8,509 research outputs found
Justification by an infinity of conditional probabilities
Today it is generally assumed that epistemic justification comes in degrees. The consequences, however, have not been adequately appreciated. In this paper we show that the assumption invalidates some venerable attacks on infinitism: once we accept that epistemic justification is gradual, an infinitist stance makes perfect sense. It is only without the assumption that infinitism runs into difficulties
The Implementation, Interpretation, and Justification of Likelihoods in Cosmology
I discuss the formal implementation, interpretation, and justification of likelihood attributions in cosmology. I show that likelihood arguments in cosmology suffer from significant conceptual and formal problems that undermine their applicability in this context
The Problem of Confirmation in the Everett Interpretation
I argue that the Oxford school Everett interpretation is internally
incoherent, because we cannot claim that in an Everettian universe the kinds of
reasoning we have used to arrive at our beliefs about quantum mechanics would
lead us to form true beliefs. I show that in an Everettian context, the
experimental evidence that we have available could not provide empirical
confirmation for quantum mechanics, and moreover that we would not even be able
to establish reference to the theoretical entities of quantum mechanics. I then
consider a range of existing Everettian approaches to the probability problem
and show that they do not succeed in overcoming this incoherence
An information theory for preferences
Recent literature in the last Maximum Entropy workshop introduced an analogy
between cumulative probability distributions and normalized utility functions.
Based on this analogy, a utility density function can de defined as the
derivative of a normalized utility function. A utility density function is
non-negative and integrates to unity. These two properties form the basis of a
correspondence between utility and probability. A natural application of this
analogy is a maximum entropy principle to assign maximum entropy utility
values. Maximum entropy utility interprets many of the common utility functions
based on the preference information needed for their assignment, and helps
assign utility values based on partial preference information. This paper
reviews maximum entropy utility and introduces further results that stem from
the duality between probability and utility
Gibbs conditioning extended, Boltzmann conditioning introduced
Conditional Equi-concentration of Types on I-projections (ICET) and Extended
Gibbs Conditioning Principle (EGCP) provide an extension of Conditioned Weak
Law of Large Numbers and of Gibbs Conditioning Principle to the case of
non-unique Relative Entropy Maximizing (REM) distribution (aka I-projection).
ICET and EGCP give a probabilistic justification to REM under rather general
conditions. mu-projection variants of the results are introduced. They provide
a probabilistic justification to Maximum Probability (MaxProb) method.
'REM/MaxEnt or MaxProb?' question is discussed, briefly. Jeffreys Conditioning
Principle is mentioned.Comment: Three major changes: 1) Definition of proper I-projection has been
changed. 2) An argument preceding Eq. (7) at the proof of ICET is now
correctly stated. 3) Abstract was rewritten. To appear at Proceedings of
MaxEnt 2004 worksho
Justification by Infinite Loops
In an earlier paper we have shown that a proposition can have a well-defined probability value, even if its justification consists of an infinite linear chain. In the present paper we demonstrate that the same holds if the justification takes the form of a closed loop. Moreover, in the limit that the size of the loop tends to infinity, the probability value of the justified proposition is always well-defined, whereas this is not always so for the infinite linear chain. This suggests that infinitism sits more comfortably with a coherentist view of justification than with an approach in which justification is portrayed as a linear process
Epistemic Akrasia and Epistemic Reasons
It seems that epistemically rational agents should avoid incoherent combinations of beliefs and should respond correctly to their epistemic reasons. However, some situations seem to indicate that such requirements cannot be simultaneously satisfied. In such contexts, assuming that there is no unsolvable dilemma of epistemic rationality, either (i) it could be rational that one’s higher-order attitudes do not align with one’s first-order attitudes or (ii) requirements such as responding correctly to epistemic reasons that agents have are not genuine rationality requirements. This result doesn’t square well with plausible theoretical assumptions concerning epistemic rationality. So, how do we solve this puzzle? In this paper, I will suggest that an agent can always reason from infallible higher-order reasons. This provides a partial solution to the above puzzle
There Is No Pure Empirical Reasoning
The justificatory force of empirical reasoning always depends upon the existence of some synthetic, a priori justification. The reasoner must begin with justified, substantive constraints on both the prior probability of the conclusion and certain conditional probabilities; otherwise, all possible degrees of belief in the conclusion are left open given the premises. Such constraints cannot in general be empirically justified, on pain of infinite regress. Nor does subjective Bayesianism offer a way out for the empiricist. Despite often-cited convergence theorems, subjective Bayesians cannot hold that any empirical hypothesis is ever objectively justified in the relevant sense. Rationalism is thus the only alternative to an implausible skepticism
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