An Indirect Debiasing Method: Priming a Target Attribute Reduces Judgmental Biases in Likelihood Estimations
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A Task-Based Taxonomy of Cognitive Biases for Information Visualization
A Task-based Taxonomy of Cognitive Biases for Information Visualization Evanthia Dimara, Steven Franconeri, Catherine Plaisant, Anastasia Bezerianos, and Pierre Dragicevic Three kinds of limitations The Computer The Display 2 Three kinds of limitations The Computer The Display The Human 3 Three kinds of limitations: humans • Human vision ️ has limitations • Human reasoning 易 has limitations The Human 4 ️Perceptual bias Magnitude estimation 5 ️Perceptual bias Magnitude estimation Color perception 6 易 Cognitive bias Behaviors when humans consistently behave irrationally Pohl’s criteria distilled: • Are predictable and consistent • People are unaware they’re doing them • Are not misunderstandings 7 Ambiguity effect, Anchoring or focalism, Anthropocentric thinking, Anthropomorphism or personification, Attentional bias, Attribute substitution, Automation bias, Availability heuristic, Availability cascade, Backfire effect, Bandwagon effect, Base rate fallacy or Base rate neglect, Belief bias, Ben Franklin effect, Berkson's paradox, Bias blind spot, Choice-supportive bias, Clustering illusion, Compassion fade, Confirmation bias, Congruence bias, Conjunction fallacy, Conservatism (belief revision), Continued influence effect, Contrast effect, Courtesy bias, Curse of knowledge, Declinism, Decoy effect, Default effect, Denomination effect, Disposition effect, Distinction bias, Dread aversion, Dunning–Kruger effect, Duration neglect, Empathy gap, End-of-history illusion, Endowment effect, Exaggerated expectation, Experimenter's or expectation bias, -
Avoiding the Conjunction Fallacy: Who Can Take a Hint?
Avoiding the conjunction fallacy: Who can take a hint? Simon Klein Spring 2017 Master’s thesis, 30 ECTS Master’s programme in Cognitive Science, 120 ECTS Supervisor: Linnea Karlsson Wirebring Acknowledgments: The author would like to thank the participants for enduring the test session with challenging questions and thereby making the study possible, his supervisor Linnea Karlsson Wirebring for invaluable guidance and good questions during the thesis work, and his fiancée Amanda Arnö for much needed mental support during the entire process. 2 AVOIDING THE CONJUNCTION FALLACY: WHO CAN TAKE A HINT? Simon Klein Humans repeatedly commit the so called “conjunction fallacy”, erroneously judging the probability of two events occurring together as higher than the probability of one of the events. Certain hints have been shown to mitigate this tendency. The present thesis investigated the relations between three psychological factors and performance on conjunction tasks after reading such a hint. The factors represent the understanding of probability and statistics (statistical numeracy), the ability to resist intuitive but incorrect conclusions (cognitive reflection), and the willingness to engage in, and enjoyment of, analytical thinking (need-for-cognition). Participants (n = 50) answered 30 short conjunction tasks and three psychological scales. A bimodal response distribution motivated dichotomization of performance scores. Need-for-cognition was significantly, positively correlated with performance, while numeracy and cognitive reflection were not. The results suggest that the willingness to engage in, and enjoyment of, analytical thinking plays an important role for the capacity to avoid the conjunction fallacy after taking a hint. The hint further seems to neutralize differences in performance otherwise predicted by statistical numeracy and cognitive reflection. -
Cognitive Bias Mitigation: How to Make Decision-Making More Rational?
