490 research outputs found
Epistemic Teleology: Synchronic and Diachronic
According to a widely held view of the matter, whenever we assess beliefs as ārationalā or ājustifiedā, we are making normative judgements about those beliefs. In this discussion, I shall simply assume, for the sake of argument, that this view is correct. My goal here is to explore a particular approach to understanding the basic principles that explain which of these normative judgements are true. Specifically, this approach is based on the assumption that all such normative principles are grounded in facts about values, and the normative principles that apply to beliefs in particular are grounded in facts about alethic valueāāa kind of value that is exemplified by believing what is true and not believing what is false. In this chapter, I shall explain what I regard as the best way of interpreting this approach. In doing so, I shall also show how this interpretation can solve some problems that have recently been raised for approaches of this kind by Selim Berker, Jennifer Carr, Michael Caie, and Hilary Greaves
Imprecise Bayesianism and Global Belief Inertia
Traditional Bayesianism requires that an agentās degrees of belief be represented by a real-valued, probabilistic credence function. However, in many cases it seems that our evidence is not rich enough to warrant such precision. In light of this, some have proposed that we instead represent an agentās degrees of belief as a set of credence functions. This way, we can respect the evidence by requiring that the set, often called the agentās credal state, includes all credence functions that are in some sense compatible with the evidence. One known problem for this evidentially motivated imprecise view is that in certain cases, our imprecise credence in a particular proposition will remain the same no matter how much evidence we receive. In this article I argue that the problem is much more general than has been appreciated so far, and that itās difficult to avoid it without compromising the initial evidentialist motivation. _1_ Introduction _2_ Precision and Its Problems _3_ Imprecise Bayesianism and Respecting Ambiguous Evidence _4_ Local Belief Inertia _5_ From Local to Global Belief Inertia _6_ Responding to Global Belief Inertia _7_ Conclusio
The Relationship Between Belief and Credence
Sometimes epistemologists theorize about belief, a tripartite attitude on which one can believe, withhold belief, or disbelieve a proposition. In other cases, epistemologists theorize about credence, a fine-grained attitude that represents oneās subjective probability or confidence level toward a proposition. How do these two attitudes relate to each other? This article explores the relationship between belief and credence in two categories: descriptive and normative. It then explains the broader significance of the belief-credence connection and concludes with general lessons from the debate thus far
Scientiļ¬c uncertainty and decision making
It is important to have an adequate model of uncertainty, since decisions must be
made before the uncertainty can be resolved. For instance, ļ¬ood defenses must be
designed before we know the future distribution of ļ¬ood events. It is standardly
assumed that probability theory oļ¬ers the best model of uncertain information. I
think there are reasons to be sceptical of this claim.
I criticise some arguments for the claim that probability theory is the only
adequate model of uncertainty. In particular I critique Dutch book arguments,
representation theorems, and accuracy based arguments.
Then I put forward my preferred model: imprecise probabilities. These are sets
of probability measures. I oļ¬er several motivations for this model of uncertain
belief, and suggest a number of interpretations of the framework. I also defend
the model against some criticisms, including the so-called problem of dilation.
I apply this framework to decision problems in the abstract. I discuss some
decision rules from the literature including Leviās E-admissibility and the more
permissive rule favoured by Walley, among others. I then point towards some
applications to climate decisions. My conclusions are largely negative: decision
making under such severe uncertainty is inevitably diļ¬cult.
I ļ¬nish with a case study of scientiļ¬c uncertainty. Climate modellers attempt
to oļ¬er probabilistic forecasts of future climate change. There is reason to be
sceptical that the model probabilities oļ¬ered really do reļ¬ect the chances of future
climate change, at least at regional scales and long lead times. Indeed, scientiļ¬c
uncertainty is multi-dimensional, and diļ¬cult to quantify. I argue that probability
theory is not an adequate representation of the kinds of severe uncertainty that
arise in some areas in science. I claim that this requires that we look for a better
framework for modelling uncertaint
Belief Revision for Growing Awareness
The Bayesian maxim for rational learning could be described as conservative
change from one probabilistic belief or credence function to another in response to
newinformation. Roughly: āHold fixed any credences that are not directly affected
by the learning experience.ā This is precisely articulated for the case when we
learn that some proposition that we had previously entertained is indeed true (the
rule of conditionalisation). But can this conservative-change maxim be extended
to revising oneās credences in response to entertaining propositions or concepts
of which one was previously unaware? The economists Karni and VierĆø (2013,
2015) make a proposal in this spirit. Philosophers have adopted effectively the
same rule: revision in response to growing awareness should not affect the relative
probabilities of propositions in oneās āoldā epistemic state. The rule is compelling,
but only under the assumptions that its advocates introduce. It is not a general
requirement of rationality, or so we argue. We provide informal counterexamples.
