69,428 research outputs found

    Aggregating causal judgements

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    Decision making typically requires judgements about causal relations. We need to know both the causal effects of our actions and the causal relevance of various environmental factors. Judgements about the nature and strength of causal relations often differ, even among experts. How to handle such diversity is the topic of this paper. First we consider the possibility of aggregating causal judgements via the aggregation of probabilistic ones. The broadly negative outcome of this investigation leads us to look at aggregating causal judgements independently of probabilistic ones. We do so by transcribing causal claims into the judgement aggregation framework and applying some recent results in this field. Finally we look at the implications for probability aggregation when it is constrained by prior aggregation of causal judgements.mathematical economics;

    Explanatory Judgment, Probability, and Abductive Inference

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    Abductive reasoning assigns special status to the explanatory power of a hypothesis. But how do people make explanatory judgments? Our study clarifies this issue by asking: (i) How does the explanatory power of a hypothesis cohere with other cognitive factors? (ii) How does probabilistic information affect explanatory judgments? In order to answer these questions, we conducted an experiment with 671 participants. Their task was to make judgments about a potentially explanatory hypothesis and its cognitive virtues. In the responses, we isolated three constructs: Explanatory Value, Rational Acceptability, and Entailment. Explanatory judgments strongly cohered with judgments of causal relevance and with a sense of understanding. Furthermore, we found that Explanatory Value was sensitive to manipulations of statistical relevance relations between hypothesis and evidence, but not to explicit information about the prior probability of the hypothesis. These results indicate that probabilistic information about statistical relevance is a strong determinant of Explanatory Value. More generally, our study suggests that abductive and probabilistic reasoning are two distinct modes of inference

    Relevance and Conditionals: A Synopsis of Open Pragmatic and Semantic Issues

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    Recently several papers have reported relevance effects on the cognitive assessments of indicative conditionals, which pose an explanatory challenge to the Suppositional Theory of conditionals advanced by David Over, which is influential in the psychology of reasoning. Some of these results concern the “Equation” (P(if A, then C) = P(C|A)), others the de Finetti truth table, and yet others the uncertain and-to-inference task. The purpose of this chapter is to take a Birdseye view on the debate and investigate some of the open theoretical issues posed by the empirical results. Central among these is whether to count these effects as belonging to pragmatics or semantics

    The accessibility dimension for structured document retrieval

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    Structured document retrieval aims at retrieving the document components that best satisfy a query, instead of merely retrieving pre-defined document units. This paper reports on an investigation of a tf-idf-acc approach, where tf and idf are the classical term frequency and inverse document frequency, and acc, a new parameter called accessibility, that captures the structure of documents. The tf-idf-acc approach is defined using a probabilistic relational algebra. To investigate the retrieval quality and estimate the acc values, we developed a method that automatically constructs diverse test collections of structured documents from a standard test collection, with which experiments were carried out. The analysis of the experiments provides estimates of the acc values

    Probabilistic learning for selective dissemination of information

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    New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of information retrieval. The model is based on the concept of 'uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile

    Causal processes and interactions: What are they and what are they good for?

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    Concerning any object of philosophical analysis, we can ask several questions, including the two posed in the title of this paper. Despite difficulties in formulating a precise criterion to distinguish causal processes from pseudoprocesses, and causal interactions from mere spatiotemporal intersections, I argue that Salmon answered the first of these questions with extraordinary clarity. The second question, by contrast, has received very little attention. I will present two problems: in the first, it seems that Salmon has provided exactly the conceptual resources needed to solve the problem; in the second, it is difficult to see how causal processes and interactions may be used to shed any light. In general, the way to carry Salmon's program forward will be to demonstrate that these resources can be made to do real philosophical work
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