On De Finetti's Instrumentalist Philosophy of Probability
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De Finetti, Bruno (1906–1985)
De Finetti, Bruno (1906–1985) ERMANNO PITACCO Volume 1, pp. 421–423 In Encyclopedia Of Actuarial Science (ISBN 0-470-84676-3) Edited by Jozef L. Teugels and Bjørn Sundt John Wiley & Sons, Ltd, Chichester, 2004 De Finetti, Bruno The Scientific Work (1906–1985) The scientific activity of de Finetti pertains to a wide range of research fields. Nevertheless, Bruno de Finetti is a world-renowned scientist mainly because of his outstanding contributions to probability and Essential Biography statistics. Foundations of subjective probability (see Bayesian Statistics), stochastic processes with inde- pendent increments, sequences of exchangeable ran- Bruno de Finetti was born on June 13, 1906 in dom variables, and statistical inference are the main Innsbruck (Austria). His parents were Italian, coming areas in which we find de Finetti’s contributions. from regions that until 1918 belonged to the Austro- Actually, de Finetti must be considered as one of the Hungarian Empire. At that time, de Finetti’s father, founders of the modern subjective approach to prob- an engineer, was actually engaged in the construction ability (see, in particular, the well-known treatise, de of railways in Tyrol. Finetti [11]). Bruno de Finetti entered the Polytechnic of Milan, For a comprehensive description of de Finetti’s aiming at a degree in engineering. After two years, contribution to probability and statistics, the reader since a Faculty of Mathematics was opened in can refer to [1]; see also [13]. Milan, he moved to mathematical studies and in Extremely significant contributions can be found 1927 he obtained a degree in applied mathemat- also in the fields of mathematical analysis, eco- ics. -
Imprecise Probability in Risk Analysis
IMPRECISE PROBABILITY IN RISK ANALYSIS D. Dubois IRIT-CNRS, Université Paul Sabatier 31062 TOULOUSE FRANCE Outline 1. Variability vs incomplete information 2. Blending set-valued and probabilistic representations : uncertainty theories 3. Possibility theory in the landscape 4. A methodology for risk-informed decision- making 5. Some applications Origins of uncertainty • The variability of observed repeatable natural phenomena : « randomness ». – Coins, dice…: what about the outcome of the next throw? • The lack of information: incompleteness – because of information is often lacking, knowledge about issues of interest is generally not perfect. • Conflicting testimonies or reports:inconsistency – The more sources, the more likely the inconsistency Example • Variability: daily quantity of rain in Toulouse – May change every day – It can be estimated through statistical observed data. – Beliefs or prediction based on this data • Incomplete information : Birth date of Brazil President – It is not a variable: it is a constant! – You can get the correct info somewhere, but it is not available. – Most people may have a rough idea (an interval), a few know precisely, some have no idea: information is subjective. – Statistics on birth dates of other presidents do not help much. • Inconsistent information : several sources of information conflict concerning the birth date (a book, a friend, a website). The roles of probability Probability theory is generally used for representing two aspects: 1. Variability: capturing (beliefs induced by) variability through repeated observations. 2. Incompleteness (info gaps): directly modeling beliefs via betting behavior observation. These two situations are not mutually exclusive. Using a single probability distribution to represent incomplete information is not entirely satisfactory: The betting behavior setting of Bayesian subjective probability enforces a representation of partial ignorance based on single probability distributions. -
An Improved Dutch Book Theorem for Conditionalization
An Improved Dutch Book Theorem for Conditionalization Michael Rescorla Abstract: Lewis proved a Dutch book theorem for Conditionalization. The theorem shows that an agent who follows any credal update rule other than Conditionalization is vulnerable to bets that inflict a sure loss. Lewis’s theorem is tailored to factive formulations of Conditionalization, i.e. formulations on which the conditioning proposition is true. Yet many scientific and philosophical applications of Bayesian decision theory require a non-factive formulation, i.e. a formulation on which the conditioning proposition may be false. I prove a Dutch book theorem tailored to non-factive Conditionalization. I also discuss the theorem’s significance. §1. Diachronic Dutch books The diachronic norm Conditionalization is a central plank of Bayesian decision theory. Conditionalization requires that, in certain situations, you conditionalize on a proposition E by replacing your old credences P with new credences Pnew given by: Pnew(H) = P(H | E). Here P(H | E) is the old conditional probability of H given E, defined by the ratio formula: PHE(&) PHE( | ) PE() for cases where P(E) > 0. A conditioning proposition is a proposition on which you have conditionalized. The probability calculus axioms entail that P(E | E) = 1, so conditionalizing on E requires you to become certain of E, i.e. to set Pnew(E) = 1. 2 Philosophers pursue various strategies for defending Conditionalization. One prominent strategy builds upon the classic Dutch book arguments advanced by Ramsey (1931) and de Finetti (1937/1980). A Dutch book is a set of acceptable bets that guarantees a net loss. You are Dutch bookable when it is possible to rig a Dutch book against you. -
