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Severe Testing as a Basic Concept in a NeymanPearson Philosophy of Induction
 BRITISH JOURNAL FOR THE PHILOSOPHY OF SCIENCE
, 2006
"... Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and longstanding problems of N–P tests s ..."
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Cited by 40 (16 self)
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Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and longstanding problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test’s (predata) error probabilities are to be used for (postdata) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities is to ensure that only statistical hypotheses that have passed severe or probative tests are inferred from the data. The severity criterion supplies a metastatistical principle for evaluating proposed statistical inferences, avoiding classic fallacies from tests that are overly sensitive, as well as those not sensitive enough to particular errors and discrepancies.
AgentOriented Integration of Distributed Mathematical Services
 Journal of Universal Computer Science
, 1999
"... Realworld applications of automated theorem proving require modern software environments that enable modularisation, networked interoperability, robustness, and scalability. These requirements are met by the AgentOriented Programming paradigm of Distributed Artificial Intelligence. We argue that ..."
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Cited by 20 (10 self)
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Realworld applications of automated theorem proving require modern software environments that enable modularisation, networked interoperability, robustness, and scalability. These requirements are met by the AgentOriented Programming paradigm of Distributed Artificial Intelligence. We argue that a reasonable framework for automated theorem proving in the large regards typical mathematical services as autonomous agents that provide internal functionality to the outside and that, in turn, are able to access a variety of existing external services. This article describes...
Information, relevance, and social decisionmaking: Some principles and results of decisiontheoretic semantics
 Logic, Language, and Computation
, 1999
"... Abstract. I propose to treat natural language semantics as a branch of pragmatics, identified in the way of C.S. Peirce, F.P. Ramsey, and R. Carnap as decisiontheory. The notion of relevance plays a key role. It is explicated traditionally, distinguished from a recent homophone, and applied in its ..."
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Cited by 20 (0 self)
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Abstract. I propose to treat natural language semantics as a branch of pragmatics, identified in the way of C.S. Peirce, F.P. Ramsey, and R. Carnap as decisiontheory. The notion of relevance plays a key role. It is explicated traditionally, distinguished from a recent homophone, and applied in its natural framework of issuebased communication. Empirical emphasis is on implicature and presupposition. Several theorems are stated and made use of. Items analyzed include ‘or’, ‘not’, ‘but’, ‘even’, and ‘also’. I conclude on parts of mind. This paper submits an approach to meaning, with a focus on broadly nontruthconditional aspects of natural language. Semantics is treated as a branch of pragmatics, identified as decisiontheory in the way of C.S. Peirce, F.P. Ramsey, and of Rudolf Carnap in his later work. A key theoretical notion, distinguishable from, but intelligibly related to, information is the positive or negative relevance of a proposition or sentence to another. It is explicated in the probabilistic way familiar from Carnap and traditional in the philosophies of science and rational action. This makes it a representation of local epistemic contextchange potential that is directional in a precisely specifiable sense and naturally related to utterers ’ instrumental intentions. Relevance so defined is proposed as an explicans for Oswald Ducrot’s insightful ‘valeur argumentative’. In view of possible confusion among some students of language, it is contrasted with a more recent and idiosyncratic pretender to the appellation, due to Dan Sperber and Deirdre Wilson. The latter proposal turns out, at best, to paraphrase H.P. Grice’s nondirectional concepts of ‘informativeness ’ and ‘perspicuity’. (More informative designations are suggested for it, and for the eponymous linguistic doctrine emanating from parts of CNRS Paris and of UC London.)
When should epidemiologic regressions use random coefficients
 Biometrics
"... SUMMARY. Regression models with random coefficients arise naturally in both frequentist and Bayesian approaches to estimation problems. They are becoming widely available in standard computer packages under the headings of generalized linear mixed models, hierarchical models, and multilevel models. ..."
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Cited by 17 (2 self)
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SUMMARY. Regression models with random coefficients arise naturally in both frequentist and Bayesian approaches to estimation problems. They are becoming widely available in standard computer packages under the headings of generalized linear mixed models, hierarchical models, and multilevel models. I here argue that such models offer a more scientifically defensible framework for epidemiologic analysis than the fixedeffects models now prevalent in epidemiology. The argument invokes an antiparsimony principle attributed to L. J. Savage, which is that models should be rich enough to reflect the complexity of the relations under study. It also invokes the countervailing principle that you cannot estimate anything if you try to estimate everything (often used to justify parsimony). Regression with random coefficients offers a rational compromise between these principles as well as an alternative to analyses based on standard variableselection algorithms and their attendant distortion of uncertainty assessments. These points are illustrated with an analysis of data on diet, nutrition, and breast cancer.
