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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, -
Optimism Bias and Illusion of Control in Finance Professionals
November, 28th – P13 Look ma(rket), No Hands! Optimism Bias and Illusion of Control in Finance Professionals Francesco Marcatto, Giovanni Colangelo, Donatella Ferrante University of Trieste, Department of Life Sciences, Psychology Unit Gaetano Kanizsa, Trieste, Italy Abstract behavior, whereas extreme optimists display financial habits The optimism bias is the tendency to judge one’s own risk as and behavior that are generally not considered prudent (Puri less than the risk of others. In the present study we found that & Robinson, 2007). Moreover, analysts have been shown to also finance professionals (N = 60) displayed an optimism be systematically overoptimistic in their forecasts about the bias when forecasting the return of an investment made by earnings potential of firms at the time of their initial public themselves or by a colleague of the same expertise. Using a offerings, and this optimism increases as the length of the multidimensional approach to the assessment of risk forecast period increases (McNichols & O’Brien, 1997; perception, we found that participants’ forecasts were biased Rajan & Servaes, 1997). To the best of our knowledge, not because they judged negative consequences as less likely for themselves, but because they were overconfident in their however, the presence of the optimism bias/unrealistic ability to avoid and control them. comparative optimism in financial risk (i.e., judging personal risk of losing money as lower than other investors’ Keywords: Optimism bias; unrealistic comparative risk) has never been subject of empirical testing. optimism; financial risk; investment risk; perceived control; illusion of control. The main aim of this study is to investigate whether the optimism bias can be observed also in experts in the field of Introduction financial risk. -
Behavioral Biases on Investment Decision: a Case Study in Indonesia
Kartini KARTINI, Katiya NAHDA / Journal of Asian Finance, Economics and Business Vol 8 No 3 (2021) 1231–1240 1231 Print ISSN: 2288-4637 / Online ISSN 2288-4645 doi:10.13106/jafeb.2021.vol8.no3.1231 Behavioral Biases on Investment Decision: A Case Study in Indonesia Kartini KARTINI1, Katiya NAHDA2 Received: November 30, 2020 Revised: February 07, 2021 Accepted: February 16, 2021 Abstract A shift in perspective from standard finance to behavioral finance has taken place in the past two decades that explains how cognition and emotions are associated with financial decision making. This study aims to investigate the influence of various psychological factors on investment decision-making. The psychological factors that are investigated are differentiated into two aspects, cognitive and emotional aspects. From the cognitive aspect, we examine the influence of anchoring, representativeness, loss aversion, overconfidence, and optimism biases on investor decisions. Meanwhile, from the emotional aspect, the influence of herding behavior on investment decisions is analyzed. A quantitative approach is used based on a survey method and a snowball sampling that result in 165 questionnaires from individual investors in Yogyakarta. Further, we use the One-Sample t-test in testing all hypotheses. The research findings show that all of the variables, anchoring bias, representativeness bias, loss aversion bias, overconfidence bias, optimism bias, and herding behavior have a significant effect on investment decisions. This result emphasizes the influence of behavioral factors on investor’s decisions. It contributes to the existing literature in understanding the dynamics of investor’s behaviors and enhance the ability of investors in making more informed decision by reducing all potential biases. -
MITIGATING COGNITIVE BIASES in RISK IDENTIFICATION: Practitioner Checklist for the AEROSPACE SECTOR
MITIGATING COGNITIVE BIASES IN RISK IDENTIFICATION: Practitioner Checklist for the AEROSPACE SECTOR Debra L. Emmons, Thomas A. Mazzuchi, Shahram Sarkani, and Curtis E. Larsen This research contributes an operational checklist for mitigating cogni- tive biases in the aerospace sector risk management process. The Risk Identification and Evaluation Bias Reduction Checklist includes steps for grounding the risk identification and evaluation activities in past project experiences through historical data, and emphasizes the importance of incorporating multiple methods and perspectives to guard against optimism and a singular project instantiation-focused view. The authors developed a survey to elicit subject matter expert judgment on the value of the check- list to support its use in government and industry as a risk management tool. The survey also provided insights on bias mitigation strategies and lessons learned. This checklist addresses the deficiency in the literature in providing operational steps for the practitioner to recognize and implement strategies for bias reduction in risk management in the aerospace sector. DOI: https://doi.org/10.22594/dau.16-770.25.01 Keywords: Risk Management, Optimism Bias, Planning Fallacy, Cognitive Bias Reduction Mitigating Cognitive Biases in Risk Identification http://www.dau.mil January 2018 This article and its accompanying research contribute an operational FIGURE 1. RESEARCH APPROACH Risk Identification and Evaluation Bias Reduction Checklist for cognitive bias mitigation in risk management for the aerospace sector. The checklist Cognitive Biases & Bias described herein offers a practical and implementable project management Enabling framework to help reduce biases in the aerospace sector and redress the Conditions cognitive limitations in the risk identification and analysis process. -
“Dysrationalia” Among University Students: the Role of Cognitive