Dutch Books, Additivity, and Utility Theory1
PHILOSOPHICAL TOPICS Vol. 21 No. 1, Spring 1993 Dutch Books, Additivity, and Utility Theory1 Brad Armendt Arizona State University One guide to an argument's significance is the number and variety of refutations it attracts. By this measure, the Dutch book argument has considerable importance.2 Of course this measure alone is not a sure guide to locating arguments deserving of our attention—if a decisive refutation has really been given, we are better off pursuing other topics. But the presence of many and varied counterarguments at least suggests that either the refutations are controversial, or that their target admits of more than one interpretation, or both. The main point of this paper is to focus on a way of understanding the Dutch Book argument (DBA) that avoids many of the well-known criticisms, and to consider how it fares against an important criticism that still remains: the objection that the DBA presupposes value-independence of bets.3 It is safe to say that the influence of Bayesian approaches to current work in decision theory, epistemology, and philosophy of science, is not on the wane—in fact the opposite. Yet the acceptance of the DBA, which is heuristically the most potent argument for that approach, may be in decline. If so, perhaps the explanation is that we have come to rely more on the decision-theoretic foundations for Bayesian theory, and one good argument is enough. Another point of this paper therefore, is to compare, in the light of objections to it, the status of the DBA with that of the decision-theoretic argument(s) 1 Thanks to Stewart Cohen, Jim Joyce, Mark Kaplan, Bernie Kobes, Patrick Maher, Brian Skyrms, and Bas van Fraassen for helpful discussions related to this paper. -
FRANK RAMSEY OUP CORRECTED PROOF – FINAL, 7/1/2020, Spi OUP CORRECTED PROOF – FINAL, 7/1/2020, Spi
OUP CORRECTED PROOF – FINAL, 7/1/2020, SPi FRANK RAMSEY OUP CORRECTED PROOF – FINAL, 7/1/2020, SPi OUP CORRECTED PROOF – FINAL, 7/1/2020, SPi CHERYL MISAK FRANK RAMSEY a sheer excess of powers 1 OUP CORRECTED PROOF – FINAL, 7/1/2020, SPi 3 Great Clarendon Street, Oxford, OXDP, United Kingdom Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries © Cheryl Misak The moral rights of the author have been asserted First Edition published in Impression: All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by licence or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate this work in any other form and you must impose this same condition on any acquirer Published in the United States of America by Oxford University Press Madison Avenue, New York, NY , United States of America British Library Cataloguing in Publication Data Data available Library of Congress Control Number: ISBN –––– Printed and bound in Great Britain by Clays Ltd, Elcograf S.p.A. Links to third party websites are provided by Oxford in good faith and for information only. -
Call for Papers
Honorary Editor: Kumaraswamy Vela Velupillai Editor: Nicola Mattoscio ‐ Co‐editor: Tonio Di Battista ISSN (print) 1722‐4241 ISSN (online) 1974‐5125 indexed by EconLit (American Economic Association) Phones +39 085 4219109 +39 085 4537980 www.gler.it ‐ [email protected] McGraw‐Hill Education CALL FOR PAPERS ‐ deadline extended to 31 May, 2017 ‐ Call for papers for the Special Issue in honour of Bruno de Finetti Context and motivations The international journal Global & Local Economic Review announces the Special Issue to commemorate one of the most reputed Italian scientist of the Twentieth century, Bruno de Finetti (Innsbruck 1906, Rome 1985). Global & Local Economic Review joins with the University of Chieti‐Pescara (Italy) even this year in order to pay tribute to a distinguished scholar who was a key figure in the development of the conceptual foundations of probability and mathematical statistics. Several public and private institutions have been dedicated to him, including departments, lectures halls, schools and streets. Since a few decades, furthermore, Bruno de Finetti is one of the prime sources of the most popular textbooks of management and applied statistics at the Harvard School of Administration. The European Association for Decision Making based in Amsterdam awards an international prize in honour of Bruno de Finetti every two years from 1995 to present, involving scientists and psychologists around the world. Another notable prize in his honour was established by the prestigious National Academy of Lincei in Rome. There is no doubt that de Finetti was that rare person, a genius. He viewed the world in a novel way which, when one took the trouble to understand it, one noticed that it was so clearly correct. -
Quantum Bayesianism Assessed 1 Introduction
