How to Improve Bayesian Reasoning Without Instruction: Frequency Formats1
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Conjunction Fallacy' Revisited: How Intelligent Inferences Look Like Reasoning Errors
Journal of Behavioral Decision Making J. Behav. Dec. Making, 12: 275±305 (1999) The `Conjunction Fallacy' Revisited: How Intelligent Inferences Look Like Reasoning Errors RALPH HERTWIG* and GERD GIGERENZER Max Planck Institute for Human Development, Berlin, Germany ABSTRACT Findings in recent research on the `conjunction fallacy' have been taken as evid- ence that our minds are not designed to work by the rules of probability. This conclusion springs from the idea that norms should be content-blind Ð in the present case, the assumption that sound reasoning requires following the con- junction rule of probability theory. But content-blind norms overlook some of the intelligent ways in which humans deal with uncertainty, for instance, when drawing semantic and pragmatic inferences. In a series of studies, we ®rst show that people infer nonmathematical meanings of the polysemous term `probability' in the classic Linda conjunction problem. We then demonstrate that one can design contexts in which people infer mathematical meanings of the term and are therefore more likely to conform to the conjunction rule. Finally, we report evidence that the term `frequency' narrows the spectrum of possible interpreta- tions of `probability' down to its mathematical meanings, and that this fact Ð rather than the presence or absence of `extensional cues' Ð accounts for the low proportion of violations of the conjunction rule when people are asked for frequency judgments. We conclude that a failure to recognize the human capacity for semantic and pragmatic inference can lead rational responses to be misclassi®ed as fallacies. Copyright # 1999 John Wiley & Sons, Ltd. KEY WORDS conjunction fallacy; probabalistic thinking; frequentistic thinking; probability People's apparent failures to reason probabilistically in experimental contexts have raised serious concerns about our ability to reason rationally in real-world environments. -
Rational Wishful Thinking
Rational self-deception Eli Spiegelman a Jan 31, 2018 a CEREN, EA 7477, Burgundy School of Business - Université Bourgogne Franche-Comté, 29 rue Sambin, Dijon 21000, France. [email protected] Keywords: Wishful thinking; self-deception; beliefs; choice theory; experiment; regret theory; ambiguity JEL D8, D9 Funding This research benefitted from funding from LESSAC, Burgundy School of Business. Abstract This note presents a model of wishful thinking as manipulation of prior beliefs, and a novel experimental test. The theoretical context is a three-period model, in which the agent is faced with a state-contingent optimal action, in which one state yields a higher payoff. In period 0 she observes the objective prior probability that each state will occur, but may alter her beliefs about these probabilities (self-deceive). The beliefs she chooses in period 0 determine her action in period 1 as a standard maximization procedure. In period 2, a signal yields information about the state of the world. A key assumption is that this signal may not be perfectly revealing. It is shown that the objective prior is optimal if and only if the signal in period 2 is perfectly revealing. Predictions of the theory are tested in a bet-choice experiment. I then present an experimental test designed to investigate the model’s predictions. Subjects choose to play a bet or pass it up for another, and the experimental control varies whether learn about the second bet upon taking the first. The results of the experiment are also compared to the literature on regret theory and ambiguity aversion. -
The Origins of Base-Rate Neglect
Young Children’s Ability to Integrate Social and Numerical Information: The Origins of Base-rate Neglect by Samantha Gualtieri A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor in Philosophy in Psychology Waterloo, Ontario, Canada, 2019 © Samantha Gualtieri 2019 Examining Committee Membership The following served on the Examining Committee for this thesis. The decision of the Examining Committee is by majority vote. External Examiner Lili Ma Associate Professor Supervisor Stephanie Denison Associate Professor Internal Member Heather Henderson Professor Internal Member Jonathan Fugelsang Professor Internal-external Member John Turri Professor ii Author’s Declaration I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. iii Abstract The seminal work of Kahneman and Tversky (1973) sparked an interest in the biases that govern decision-making, notably due to their findings on adults’ tendency to neglect base-rate information (i.e., prior probability) when it conflicts with social information (e.g., a personality description, or testimony information). Though research over the past 45 years uncovered the conditions that lead to base-rate neglect, very little has investigated the origins of these biases. Young children can use base-rate and social information on their own, but their ability to integrate this information remains poorly understood. Do children show a preference for social information as soon as they are able to use it? Or, does their preference for social information develop over time? The current thesis explores 4- to 6-year-old children’s ability to integrate base-rate and social information, providing insight into the origins of base-rate neglect. -