Cognitive Bias Mitigation: How to make decision-making more rational? Abstract Cognitive biases distort judgement and adversely impact decision-making, which results in economic inefficiencies. Initial attempts to mitigate these biases met with little success. However, recent studies which used computer games and educational videos to train people to avoid biases (Clegg et al., 2014; Morewedge et al., 2015) showed that this form of training reduced selected cognitive biases by 30 %. In this work I report results of an experiment which investigated the debiasing effects of training on confirmation bias. The debiasing training took the form of a short video which contained information about confirmation bias, its impact on judgement, and mitigation strategies. The results show that participants exhibited confirmation bias both in the selection and processing of information, and that debiasing training effectively decreased the level of confirmation bias by 33 % at the 5% significance level. Key words: Behavioural economics, cognitive bias, confirmation bias, cognitive bias mitigation, confirmation bias mitigation, debiasing JEL classification: D03, D81, Y80 1 Introduction Empirical research has documented a panoply of cognitive biases which impair human judgement and make people depart systematically from models of rational behaviour (Gilovich et al., 2002; Kahneman, 2011; Kahneman & Tversky, 1979; Pohl, 2004). Besides distorted decision-making and judgement in the areas of medicine, law, and military (Nickerson, 1998), cognitive biases can also lead to economic inefficiencies. Slovic et al. (1977) point out how they distort insurance purchases, Hyman Minsky (1982) partly blames psychological factors for economic cycles. Shefrin (2010) argues that confirmation bias and some other cognitive biases were among the significant factors leading to the global financial crisis which broke out in 2008. -
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]. -
Cognitive Biases in Economic Decisions – Three Essays on the Impact of Debiasing
TECHNISCHE UNIVERSITÄT MÜNCHEN Lehrstuhl für Betriebswirtschaftslehre – Strategie und Organisation Univ.-Prof. Dr. Isabell M. Welpe Cognitive biases in economic decisions – three essays on the impact of debiasing Christoph Martin Gerald Döbrich Abdruck der von der Fakultät für Wirtschaftswissenschaften der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften (Dr. rer. pol.) genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Gunther Friedl Prüfer der Dissertation: 1. Univ.-Prof. Dr. Isabell M. Welpe 2. Univ.-Prof. Dr. Dr. Holger Patzelt Die Dissertation wurde am 28.11.2012 bei der Technischen Universität München eingereicht und durch die Fakultät für Wirtschaftswissenschaften am 15.12.2012 angenommen. Acknowledgments II Acknowledgments Numerous people have contributed to the development and successful completion of this dissertation. First of all, I would like to thank my supervisor Prof. Dr. Isabell M. Welpe for her continuous support, all the constructive discussions, and her enthusiasm concerning my dissertation project. Her challenging questions and new ideas always helped me to improve my work. My sincere thanks also go to Prof. Dr. Matthias Spörrle for his continuous support of my work and his valuable feedback for the articles building this dissertation. Moreover, I am grateful to Prof. Dr. Dr. Holger Patzelt for acting as the second advisor for this thesis and Professor Dr. Gunther Friedl for leading the examination board. This dissertation would not have been possible without the financial support of the Elite Network of Bavaria. I am very thankful for the financial support over two years which allowed me to pursue my studies in a focused and efficient manner. Many colleagues at the Chair for Strategy and Organization of Technische Universität München have supported me during the completion of this thesis. -
Working Memory, Cognitive Miserliness and Logic As Predictors of Performance on the Cognitive Reflection Test
Working Memory, Cognitive Miserliness and Logic as Predictors of Performance on the Cognitive Reflection Test Edward J. N. Stupple ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Maggie Gale ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Christopher R. Richmond ([email protected]) Centre for Psychological Research, University of Derby Kedleston Road, Derby. DE22 1GB Abstract Most participants respond that the answer is 10 cents; however, a slower and more analytic approach to the The Cognitive Reflection Test (CRT) was devised to measure problem reveals the correct answer to be 5 cents. the inhibition of heuristic responses to favour analytic ones. The CRT has been a spectacular success, attracting more Toplak, West and Stanovich (2011) demonstrated that the than 100 citations in 2012 alone (Scopus). This may be in CRT was a powerful predictor