Quantum Bayesianism Assessed John Earman Dept. HPS University of Pittsburgh Abstract: The idea that the quantum probabilities are best construed as the personal/subjective degrees of belief of Bayesian agents is an old one. In recent years the idea has been vigorously pursued by a group of physicists who fly the banner of quantum Bayesianism (QBism). The present paper aims to identify the prospects and problems of implementing QBism, and it critically assesses the claim that QBism provides a resolution (or dissolution) of some of the long standing foundations issues in quantum mechanics, including the measurement problem and puzzles of non-locality. 1 Introduction Bruno de Finetti, the patron saint of the personalist (a.k.a. subjectivist) interpretation of probability, famously proclaimed that “THERE ARE NO PROBABILITIES”(de Finetti 1990, p. x). Decoding the bombast, what de Finetti meant to convey is the claim that there are no probabilities in nature, no objective physical probabilities (a.k.a. chances); there are only the degrees of belief of individual agents who, if rational, regiment their credences in conformity with the axioms of probability theory. Although an appendix in Vol. 2 of de Finetti’s Theory of Probability (1990, pp. 313-321) sketches some of the formalism of quantum mechanics (QM), de Finetti himself did not take up the challenge of showing how physicists’deployment of quantum probabilities can be understood in personalist terms. Philosophers of science have an abiding concern with the nature and meaning of probability, and it is natural that some of them have taken up this challenge (see, for example, Pitowsky 2006). -
Imprecise Probability for Multiparty Session Types in Process Algebra APREPRINT
IMPRECISE PROBABILITY FOR MULTIPARTY SESSION TYPES IN PROCESS ALGEBRA A PREPRINT Bogdan Aman Gabriel Ciobanu Romanian Academy, Institute of Computer Science Romanian Academy, Institute of Computer Science Alexandru Ioan Cuza University, Ia¸si, Romania Alexandru Ioan Cuza University, Ia¸si, Romania [email protected] [email protected] February 18, 2020 ABSTRACT In this paper we introduce imprecise probability for session types. More exactly, we use a proba- bilistic process calculus in which both nondeterministic external choice and probabilistic internal choice are considered. We propose the probabilistic multiparty session types able to codify the structure of the communications by using some imprecise probabilities given in terms of lower and upper probabilities. We prove that this new probabilistic typing system is sound, as well as several other results dealing with both classical and probabilistic properties. The approach is illustrated by a simple example inspired by survey polls. Keywords Imprecise Probability · Nondeterministic and Probabilistic Choices · Multiparty Session Types. 1 Introduction Aiming to represent the available knowledge more accurately, imprecise probability generalizes probability theory to allow for partial probability specifications applicable when a unique probability distribution is difficult to be identified. The idea that probability judgments may be imprecise is not novel. It would be enough to mention the Dempster- Shafer theory for reasoning with uncertainty by using upper and lower probabilities [1]. The idea appeared even earlier in 1921, when J.M. Keynes used explicit intervals for approaching probabilities [2] (B. Russell called this book “undoubtedly the most important work on probability that has appeared for a very long time”). -
Demystifying Dilation
Forthcoming in Erkenntnis. Penultimate version. Demystifying Dilation Arthur Paul Pedersen Gregory Wheeler Abstract Dilation occurs when an interval probability estimate of some event E is properly included in the interval probability estimate of E conditional on every event F of some partition, which means that one’s initial estimate of E becomes less precise no matter how an experiment turns out. Critics main- tain that dilation is a pathological feature of imprecise probability models, while others have thought the problem is with Bayesian updating. How- ever, two points are often overlooked: (i) knowing that E is stochastically independent of F (for all F in a partition of the underlying state space) is sufficient to avoid dilation, but (ii) stochastic independence is not the only independence concept at play within imprecise probability models. In this paper we give a simple characterization of dilation formulated in terms of deviation from stochastic independence, propose a measure of dilation, and distinguish between proper and improper dilation. Through this we revisit the most sensational examples of dilation, which play up independence be- tween dilator and dilatee, and find the sensationalism undermined by either fallacious reasoning with imprecise probabilities or improperly constructed imprecise probability models. 1 Good Grief! Unlike free advice, which can be a real bore to endure, accepting free information when it is available seems like a Good idea. In fact, it is: I. J. Good (1967) showed that under certain assumptions it pays you, in expectation, to acquire new infor- mation when it is free. This Good result reveals why it is rational, in the sense of maximizing expected utility, to use all freely available evidence when estimating a probability. -