Thinking About the Ultimate Argument for Realism∗
Thinking About the Ultimate Argument for Realism∗ Stathis Psillos Department of Philosophy and History of Science University of Athens Panepistimioupolis (University Campus) Athens 15771 Greece [email protected] 1. Introduction Alan Musgrave has been one of the most passionate defenders of scientific realism. Most of his papers in this area are, by now, classics. The title of my paper alludes to Musgrave’s piece “The Ultimate Argument for Realism”, though the expression is Bas van Fraassen’s (1980, 39), and the argument is Hilary Putnam’s (1975, 73): realism “is the only philosophy of science that does not make the success of science a miracle”. Hence, the code-name ‘no-miracles’ argument (henceforth, NMA). In fact, NMA has quite a history and a variety of formulations. I have documented all this in my (1999, chapter 4). But, no matter how exactly the argument is formulated, its thrust is that the success of scientific theories lends credence to the following two theses: a) that scientific theories should be interpreted realistically and b) that, so interpreted, these theories are approximately true. The original authors of the argument, however, did not put an extra stress on novel predictions, which, as Musgrave (1988) makes plain, is the litmus test for the ability of any approach to science to explain the success of science. Here is why reference to novel predictions is crucial. Realistically understood, theories entail too many novel claims, most of them about unobservables (e.g., that ∗ I want to dedicate this paper to Alan Musgrave. His exceptional combination of clear-headed and profound philosophical thinking has been a model for me. -
Graphical Techniques in Debiasing: an Exploratory Study
GRAPHICAL TECHNIQUES IN DEBIASING: AN EXPLORATORY STUDY by S. Bhasker Information Systems Department Leonard N. Stern School of Business New York University New York, New York 10006 and A. Kumaraswamy Management Department Leonard N. Stern School of Business New York University New York, NY 10006 October, 1990 Center for Research on Information Systems Information Systems Department Leonard N. Stern School of Business New York University Working Paper Series STERN IS-90-19 Forthcoming in the Proceedings of the 1991 Hawaii International Conference on System Sciences Center for Digital Economy Research Stem School of Business IVorking Paper IS-90-19 Center for Digital Economy Research Stem School of Business IVorking Paper IS-90-19 2 Abstract Base rate and conjunction fallacies are consistent biases that influence decision making involving probability judgments. We develop simple graphical techniques and test their eflcacy in correcting for these biases. Preliminary results suggest that graphical techniques help to overcome these biases and improve decision making. We examine the implications of incorporating these simple techniques in Executive Information Systems. Introduction Today, senior executives operate in highly uncertain environments. They have to collect, process and analyze a deluge of information - most of it ambiguous. But, their limited information acquiring and processing capabilities constrain them in this task [25]. Increasingly, executives rely on executive information/support systems for various purposes like strategic scanning of their business environments, internal monitoring of their businesses, analysis of data available from various internal and external sources, and communications [5,19,32]. However, executive information systems are, at best, support tools. Executives still rely on their mental or cognitive models of their businesses and environments and develop heuristics to simplify decision problems [10,16,25]. -
The Art of Thinking Clearly
For Sabine The Art of Thinking Clearly Rolf Dobelli www.sceptrebooks.co.uk First published in Great Britain in 2013 by Sceptre An imprint of Hodder & Stoughton An Hachette UK company 1 Copyright © Rolf Dobelli 2013 The right of Rolf Dobelli to be identified as the Author of the Work has been asserted by him in accordance with the Copyright, Designs and Patents Act 1988. 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 written permission of the publisher, nor be otherwise circulated in any form of binding or cover other than that in which it is published and without a similar condition being imposed on the subsequent purchaser. A CIP catalogue record for this title is available from the British Library. eBook ISBN 978 1 444 75955 6 Hardback ISBN 978 1 444 75954 9 Hodder & Stoughton Ltd 338 Euston Road London NW1 3BH www.sceptrebooks.co.uk CONTENTS Introduction 1 WHY YOU SHOULD VISIT CEMETERIES: Survivorship Bias 2 DOES HARVARD MAKE YOU SMARTER?: Swimmer’s Body Illusion 3 WHY YOU SEE SHAPES IN THE CLOUDS: Clustering Illusion 4 IF 50 MILLION PEOPLE SAY SOMETHING FOOLISH, IT IS STILL FOOLISH: Social Proof 5 WHY YOU SHOULD FORGET THE PAST: Sunk Cost Fallacy 6 DON’T ACCEPT FREE DRINKS: Reciprocity 7 BEWARE THE ‘SPECIAL CASE’: Confirmation Bias (Part 1) 8 MURDER YOUR DARLINGS: Confirmation Bias (Part 2) 9 DON’T BOW TO AUTHORITY: Authority Bias 10 LEAVE YOUR SUPERMODEL FRIENDS AT HOME: Contrast Effect 11 WHY WE PREFER A WRONG MAP TO NO -