Solving TimeDependent Problems: A DecisionTheoretic Approach to Planning in Dynamic Environments
, 1991
"... Controlling a robot involves making decisions that modify its behavior. Making good decisions may require timeconsuming computation. Changes in the environment over time affect when this computation can be done (e.g., after obtaining the necessary information) , and when a result is useful (e.g., b ..."
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Cited by 16 (1 self)
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Controlling a robot involves making decisions that modify its behavior. Making good decisions may require timeconsuming computation. Changes in the environment over time affect when this computation can be done (e.g., after obtaining the necessary information) , and when a result is useful (e.g., before some event occurs). This sensitivity to when computation is performed and when decisions are made is what makes these problems "timedependent." A controller with more than one decision to make must trade off computation time, based on the expected effect on the system's behavior. We call the resulting metalevel scheduling problem a "deliberationscheduling" problem. We have
PAGODA: A Model for Autonomous Learning in Probabilistic Domains
, 1992
"... as a testbed for designing intelligent agents. The system consists of an overall agent architecture and five components within the architecture. The five components are: 1. GoalDirected Learning (GDL), a decisiontheoretic method for selecting learning goals. 2. Probabilistic Bias Evaluation (PBE) ..."
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Cited by 8 (3 self)
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as a testbed for designing intelligent agents. The system consists of an overall agent architecture and five components within the architecture. The five components are: 1. GoalDirected Learning (GDL), a decisiontheoretic method for selecting learning goals. 2. Probabilistic Bias Evaluation (PBE), a technique for using probabilistic background knowledge to select learning biases for the learning goals. 3. Uniquely Predictive Theories (UPTs) and Probability Computation using Independence (PCI), a probabilistic representation and Bayesian inference method for the agent's theories. 4. A probabilistic learning component, consisting of a heuristic search algorithm and a Bayesian method for evaluating proposed theories. 5. A decisiontheoretic probabilistic planner, which searches through the probability space defined by the agent's current theory to select the best action. PAGODA is given as input an initial planning goal (its ove
Significance Tests, Belief Calculi, and Burden
 of Proof in Legal and Scientific Discourse. Laptec2003, Frontiers in Artificial Intelligence and its Applications
, 2003
"... ..."
Probability, Confirmation, and the Conjunction Fallacy
, 2007
"... Abstract. The conjunction fallacy has been a key topic in debates on the rationality of human reasoning and its limitations. Despite extensive inquiry, however, the attempt of providing a satisfactory account of the phenomenon has proven challenging. Here, we elaborate the suggestion (first discusse ..."
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Cited by 4 (2 self)
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Abstract. The conjunction fallacy has been a key topic in debates on the rationality of human reasoning and its limitations. Despite extensive inquiry, however, the attempt of providing a satisfactory account of the phenomenon has proven challenging. Here, we elaborate the suggestion (first discussed by Sides et al., 2001) that in standard conjunction problems the fallacious probability judgments experimentally observed are typically guided by sound assessments of confirmation relations, meant in terms of contemporary Bayesian confirmation theory. Our main formal result is a confirmationtheoretic account of the conjunction fallacy which is proven robust (i.e., not depending on various alternative ways of measuring degrees of confirmation). The proposed analysis is shown distinct from contentions that the conjunction effect is in fact not a fallacy and is compared with major competing explanations of the phenomenon, including earlier references to a confirmationtheoretic account.
Cognitive constructivism, eigensolutions, and sharp statistical hypotheses
 Cybern. Hum. Knowing 2007
"... ..."
Carnap and the logic of inductive inference
 Handbook of the history of logic. Volume 10: Inductive logic
, 2009
"... This chapter discusses Carnap’s work on probability and induction, using the notation and terminology of modern mathematical probability, viewed from the perspective of the modern Bayesian or subjective school of probability. (It is a much expanded and more mathematical version of [Zabell, 2007]). C ..."
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This chapter discusses Carnap’s work on probability and induction, using the notation and terminology of modern mathematical probability, viewed from the perspective of the modern Bayesian or subjective school of probability. (It is a much expanded and more mathematical version of [Zabell, 2007]). Carnap initially