An Imprecise SHAP As a Tool for Explaining the Class Probability Distributions Under Limited Training Data
An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data Lev V. Utkin, Andrei V. Konstantinov, and Kirill A. Vishniakov Peter the Great St.Petersburg Polytechnic University St.Petersburg, Russia e-mail: [email protected], [email protected], [email protected] Abstract One of the most popular methods of the machine learning prediction explanation is the SHapley Additive exPlanations method (SHAP). An imprecise SHAP as a modification of the original SHAP is proposed for cases when the class probability distributions are imprecise and represented by sets of distributions. The first idea behind the imprecise SHAP is a new approach for computing the marginal contribution of a feature, which fulfils the important efficiency property of Shapley values. The second idea is an attempt to consider a general approach to calculating and reducing interval-valued Shapley values, which is similar to the idea of reachable probability intervals in the imprecise probability theory. A simple special implementation of the general approach in the form of linear optimization problems is proposed, which is based on using the Kolmogorov-Smirnov distance and imprecise contamination models. Numerical examples with synthetic and real data illustrate the imprecise SHAP. Keywords: interpretable model, XAI, Shapley values, SHAP, Kolmogorov-Smirnov distance, imprecise probability theory. arXiv:2106.09111v1 [cs.LG] 16 Jun 2021 1 Introduction An importance of the machine learning models in many applications and their success in solv- ing many applied problems lead to another problem which may be an obstacle for using the models in areas where their prediction accuracy as well as understanding is crucial, for ex- ample, in medicine, reliability analysis, security, etc. -
Propagation of Imprecise Probabilities Through
PROPAGATION OF IMPRECISE PROBABILITIES THROUGH BLACK-BOX MODELS A Thesis Presented to The Academic Faculty by Morgan Bruns In Partial Fulfillment of the Requirements for the Degree Master of Science in the School of Mechanical Engineering Georgia Institute of Technology May 2006 PROPAGATION OF IMPRECISE PROBABILITIES THROUGH BLACK-BOX MODELS Approved by: Dr. Christiaan J.J. Paredis, Advisor School of Mechanical Engineering Georgia Institute of Technology Dr. Bert Bras School of Mechanical Engineering Georgia Institute of Technology Dr. Leon F. McGinnis School of Industrial and Systems Engineering Georgia Institute of Technology Date Approved: April 7, 2006 ACKNOWLEDGEMENTS This thesis would not have been possible without the assistance of many of my friends and colleagues. I would like to thank my advisor, Dr. Chris Paredis, for his support, guidance, and insight. I would also like to thank the students of the Systems Realization Laboratory for the intellectually nurturing environment they have provided. In particular, I would like to thank Jason Aughenbaugh, Scott Duncan, Jay Ling, Rich Malak, and Steve Rekuc for their criticism and advice regarding my research. iii TABLE OF CONTENTS ACKNOWLEDGEMENTS iii LIST OF TABLES vi LIST OF FIGURES vii LIST OF ABBREVIATIONS viii LIST OF SYMBOLS ix SUMMARY xi CHAPTER 1 : INTRODUCTION 1 1.1 Design decision making 1 1.2 Imprecision in design 2 1.3 Challenges for decision making formalisms 4 1.4 Research questions, hypotheses, and thesis outline 5 CHAPTER 2 : DECISION MAKING UNDER IMPRECISE UNCERTAINTY -
Demystifying Dilation
CORE Metadata, citation and similar papers at core.ac.uk Provided by PhilSci Archive Forthcoming in Erkenntnis. Penultimate version. Demystifying Dilation Arthur Paul Pedersen Gregory Wheeler Abstract Dilation occurs when an interval probability estimate of some event E is properly included in the interval probability estimate of E conditional on every event F of some partition, which means that one’s initial estimate of E becomes less precise no matter how an experiment turns out. Critics main- tain that dilation is a pathological feature of imprecise probability models, while others have thought the problem is with Bayesian updating. How- ever, two points are often overlooked: (i) knowing that E is stochastically independent of F (for all F in a partition of the underlying state space) is sufficient to avoid dilation, but (ii) stochastic independence is not the only independence concept at play within imprecise probability models. In this paper we give a simple characterization of dilation formulated in terms of deviation from stochastic independence, propose a measure of dilation, and distinguish between proper and improper dilation. Through this we revisit the most sensational examples of dilation, which play up independence be- tween dilator and dilatee, and find the sensationalism undermined by either fallacious reasoning with imprecise probabilities or improperly constructed imprecise probability models. 1 Good Grief! Unlike free advice, which can be a real bore to endure, accepting free information when it is available seems like a Good idea. In fact, it is: I. J. Good (1967) showed that under certain assumptions it pays you, in expectation, to acquire new infor- mation when it is free.