Zusammenfassung
Zusammenfassung Auf den folgenden beiden Seiten werden die zuvor detailliert darge- stellten 100 Fehler nochmals übersichtlich gruppiert und klassifiziert. Der kompakte Überblick soll Sie dabei unterstützen, die systemati- schen Fehler durch geeignete Maßnahmen möglichst zu verhindern beziehungsweise so zu reduzieren, dass sich keine allzu negativen Auswirkungen auf Ihr operatives Geschäft ergeben! Eine gezielte und strukturierte Auseinandersetzung mit den Fehler(kategorie)n kann Sie ganz allgemein dabei unterstützen, Ihr Unternehmen „resilienter“ und „antifragiler“ aufzustellen, das heißt, dass Sie von negativen Entwicklungen nicht sofort umgeworfen werden (können). Außerdem kann eine strukturierte Auseinander- setzung auch dabei helfen, sich für weitere, hier nicht explizit genannte – aber ähnliche – Fehler zu wappnen. © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019 402 C. Glaser, Risiko im Management, https://doi.org/10.1007/978-3-658-25835-1 403 Zusammenfassung Zu viele Zu wenig Nicht ausreichend Informationen Informationsgehalt Zeit zur Verfügung Deshalb beachten wir Deshalb füllen wir die Deshalb nehmen wir häufig typischerweise nur… Lücken mit… an, dass… Veränderungen Mustern und bekannten wir Recht haben Außergewöhnliches Geschichten wir das schaffen können Wiederholungen Allgemeinheiten und das Naheliegendste auch Bekanntes/Anekdoten Stereotypen das Beste ist Bestätigungen vereinfachten wir beenden sollten, was Wahrscheinlichkeiten und wir angefangen haben Zahlen unsere Optionen einfacheren -
2 Cognitive Load Increases Risk Aversion 5 2.1 Introduction
Essays in Experimental and Neuroeconomics DISSERTATION zur Erlangung des akademischen Grades Dr. rer. pol. im Fach Volkswirtschaftslehre eingereicht an der Wirtschaftswissenschaftlichen Fakultät der Humboldt-Universität zu Berlin von Diplom-Volkswirt Holger Gerhardt geboren am 17. Januar 1978 in Arnsberg Präsident der Humboldt-Universität zu Berlin: Prof. Dr. Jan-Hendrik Olbertz Dekan der Wirtschaftswissenschaftlichen Fakultät: Prof. Oliver Günther, Ph. D. Gutachter: 1. Prof. Lutz Weinke, Ph. D. 2. Prof. Dr. Hauke R. Heekeren eingereicht am: 9. Mai 2011 Tag des Kolloquiums: 20. Juni 2011 Contents 1 Introduction 1 Bibliography . 3 2 Cognitive load increases risk aversion 5 2.1 Introduction . 5 2.2 Related literature . 7 2.2.1 Introductory remarks . 7 2.2.2 Overview of dual-system and “dual-self” approaches . 7 2.2.3 Subjective expected-utility theory as a unitary-process model of decision making under risk . 10 2.2.4 Dual-process approaches to decision making under risk . 11 2.2.5 Empirical evidence on dual processes in decision making under risk . 12 2.3 Experimental design . 17 2.3.1 Introduction: Advantages of our design over alternative designs . 17 2.3.2 Trial setup . 18 2.3.3 Additional measures of individual differences . 24 2.4 Results . 24 2.4.1 Introductory remarks . 24 2.4.2 Were the tasks adequate? . 25 2.4.3 How did subjects allocate attention to the two simultane- ous tasks? . 25 2.4.4 Preference reversal?—How often did subjects choose the riskier lottery? . 26 2.4.5 Structural regressions: the influence of additional cognitive load on subjects’ degree of relative risk aversion . -
Heuristics and Biases the Psychology of Intuitive Judgment. In
P1: FYX/FYX P2: FYX/UKS QC: FCH/UKS T1: FCH CB419-Gilovich CB419-Gilovich-FM May 30, 2002 12:3 HEURISTICS AND BIASES The Psychology of Intuitive Judgment Edited by THOMAS GILOVICH Cornell University DALE GRIFFIN Stanford University DANIEL KAHNEMAN Princeton University iii P1: FYX/FYX P2: FYX/UKS QC: FCH/UKS T1: FCH CB419-Gilovich CB419-Gilovich-FM May 30, 2002 12:3 published by the press syndicate of the university of cambridge The Pitt Building, Trumpington Street, Cambridge, United Kingdom cambridge university press The Edinburgh Building, Cambridge CB2 2RU, UK 40 West 20th Street, New York, NY 10011-4211, USA 477 Williamstown, Port Melbourne, VIC 3207, Australia Ruiz de Alarcon´ 13, 28014, Madrid, Spain Dock House, The Waterfront, Cape Town 8001, South Africa http://www.cambridge.org C Cambridge University Press 2002 This book is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2002 Printed in the United States of America Typeface Palatino 9.75/12.5 pt. System LATEX2ε [TB] A catalog record for this book is available from the British Library. Library of Congress Cataloging in Publication data Heuristics and biases : the psychology of intuitive judgment / edited by Thomas Gilovich, Dale Griffin, Daniel Kahneman. p. cm. Includes bibliographical references and index. ISBN 0-521-79260-6 – ISBN 0-521-79679-2 (pbk.) 1. Judgment. 2. Reasoning (Psychology) 3. Critical thinking. I. Gilovich, Thomas. II. Griffin, Dale III. Kahneman, Daniel, 1934– BF447 .H48 2002 153.4 – dc21 2001037860 ISBN 0 521 79260 6 hardback ISBN 0 521 79679 2 paperback iv P1: FYX/FYX P2: FYX/UKS QC: FCH/UKS T1: FCH CB419-Gilovich CB419-Gilovich-FM May 30, 2002 12:3 Contents List of Contributors page xi Preface xv Introduction – Heuristics and Biases: Then and Now 1 Thomas Gilovich and Dale Griffin PART ONE. -