And we show that, when awareness grows, the boundary between oneās āoldā and
ānewā epistemic commitments is blurred. Accordingly, there is no general notion
of conservative change in this setting
Coherent frequentism
By representing the range of fair betting odds according to a pair of
confidence set estimators, dual probability measures on parameter space called
frequentist posteriors secure the coherence of subjective inference without any
prior distribution. The closure of the set of expected losses corresponding to
the dual frequentist posteriors constrains decisions without arbitrarily
forcing optimization under all circumstances. This decision theory reduces to
those that maximize expected utility when the pair of frequentist posteriors is
induced by an exact or approximate confidence set estimator or when an
automatic reduction rule is applied to the pair. In such cases, the resulting
frequentist posterior is coherent in the sense that, as a probability
distribution of the parameter of interest, it satisfies the axioms of the
decision-theoretic and logic-theoretic systems typically cited in support of
the Bayesian posterior. Unlike the p-value, the confidence level of an interval
hypothesis derived from such a measure is suitable as an estimator of the
indicator of hypothesis truth since it converges in sample-space probability to
1 if the hypothesis is true or to 0 otherwise under general conditions.Comment: The confidence-measure theory of inference and decision is explicitly
extended to vector parameters of interest. The derivation of upper and lower
confidence levels from valid and nonconservative set estimators is formalize
A Probabilistic Modelling Approach for Rational Belief in Meta-Epistemic Contexts
This work is part of the larger project INTEGRITY. Integrity develops a conceptual frame integrating beliefs with individual (and consensual group) decision making and action based on belief awareness. Comments and criticisms are most welcome via email.
The text introduces the conceptual (internalism, externalism), quantitative (probabilism) and logical perspectives (logics for reasoning about probabilities by Fagin, Halpern, Megiddo and MEL by Banerjee, Dubois) for the framework
A Probabilistic Modelling Approach for Rational Belief in Meta-Epistemic Contexts
This work is part of the larger project INTEGRITY. Integrity develops a conceptual frame integrating beliefs
with individual (and consensual group) decision making and action based on belief awareness. Comments and
criticisms are most welcome via email.
Starting with a thorough discussion of the conceptual embedding in existing schools of thought and liter-
ature we develop a framework that aims to be empirically adequate yet scalable to epistemic states where an
agent might testify to uncertainly believe a propositional formula based on the acceptance that a propositional
formula is possible, called accepted truth. The familiarity of human agents with probability assignments make
probabilism particularly appealing as quantitative modelling framework for defeasible reasoning that aspires
empirical adequacy for gradual belief expressed as credence functions. We employ the inner measure induced
by the probability measure, going back to Halmos, interpreted as estimate for uncertainty. Doing so omits
generally requiring direct probability assignments testiļæ½ed as strength of belief and uncertainty by a human
agent. We provide a logical setting of the two concepts uncertain belief and accepted truth, completely relying
on the the formal frameworks of 'Reasoning about Probabilities' developed by Fagin, Halpern and Megiddo and
the 'Metaepistemic logic MEL' developed by Banerjee and Dubois. The purport of Probabilistic Uncertainty is
a framework allowing with a single quantitative concept (an inner measure induced by a probability measure)
expressing two epistemological concepts: possibilities as belief simpliciter called accepted truth, and the agents'
credence called uncertain belief for a criterion of evaluation, called rationality. The propositions accepted to be
possible form the meta-epistemic context(s) in which the agent can reason and testify uncertain belief or suspend
judgement
Narration in judiciary fact-finding : a probabilistic explication
Legal probabilism is the view that juridical fact-finding should be modeled using Bayesian methods. One of the alternatives to it is the narration view, according to which instead we should conceptualize the process in terms of competing narrations of what (allegedly) happened. The goal of this paper is to develop a reconciliatory account, on which the narration view is construed from the Bayesian perspective within the framework of formal Bayesian epistemology
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