of heuristics and biases task part due to the ease of administration; with only three items performance - proposing it as a metric of the cognitive miserliness central to dual process theories of thinking. This and no requirement for expensive equipment, the practical thesis was examined using reasoning response-times, advantages are considerable. There have, moreover, been normative responses from two reasoning tasks and working numerous correlates of the CRT demonstrated, from a wide memory capacity (WMC) to predict individual differences in range of tasks in the heuristics and biases literature (Toplak performance on the CRT. These data offered limited support et al., 2011) to risk aversion and SAT scores (Frederick, for the view of miserliness as the primary factor in the CRT. -
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. -
The Challenge of Debiasing Personnel Decisions: Avoiding Both Under- and Overcorrection
University of Pennsylvania ScholarlyCommons Management Papers Wharton Faculty Research 12-2008 The Challenge of Debiasing Personnel Decisions: Avoiding Both Under- and Overcorrection Philip E. Tetlock University of Pennsylvania Gregory Mitchell Terry L. Murray Follow this and additional works at: https://repository.upenn.edu/mgmt_papers Part of the Business Administration, Management, and Operations Commons Recommended Citation Tetlock, P. E., Mitchell, G., & Murray, T. L. (2008). The Challenge of Debiasing Personnel Decisions: Avoiding Both Under- and Overcorrection. Industrial and Organizational Psychology, 1 (4), 439-443. http://dx.doi.org/10.1111/j.1754-9434.2008.00084.x This paper is posted at ScholarlyCommons. https://repository.upenn.edu/mgmt_papers/32 For more information, please contact [email protected]. The Challenge of Debiasing Personnel Decisions: Avoiding Both Under- and Overcorrection Disciplines Business Administration, Management, and Operations This journal article is available at ScholarlyCommons: https://repository.upenn.edu/mgmt_papers/32 1 The Challenge of Debiasing Personnel Decisions: Avoiding Both Under- and Over-Correction Philip E. Tetlock, * Gregory Mitchell, ** and Terry L. Murray *** Introduction This commentary advances two interrelated scientific arguments. First, we endorse Landy's (2008) concerns about the insufficient emphasis placed on individuating information by scholars eager to import social-cognition work on stereotyping into employment law. Building on Landy’s analysis, we emphasize that greater attention needs to be given to the power of accountability and teamwork incentives to motivate personnel decision-makers to seek and utilize individuating information that is predictive of job-relevant behavior. Second, we note how easy it is for exchanges between proponents and skeptics of unconscious stereotyping to lead to ideological stalemates. -
Cognitive Biases in Software Engineering: a Systematic Mapping Study
Cognitive Biases in Software Engineering: A Systematic Mapping Study Rahul Mohanani, Iflaah Salman, Burak Turhan, Member, IEEE, Pilar Rodriguez and Paul Ralph Abstract—One source of software project challenges and failures is the systematic errors introduced by human cognitive biases. Although extensively explored in cognitive psychology, investigations concerning cognitive biases have only recently gained popularity in software engineering research. This paper therefore systematically maps, aggregates and synthesizes the literature on cognitive biases in software engineering to generate a comprehensive body of knowledge, understand state of the art research and provide guidelines for future research and practise. Focusing on bias antecedents, effects and mitigation techniques, we identified 65 articles (published between 1990 and 2016), which investigate 37 cognitive biases. Despite strong and increasing interest, the results reveal a scarcity of research on mitigation techniques and poor theoretical foundations in understanding and interpreting cognitive biases. Although bias-related research has generated many new insights in the software engineering community, specific bias mitigation techniques are still needed for software professionals to overcome the deleterious effects of cognitive biases on their work. Index Terms—Antecedents of cognitive bias. cognitive bias. debiasing, effects of cognitive bias. software engineering, systematic mapping. 1 INTRODUCTION OGNITIVE biases are systematic deviations from op- knowledge. No analogous review of SE research exists. The timal reasoning [1], [2]. In other words, they are re- purpose of this study is therefore as follows: curring errors in thinking, or patterns of bad judgment Purpose: to review, summarize and synthesize the current observable in different people and contexts. A well-known state of software engineering research involving cognitive example is confirmation bias—the tendency to pay more at- biases. -
