Mitigating Cognitive Bias and Design Distortion
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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, -
The Availability Heuristic
CHAPTER 11 THE AVAILABILITY HEURISTIC According to Amos Tversky and Daniel Kahneman (1974, p. 1127), the availability heuristic is a rule of thumb in which decision makers "assess the frequency of a class or the probability of an event by the ease with which instances or occurrences can be brought to mind." Usually this heuristic works quite well; all things being equal, common events are easier to remember or imagine than are uncommon events. By rely ing on availability to estimate frequency and probability, decision mak ers are able to simplify what might otherwise be very difficult judg ments. As with any heuristic, however, there are cases in which the general rule of thumb breaks down and leads to systematic biases. Some events are more available than others not because they tend to occur frequently or with high probability, but because they are inherently easier to think about, because they have taken place recently, because they are highly emotional, and so forth. This chapter examines three general questions: (1) What are instances in which the availability heuristic leads to biased judgments? (2) Do decision makers perceive an event as more likely after they have imagined it happening? (3) How is vivid information dif ferent from other information? AVAILABILITY GOES AWRY Which is a more likely cause of death in the United States-being killed by falling airplane parts or by a shark? Most people rate shark attacks as more probable than death from falling airplane parts (see Item #7 of the Reader Survey for your answer). Shark attacks certainly receive more publicity than do deaths from falling airplane parts, and they are far easier to imagine (thanks in part to movies such as Jaws). -
Nudge Theory
Nudge Theory http://www.businessballs.com/nudge-theory.htm Nudge theory is a flexible and modern concept for: • understanding of how people think, make decisions, and behave, • helping people improve their thinking and decisions, • managing change of all sorts, and • identifying and modifying existing unhelpful influences on people. Nudge theory was named and popularized by the 2008 book, 'Nudge: Improving Decisions About Health, Wealth, and Happiness', written by American academics Richard H Thaler and Cass R Sunstein. The book is based strongly on the Nobel prize- winning work of the Israeli-American psychologists Daniel Kahneman and Amos Tversky. This article: • reviews and explains Thaler and Sunstein's 'Nudge' concept, especially 'heuristics' (tendencies for humans to think and decide instinctively and often mistakenly) • relates 'Nudge' methods to other theories and models, and to Kahneman and Tversky's work • defines and describes additional methods of 'nudging' people and groups • extends the appreciation and application of 'Nudge' methodology to broader change- management, motivation, leadership, coaching, counselling, parenting, etc • offers 'Nudge' methods and related concepts as a 'Nudge' theory 'toolkit' so that the concept can be taught and applied in a wide range of situations involving relationships with people, and enabling people to improve their thinking and decision- making • and offers a glossary of Nudge theory and related terms 'Nudge' theory was proposed originally in US 'behavioral economics', but it can be adapted and applied much more widely for enabling and encouraging change in people, groups, or yourself. Nudge theory can also be used to explore, understand, and explain existing influences on how people behave, especially influences which are unhelpful, with a view to removing or altering them. -
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. -
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. -
CHALK TALK Thinking About Thinking: Medical Decision Making Under the Microscope Christiana Iyasere, MD, and Douglas Wright, MD, Phd
SGIM FORUM 2011; 34(11) CHALK TALK Thinking about Thinking: Medical Decision Making Under the Microscope Christiana Iyasere, MD, and Douglas Wright, MD, PhD Drs. Iyasere and Wright are faculty in the Inpatient Clinician Educator Service of the Department of Medicine at Massachusetts General Hospital in Boson, MA. ase: A 36-year-old African-Ameri- athletic—he graduated from college than 10 seconds and says “20,160” C can woman, healthy except for with a degree in physics and has before moving breezily along with treated hypothyroidism, visits you in completed several triathlons. Neil is a her coffee. Having Katrina’s input, are clinic complaining of six months of fa- veteran of the US Navy, where he you tempted to change your answer tigue and progressive shortness of served as fleet naval aviator and land- to questions 3a and 3b? Go ahead, breath with exertion. You thoroughly ing signal officer. Is Neil more likely to admit it. Aren’t you now more confi- interview and examine the patient. be: a) a librarian or b) an astronaut? dent that the correct answer is that Physical examination reveals conjunc- Question 2: Jot down a list of the product is closest to 20,000? tival pallor and dullness to percussion English words that begin with the let- Question 5: You have known one third of the way up both lung ter “r” (e.g. rooster). Next, jot down your medical school roommate Jus- fields. Something tells you to ask her a list of words that have an r in the tice for four years. -
