scholarly journals Practical reasoning and degrees of outright belief

Synthese ◽  
2021 ◽  
Author(s):  
Moritz Schulz
Keyword(s):  
Practical Reasoning ◽  
Higher Order ◽  
Epistemic Norm ◽  
Degrees Of Belief ◽  
Degree Of Belief ◽  
Oxford University ◽  
Ranking Theory ◽  
Low Degree ◽  
Alternative Proposal ◽  
Epistemic Standards

AbstractAccording to a suggestion by Williamson (Knowledge and its limits, Oxford University Press, 2000, p. 99), outright belief comes in degrees: one has a high/low degree of belief iff one is willing to rely on the content of one’s belief in high/low-stakes practical reasoning. This paper develops an epistemic norm for degrees of outright belief so construed. Starting from the assumption that outright belief aims at knowledge, it is argued that degrees of belief aim at various levels of strong knowledge, that is, knowledge which satisfies particularly high epistemic standards. This account is contrasted with and shown to be superior to an alternative proposal according to which higher degrees of outright belief aim at higher-order knowledge. In an “Appendix”, it is indicated that the logic of degrees of outright belief is closely linked to ranking theory.

INTERVAL METHODS IN KNOWLEDGE REPRESENTATION

1996 ◽  
Vol 04 (05) ◽  
pp. 467-490 ◽  
Author(s):  
Vladik Kreinovich
Keyword(s):  
Knowledge Representation ◽  
El Paso ◽  
Upper Bounds ◽  
Knowledge Elicitation ◽  
Interval Methods ◽  
Degrees Of Belief ◽  
Lower And Upper Bounds ◽  
University Of Texas ◽  
Degree Of Belief ◽  
Measurement Results

In this issues, we continue to publish abstracts and reviews of recents papers on interval methods in knowledge representation. In knowledge representation, intervals are used for two main purposes: • to describe durations of events; and • to describe uncertainty of measurement results and expert estimates of different quantities; often, we do not know the exact value of a quantity, but we know its lower and upper bounds (e.g., we may not know the exact value of someone's weight, but we may know that this weight is in between 140 and 160 pounds). An important case of this uncertainty occurs in knowledge elicitation, when we ask experts to numerically estimate their degrees of belief in their own statements; in this case, it is often difficult for an expert to estimate this degree of belief precisely, but an expert can often provide us with an interval of possible values. The reviews are collected by Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA, email [email protected]

scholarly journals Conceptos fenoménicos, conceptos psicológicos y la explicación de la conciencia

2009 ◽  
Vol 41 (121) ◽  
pp. 85-97
Author(s):  
Diana I. Pérez
Keyword(s):  
Higher Order ◽  
Oxford University ◽  
Oxford University Press

Peter Carruthers, Consciousness. Essays from a Higher-Order Perspective, Oxford University Press, Oxford, 2005, 247 pp.

Uncertainty, Certainty, and Beyond

2018 ◽  
Author(s):  
Rani Lill Anjum ◽  
Stephen Mumford
Keyword(s):  
Maximal Degree ◽  
Degrees Of Belief ◽  
Degree Of Belief ◽  
The World ◽  
New Evidence

The issue of probability enters into science because there can be inconclusive evidence, degrees of belief, and chancy phenomena in the world. This is relevant to Bayesian thinking, for example, which accepts that theories should be accepted only tentatively and considered more or less probable in the light of new evidence. Probability can be modelled in a simplified way, such as where a maximal degree of belief is assigned the value 1. A question remains of how well this reflects the reality of epistemic phenomena, which seems to allow cases where there is more than certainty, i.e. where you would still be certain of something even with less evidence than there is.

Interval-Valued Degrees of Belief: Applications of Interval Computations to Expert Systems and Intelligent Control

1997 ◽  
Vol 05 (03) ◽  
pp. 317-358 ◽  
Author(s):  
Hung T. Nguyen ◽  
Vladik Kreinovich ◽  
Qiang Zuo
Keyword(s):  
Expert Systems ◽  
Intelligent Control ◽  
Degrees Of Belief ◽  
Interval Computations ◽  
Degree Of Belief ◽  
Interval Valued

Usually, expert systems use numbers to describe the experts' degree of belief in their statements. In practice, however, it is difficult to assign an exact numerical value to the expert's degree of belief. At best, we can get an interval of possible values. This fact leads to the use of interval-valued degree of belief. When intervals are used to describe degrees of belief, then computations with intervals must be used to process them. In this paper, we describe applications of such interval computations to expert systems and to intelligent control.

Models, Idealizations and Objective Chance

2019 ◽  
pp. 311-326
Author(s):  
Jan Sprenger ◽  
Stephan Hartmann
Keyword(s):  
Bayesian Inference ◽  
Statistical Model ◽  
Statistical Models ◽  
Scientific Modeling ◽  
Degrees Of Belief ◽  
Standard Interpretation ◽  
Degree Of Belief ◽  
Interpretation Of Probability ◽  
Models In Science ◽  
Objective Chance

How does Bayesian inference handle the highly idealized nature of many (statistical) models in science? The standard interpretation of probability as degree of belief in the truth of a model does not seem to apply in such cases since all candidate models are most probably wrong. Similarly, it is not clear how chance-credence coordination works for the probabilities generated by a statistical model. We solve these problems by developing a suppositional account of degree of belief where probabilities in scientific modeling are decoupled from our actual (unconditional) degrees of belief. This explains the normative pull of chance-credence coordination in Bayesian inference, uncovers the essentially counterfactual nature of reasoning with Bayesian models, and squares well with our intuitive judgment that statistical models provide “objective” probabilities.

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