Mitigating the Attraction Effect with Visualizations Evanthia Dimara, Gilles Bailly, Anastasia Bezerianos, Steven Franconeri
Mitigating the Attraction Effect with Visualizations Evanthia Dimara, Gilles Bailly, Anastasia Bezerianos, Steven Franconeri To cite this version: Evanthia Dimara, Gilles Bailly, Anastasia Bezerianos, Steven Franconeri. Mitigating the Attrac- tion Effect with Visualizations. IEEE Transactions on Visualization and Computer Graphics, Insti- tute of Electrical and Electronics Engineers, 2019, TVCG 2019 (InfoVis 2018), 25 (1), pp.850 - 860. 10.1109/TVCG.2018.2865233. hal-01845004v2 HAL Id: hal-01845004 https://hal.inria.fr/hal-01845004v2 Submitted on 22 Aug 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2018.2865233, IEEE Transactions on Visualization and Computer Graphics Mitigating the Attraction Effect with Visualizations Evanthia Dimara, Gilles Bailly, Anastasia Bezerianos, and Steven Franconeri Abstract—Human decisions are prone to biases, and this is no less true for decisions made within data visualizations. Bias mitigation strategies often focus on the person, by educating people about their biases, typically with little success. We focus instead on the system, presenting the first evidence that altering the design of an interactive visualization tool can mitigate a strong bias – the attraction effect. -
How a Machine Learns and Fails 2019
Repositorium für die Medienwissenschaft Matteo Pasquinelli How a Machine Learns and Fails 2019 https://doi.org/10.25969/mediarep/13490 Veröffentlichungsversion / published version Zeitschriftenartikel / journal article Empfohlene Zitierung / Suggested Citation: Pasquinelli, Matteo: How a Machine Learns and Fails. In: spheres: Journal for Digital Cultures. Spectres of AI (2019), Nr. 5, S. 1–17. DOI: https://doi.org/10.25969/mediarep/13490. Erstmalig hier erschienen / Initial publication here: https://spheres-journal.org/wp-content/uploads/spheres-5_Pasquinelli.pdf Nutzungsbedingungen: Terms of use: Dieser Text wird unter einer Creative Commons - This document is made available under a creative commons - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0/ Attribution - Non Commercial - No Derivatives 4.0/ License. For Lizenz zur Verfügung gestellt. Nähere Auskünfte zu dieser Lizenz more information see: finden Sie hier: http://creativecommons.org/licenses/by-nc-nd/4.0/ http://creativecommons.org/licenses/by-nc-nd/4.0/ © the author(s) 2019 www.spheres-journal.org ISSN 2363-8621 #5 Spectres of AI sadfasdf MATTEO PASQUINELLI HOW A MACHINE LEARNS AND FAILS – A GRAMMAR OF ERROR FOR ARTIFICIAL INTELLIGENCE “Once the characteristic numbers are established for most concepts, mankind will then possess a new instrument which will enhance the capabilities of the mind to a far greater extent than optical instruments strengthen the eyes, and will supersede the microscope and telescope to the same extent that reason is superior to eyesight.”1 — Gottfried Wilhelm Leibniz. “The Enlightenment was […] not about consensus, it was not about systematic unity, and it was not about the deployment of instrumental reason: what was developed in the Enlightenment was a modern idea of truth defined by error, a modern idea of knowledge defined by failure, conflict, and risk, but also hope.”2 — David Bates. -
Fixing Biases: Principles of Cognitive De-Biasing
Fixing biases: Principles of cognitive de-biasing Pat Croskerry MD, PhD Clinical Reasoning in Medical Education National Science Learning Centre, University of York Workshop, November 26, 2019 Case q A 65 year old female presents to the ED with a complaint of shoulder sprain. She said she was gardening this morning and injured her shoulder pushing her lawn mower. q At triage she has normal vital signs and in no distress. The triage nurse notes her complaint and triages her to the fast track area. q She is seen by an emergency physician who notes her complaint and examines her shoulder. He orders an X-ray. q The shoulder X ray shows narrowing of the joint and signs of osteoarthrtritis q He discharges her with a sling and Rx for Arthrotec q She is brought to the ED 4 hours later following an episode of syncope, sweating, and weakness. She is diagnosed with an inferior MI. Biases q A 65 year old female presents to the ED with a complaint of ‘shoulder sprain’. She said she was gardening this morning and sprained her shoulder pushing her lawn mower (Framing). q At triage she has normal vital signs and in no distress. The triage nurse notes her complaint and triages her to the fast track area (Triage cueing). q She is seen by an emergency physician who notes her complaint and examines her shoulder. He orders an X-ray (Ascertainment bias). q The shoulder X ray shows narrowing of the joint and signs of osteoarthrtritis. He explains to the patient the cause of her pain (Confirmation bias).