabilities to model opinion. These reactions are based on an overly narrow conception of imprecise belief states which assumes that we know everything there is to know about a person's doxastic attitudes once we have identified the spreads of values for her imprecise credences. Once we recognize that the imprecise model has the resources to characterize a much richer family of doxastic attitudes than this, we will see that White's charges of epistemological and decision theoretic incoherence are unfounded. The Basic Ideas of Bayesian EpistemologySpeaking broadly, the Bayesian approach to epistemology is based on four interconnected ideas:1. Belief is not all-or-nothing. Opinions come in varying gradations of strength which can range from full certainty of truth, through equal confidence in truth and falsehood, to complete certainty of falsehood. 2. Gradational beliefs are governed by the laws of probability, which codify minimum standards of inductive consistency (or "coherence") to which rational opinions must conform.

show abstract

Section: The Need For Imprecise Credencesmentioning

confidence: 99%

A Defense of Imprecise Credences in Inference and Decision Making1

Joyce

2010

Philosophical Perspectives

Self Cite

210

118

View full text Add to dashboard Cite

show abstract

“…This has caused some concerns as to whether probability kinematics is a rational way to update beliefs (Döring, 1999;Lange, 2000;Kelly, 2008). I agree with Wagner (2002) and Joyce (2010) that these concerns are misguided. As Joyce (2010) points out, probability kinematics is non-commutative exactly when it should be; namely, when belief revision destroys information obtained in previous updates.…”

mentioning

confidence: 88%

“…But what does it mean to get the same uncertain evidence? An obvious starting point for explicating uncertain evidence are hard Jeffrey shifts (Joyce, 2010). A hard Jeffrey shift sets values for P n regardless of the prior probability P n−1 , and so may destroy any information about the partition that was encoded in the prior.…”

mentioning

confidence: 99%

Merging of Opinions and Probability Kinematics

Huttegger

2015

The Review of Symbolic Logic

View full text Add to dashboard Cite

We explore the question of whether sustained rational disagreement is possible from a broadly Bayesian perspective. The setting is one where agents update on the same information, with special consideration being given to the case of uncertain information. The classical merging of opinions theorem of Blackwell and Dubins shows when updated beliefs come and stay closer for Bayesian conditioning. We extend this result to a type of Jeffrey conditioning where agents update on evidence that is uncertain but solid (hard Jeffrey shifts). However, merging of beliefs does not generally hold for Jeffrey conditioning on evidence that is fluid (soft Jeffrey shifts, Field shifts). Several theorems on the asymptotic behavior of subjective probabilities are proven. Taken together they show that while a consensus nearly always emerges in important special cases, sustained rational disagreement can be expected in many other situations.

show abstract

“…This stands in stark contrast to how convergence-to-the-truth theorems are usually viewed. As for instance Joyce (2010) notes for a setting similar to the one discussed by Belot, convergence to the truth is not too surprising "because the data is so incredibly informative in the limit that the subject's prior beliefs are irrelevant to her final view as a matter of logic" (Joyce 2010, p. 446). At the limit we would know the truth value of any proposition about observations-on judgement day all observations will have been made.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Convergence to the Truth and the Metaphysics of Possible Worlds

Huttegger¹

2015

Philos. of Sci.

View full text Add to dashboard Cite

Belot (2013) argues that Bayesians are epistemologically flawed because they believe with probability one that they will learn the truth about observational propositions in the limit. While Belot's considerations suggest that this result should be interpreted with some care, the concerns he raises can largely be defused by putting convergence to the truth in the context of learning from an arbitrarily large but finite number of observations.

show abstract

The Development of Subjective Bayesianism

Cited by 35 publications

References 54 publications

A Defense of Imprecise Credences in Inference and Decision Making1

A Defense of Imprecise Credences in Inference and Decision Making1

Merging of Opinions and Probability Kinematics

Bayesian Convergence to the Truth and the Metaphysics of Possible Worlds

Contact Info

Product

Resources

About