Cognitive Biases Potentially Affecting Judgment of Global Risks Forthcoming in Global Catastrophic Risks, Eds
Cognitive biases potentially affecting judgment of global risks Forthcoming in Global Catastrophic Risks, eds. Nick Bostrom and Milan Cirkovic Draft of August 31, 2006. Eliezer Yudkowsky ([email protected]) Singularity Institute for Artificial Intelligence Palo Alto, CA Introduction1 All else being equal, not many people would prefer to destroy the world. Even faceless corporations, meddling governments, reckless scientists, and other agents of doom, require a world in which to achieve their goals of profit, order, tenure, or other villainies. If our extinction proceeds slowly enough to allow a moment of horrified realization, the doers of the deed will likely be quite taken aback on realizing that they have actually destroyed the world. Therefore I suggest that if the Earth is destroyed, it will probably be by mistake. The systematic experimental study of reproducible errors of human reasoning, and what these errors reveal about underlying mental processes, is known as the heuristics and biases program in cognitive psychology. This program has made discoveries highly relevant to assessors of global catastrophic risks. Suppose you're worried about the risk of Substance P, an explosive of planet-wrecking potency which will detonate if exposed to a strong radio signal. Luckily there's a famous expert who discovered Substance P, spent the last thirty years working with it, and knows it better than anyone else in the world. You call up the expert and ask how strong the radio signal has to be. The expert replies that the critical threshold is probably around 4,000 terawatts. "Probably?" you query. "Can you give me a 98% confidence interval?" "Sure," replies the expert. -
Heuristics & Biases Simplified
University of Pennsylvania ScholarlyCommons Master of Behavioral and Decision Sciences Capstones Behavioral and Decision Sciences Program 8-9-2019 Heuristics & Biases Simplified: Easy ot Understand One-Pagers that Use Plain Language & Examples to Simplify Human Judgement and Decision-Making Concepts Brittany Gullone University of Pennsylvania Follow this and additional works at: https://repository.upenn.edu/mbds Part of the Social and Behavioral Sciences Commons Gullone, Brittany, "Heuristics & Biases Simplified: Easy to Understand One-Pagers that Use Plain Language & Examples to Simplify Human Judgement and Decision-Making Concepts" (2019). Master of Behavioral and Decision Sciences Capstones. 7. https://repository.upenn.edu/mbds/7 The final capstone research project for the Master of Behavioral and Decision Sciences is an independent study experience. The paper is expected to: • Present a position that is unique, original and directly applies to the student's experience; • Use primary sources or apply to a primary organization/agency; • Conform to the style and format of excellent academic writing; • Analyze empirical research data that is collected by the student or that has already been collected; and • Allow the student to demonstrate the competencies gained in the master’s program. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/mbds/7 For more information, please contact [email protected]. Heuristics & Biases Simplified: Easy ot Understand One-Pagers that Use Plain Language & Examples to Simplify Human Judgement and Decision-Making Concepts Abstract Behavioral Science is a new and quickly growing field of study that has found ways of capturing readers’ attention across a variety of industries. The popularity of this field has led to a wealth of terms, concepts, and materials that describe human behavior and decision making. -
Anchoring Bias in Eliciting Attribute Weights and Values in Multi-Attribute Decision-Making
Delft University of Technology Anchoring bias in eliciting attribute weights and values in multi-attribute decision-making Rezaei, Jafar DOI 10.1080/12460125.2020.1840705 Publication date 2020 Document Version Final published version Published in Journal of Decision Systems Citation (APA) Rezaei, J. (2020). Anchoring bias in eliciting attribute weights and values in multi-attribute decision-making. Journal of Decision Systems, 30(1), 72-96. https://doi.org/10.1080/12460125.2020.1840705 Important note To cite this publication, please use the final published version (if applicable). Please check the document version above. Copyright Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons. Takedown policy Please contact us and provide details if you believe this document breaches copyrights. We will remove access to the work immediately and investigate your claim. This work is downloaded from Delft University of Technology. For technical reasons the number of authors shown on this cover page is limited to a maximum of 10. Journal of Decision Systems ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tjds20 Anchoring bias in eliciting attribute weights and values in multi-attribute decision-making Jafar Rezaei To cite this article: Jafar Rezaei (2020): Anchoring bias in eliciting attribute weights and values in multi-attribute decision-making, Journal of Decision Systems, DOI: 10.1080/12460125.2020.1840705 To link to this article: https://doi.org/10.1080/12460125.2020.1840705 © 2020 The Author(s). -
The Flushtration Count Illusion: Attribute Substitution Tricks Our Interpretation of a Simple Visual Event Sequence Cyril Thomas
