102,766 research outputs found

    Belief Revision in the Context of Hume’s Treatise and Contemporary Psychology

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    This paper examines the emotional and social motivations of belief and belief correction. As beliefs motivate one’s actions, one must examine how one revises an erroneous or harmful belief and what methodology one can employ in order to best facilitate this revision, resulting in more conscientious action. This paper examines belief formation and revision in the context of David Hume’s 1739-1740 work A Treatise of Human Nature, with particular attention to not only Hume’s account of belief and belief revision, but also the interaction of passions, the mechanism of sympathy, reason, and probability judgments. It is hypothesized Hume’s theory of belief will be reflected in contemporary psychology and cognitive science, with individuals more likely to revise their beliefs based emotional and social factors and experiences proposed by Hume

    Belief Revision in the Context of Hume’s Treatise and Contemporary Psychology

    Get PDF
    This paper examines the emotional and social motivations of belief and belief correction. As beliefs motivate one’s actions, one must examine how one revises an erroneous or harmful belief and what methodology one can employ in order to best facilitate this revision, resulting in more conscientious action. This paper examines belief formation and revision in the context of David Hume’s 1739-1740 work A Treatise of Human Nature, with particular attention to not only Hume’s account of belief and belief revision, but also the interaction of passions, the mechanism of sympathy, reason, and probability judgments. It is hypothesized Hume’s theory of belief will be reflected in contemporary psychology and cognitive science, with individuals more likely to revise their beliefs based emotional and social factors and experiences proposed by Hume

    A Bayesian Framework for Modifications of Probabilistic Relational Data

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    The inherent uncertainty pervasive over the real world often forces business decisions to be made using uncertain data. The conventional relational model does not have the ability to handle uncertain data. In recent years, several approaches have been proposed in the literature for representing uncertain data by extending the relational model, primarily using probability theory. However, the aspect of database modification has been overlooked in these investigations. It is clear that any modification of existing probabilistic data, based on new information, amounts to the revision of one’s belief about real world objects. In this paper, we examine the aspect of belief revision and develop a generalized algorithm that can be used for modification of existing data in a probabilistic relational database

    A Unified Characterization of Belief Revision Rules

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    This paper characterizes several belief-revision rules in a unified framework: Bayesian revision upon learning some event, Jeffrey revision upon learning new probabilities of some events, Adams revision upon learning some new conditional probabilities, and `dual-Jeffrey' revision upon learning a new conditional probability function. Despite their differences, these revision rules can be characterized in terms of the same two axioms: responsiveness, which requires that revised beliefs incorporate what has been learnt, and conservativeness, which requires that beliefs on which the learnt input is `silent' do not change. So, the four revision rules apply the same principles, albeit to different learnt inputs. To illustrate that there is room for non-Bayesian belief revision in economic theory, we also sketch a simple decision-theoretic application
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