Anticipatory Anxiety and Wishful Thinking
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Engelmann, Jan; Lebreton, Maël; Schwardmann, Peter; van der Weele, Joël J.; Chang, Li-Ang Working Paper Anticipatory Anxiety and Wishful Thinking Tinbergen Institute Discussion Paper, No. TI 2019-042/I Provided in Cooperation with: Tinbergen Institute, Amsterdam and Rotterdam Suggested Citation: Engelmann, Jan; Lebreton, Maël; Schwardmann, Peter; van der Weele, Joël J.; Chang, Li-Ang (2019) : Anticipatory Anxiety and Wishful Thinking, Tinbergen Institute Discussion Paper, No. TI 2019-042/I, Tinbergen Institute, Amsterdam and Rotterdam This Version is available at: http://hdl.handle.net/10419/205332 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under -
Statistical Tips for Interpreting Scientific Claims
Research Skills Seminar Series 2019 CAHS Research Education Program Statistical Tips for Interpreting Scientific Claims Mark Jones Statistician, Telethon Kids Institute 18 October 2019 Research Skills Seminar Series | CAHS Research Education Program Department of Child Health Research | Child and Adolescent Health Service ResearchEducationProgram.org © CAHS Research Education Program, Department of Child Health Research, Child and Adolescent Health Service, WA 2019 Copyright to this material produced by the CAHS Research Education Program, Department of Child Health Research, Child and Adolescent Health Service, Western Australia, under the provisions of the Copyright Act 1968 (C’wth Australia). Apart from any fair dealing for personal, academic, research or non-commercial use, no part may be reproduced without written permission. The Department of Child Health Research is under no obligation to grant this permission. Please acknowledge the CAHS Research Education Program, Department of Child Health Research, Child and Adolescent Health Service when reproducing or quoting material from this source. Statistical Tips for Interpreting Scientific Claims CONTENTS: 1 PRESENTATION ............................................................................................................................... 1 2 ARTICLE: TWENTY TIPS FOR INTERPRETING SCIENTIFIC CLAIMS, SUTHERLAND, SPIEGELHALTER & BURGMAN, 2013 .................................................................................................................................. 15 3 -
How to Improve Bayesian Reasoning Without Instruction: Frequency Formats
Psychological Review Copyright 1995 by the American Psychological Association, Inc. 1995, VoTl02, No. 4,684-704 0033-295X/95/S3.00 How to Improve Bayesian Reasoning Without Instruction: Frequency Formats Gerd Gigerenzer Ulrich Hoffrage University of Chicago Max Planck Institute for Psychological Research Is the mind, by design, predisposed against performing Bayesian inference? Previous research on base rate neglect suggests that the mind lacks the appropriate cognitive algorithms. However, any claim against the existence of an algorithm, Bayesian or otherwise, is impossible to evaluate unless one specifies the information format in which it is designed to operate. The authors show that Bayes- ian algorithms are computationally simpler in frequency formats than in the probability formats used in previous research. Frequency formats correspond to the sequential way information is ac- quired in natural sampling, from animal foraging to neural networks. By analyzing several thousand solutions to Bayesian problems, the authors found that when information was presented in frequency formats, statistically naive participants derived up to 50% of all inferences by Bayesian algorithms. Non-Bayesian algorithms included simple versions of Fisherian and Neyman-Pearsonian inference. Is the mind, by design, predisposed against performing (1854/1958) derived the laws of algebra, logic, and probability Bayesian inference? The classical probabilists of the Enlighten- from what he believed to be the laws of thought. It also became ment, including Condorcet, Poisson, and Laplace, equated the basis of vital contributions to psychology, as when Piaget probability theory with the common sense of educated people, and Inhelder (1951 /1975) added an ontogenetic dimension to who were known then as "hommes eclaires." Laplace (1814/ their Enlightenment view of probabilistic reasoning.