The Flushtration Count Illusion: Attribute substitution tricks our interpretation of a simple visual event sequence Cyril Thomas1, André Didierjean2 and Gustav Kuhn1 1 Department of Psychology 2 Laboratoire de Psychologie & MSHE Goldsmiths University of London Université de Bourgogne-Franche-Comté, New Cross, London SE14 6NW 30 rue Mégevand, 25030 Besançon United Kingdom France Address for correspondence: Cyril Thomas, Department of Psychology, Goldsmiths University of London, New Cross, London SE14 6NW, United Kingdom E-mail: [email protected] Telephone number: +33 (0)3 81 66 51 92 Fax number: +33 (0)3 81 66 54 40 Abstract: When faced with a difficult question, people sometimes work out an answer to a related, easier question without realizing that a substitution has taken place (e.g., Kahneman; 2011). In two experiments, we investigated whether this attribute substitution effect can also affect the interpretation of a simple visual event sequence. We used a magic trick called the “Flushtration Count Illusion,” which involves a technique used by magicians to give the 1 illusion of having seen multiple cards with identical backs, when in fact only the back of one card (the bottom card) is repeatedly shown. In Experiment 1, we demonstrated that most participants are susceptible to the illusion, even if they have the visual and analytical reasoning capacity to correctly process the sequence. In Experiment 2, we demonstrated that participants construct a biased and simplified representation of the Flushtration Count by substituting some attributes of the event sequence. We discussed of the psychological processes underlying this attribute substitution effect. Keywords: Magic, Attribute Substitution Effect, Misdirection, Flushtration Count Illusion, Perception, reasoning, Dual process theory Word Count: 4992. -
A Disinformation-Misinformation Ecology: the Case of Trump Thomas J
Chapter A Disinformation-Misinformation Ecology: The Case of Trump Thomas J. Froehlich Abstract This paper lays out many of the factors that make disinformation or misinformation campaigns of Trump successful. By all rational standards, he is unfit for office, a compulsive liar, incompetent, arrogant, ignorant, mean, petty, and narcissistic. Yet his approval rating tends to remain at 40%. Why do rational assessments of his presidency fail to have any traction? This paper looks at the con- flation of knowledge and beliefs in partisan minds, how beliefs lead to self-decep- tion and social self-deception and how they reinforce one another. It then looks at psychological factors, conscious and unconscious, that predispose partisans to pursue partisan sources of information and reject non-partisan sources. It then explains how these factors sustain the variety and motivations of Trump supporters’ commitment to Trump. The role of cognitive authorities like Fox News and right-wing social media sites are examined to show how the power of these media sources escalates and reinforces partisan views and the rejection of other cognitive authorities. These cognitive authorities also use emotional triggers to inflame Trump supporters, keeping them addicted by feeding their anger, resentment, or self-righteousness. The paper concludes by discussing the dynamics of the Trump disinformation- misinformation ecology, creating an Age of Inflamed Grievances. Keywords: Trumpism, disinformation, cognitive authority, Fox News, social media, propaganda, inflamed grievances, psychology of disinformation, Donald Trump, media, self-deception, social self-deception 1. Introduction This paper investigates how disinformation-misinformation campaigns, particularly in the political arena, succeed and why they are so hard to challenge, defeat, or deflect. -
Wishful Thinking About the Future: Does Desire Impact Optimism? Zlatan Krizan1* and Paul D
Social and Personality Psychology Compass 3/3 (2009): 227–243, 10.1111/j.1751-9004.2009.00169.x WishfulBlackwellOxford,SPCOSocial1751-9004©Journal16910.1111/j.1751-9004.2009.00169.xMarch0227???243???Original 2009 and2009 Thinking UKTheCompilationArticle Publishing Personality Authors ©Ltd Psychology2009 Blackwell Compass Publishing Ltd. Wishful Thinking about the Future: Does Desire Impact Optimism? Zlatan Krizan1* and Paul D. Windschitl2 1 Iowa State University 2 University of Iowa Abstract The notion that desire for an outcome inflates optimism about that outcome has been dubbed the desirability bias or wishful thinking. In this paper, we discuss the importance of distinguishing wishful thinking from the more general concept of motivated reasoning, and we explain why documenting overoptimism or correla- tions between preferences and optimism is not sufficient to infer a desirability bias. Then, we discuss results from a review and meta-analysis of the experimental litera- ture on wishful thinking. These findings, in conjunction with more recent work, not only highlight important moderators and mediators of the desirability bias but also point out limitations of the empirical research on the bias. These results also reveal an important difference between how likelihood judgments and discrete outcome predictions respond to desirability of outcomes. We conclude by presenting avenues for future research useful for understanding wishful thinking’s manifesta- tion in everyday environments and its integration with related phenomena. —A soon-to-be bride and groom who hope the weather will cooperate on their wedding day. —An Illinois grade-schooler who thinks it would be ‘pretty cool’ if Chicago was indeed selected as the host city for the 2016 Olympics.