“Dysrationalia” among university students: The role of cognitive abilities, different aspects of rational thought and self-control in explaining epistemically suspect beliefs Erceg, Nikola; Galić, Zvonimir; Bubić, Andreja Source / Izvornik: Europe’s Journal of Psychology, 2019, 15, 159 - 175 Journal article, Published version Rad u časopisu, Objavljena verzija rada (izdavačev PDF) https://doi.org/10.5964/ejop.v15i1.1696 Permanent link / Trajna poveznica: https://urn.nsk.hr/urn:nbn:hr:131:942674 Rights / Prava: Attribution 4.0 International Download date / Datum preuzimanja: 2021-09-29 Repository / Repozitorij: ODRAZ - open repository of the University of Zagreb Faculty of Humanities and Social Sciences Europe's Journal of Psychology ejop.psychopen.eu | 1841-0413 Research Reports “Dysrationalia” Among University Students: The Role of Cognitive Abilities, Different Aspects of Rational Thought and Self-Control in Explaining Epistemically Suspect Beliefs Nikola Erceg* a, Zvonimir Galić a, Andreja Bubić b [a] Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia. [b] Department of Psychology, Faculty of Humanities and Social Sciences, University of Split, Split, Croatia. Abstract The aim of the study was to investigate the role that cognitive abilities, rational thinking abilities, cognitive styles and self-control play in explaining the endorsement of epistemically suspect beliefs among university students. A total of 159 students participated in the study. We found that different aspects of rational thought (i.e. rational thinking abilities and cognitive styles) and self-control, but not intelligence, significantly predicted the endorsement of epistemically suspect beliefs. Based on these findings, it may be suggested that intelligence and rational thinking, although related, represent two fundamentally different constructs. -
The Optimism Bias: a Cognitive Neuroscience Perspective 1
Xjenza Online - Journal of The Malta Chamber of Scientists www.xjenza.org Doi: http://dx.medra.org/10.7423/XJENZA.2014.1.04 Review Article The Optimism Bias: A cognitive neuroscience perspective Claude J. Bajada1 1Institute of Cognitive Neuroscience, University College London, WC1N 3AR, UK Abstract. The optimism bias is a well-established 2 The Study of the Optimism psychological phenomenon. Its study has implications that are far reaching in fields as diverse as mental Bias health and economic theory. With the emerging field of cognitive neuroscience and the advent of advanced neu- roimaging techniques, it has been possible to investigate the neural basis of the optimism bias and to understand One way for scientists to test the optimism bias in the in which neurological conditions this natural bias fails. laboratory is to ask an individual to predict his chances This review first defines the optimism bias, discusses its of experiencing an event and then following up to see implications and reviews the literature that investigates whether the event transpired. The problem with this its neural basis. Finally some potential pitfalls in approach is that the outcome of an event does not al- experimental design are discussed. ways accurately represent the person's prior chances to attain that outcome; this is especially true when the outcome is a binary one. For example, we know that an Keywords Optimism bias - cognitive neuroscience - individual has an infinitesimally small chance at winning psychology - neural basis. the national lottery. If Peter predicts that he has a 75% chance of winning next week's lottery and then happens to win the lottery, this does not mean that Peter was 1 Introduction actually pessimistic in his prediction. -
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. -
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. -
Fundamental Attribution Error Placebo E Ect Reactance Optimism Bias
availability curse of anchoring sunk cost fallacy heuristic knowledge The first thing you judge influences your You irrationally cling to things that have already Your judgments are influenced by what Once you understand something you presume judgment of all that follows. cost you something. springs most easily to mind. it to be obvious to everyone. Human minds are associative in nature, so the order in which we When we've invested our time, money, or emotion into something, it How recent, emotionally powerful, or unusual your memories are can Things makes sense once they make sense, so it can be hard to receive information helps determine the course of our judgments hurts us to let it go. This aversion to pain can distort our better make them seem more relevant. This, in turn, can cause you to apply remember why they didn't. We build complex networks of understanding and perceptions. judgment and cause us to make unwise investments. them too readily. and forget how intricate the path to our available knowledge really is. Be especially mindful of this bias during financial negotiations such To regain objectivity, ask yourself: had I not already invested Try to gain dierent perspectives and relevant statistical When teaching someone something new, go slow and explain like as houses, cars, and salaries. The initial price oered is proven to something, would I still do so now? What would I counsel a friend information rather than relying purely on first judgments and they're ten years old (without being patronizing). Repeat key points have a significant eect. -
Thinking and Reasoning
Thinking and Reasoning Thinking and Reasoning ■ An introduction to the psychology of reason, judgment and decision making Ken Manktelow First published 2012 British Library Cataloguing in Publication by Psychology Press Data 27 Church Road, Hove, East Sussex BN3 2FA A catalogue record for this book is available from the British Library Simultaneously published in the USA and Canada Library of Congress Cataloging in Publication by Psychology Press Data 711 Third Avenue, New York, NY 10017 Manktelow, K. I., 1952– Thinking and reasoning : an introduction [www.psypress.com] to the psychology of reason, Psychology Press is an imprint of the Taylor & judgment and decision making / Ken Francis Group, an informa business Manktelow. p. cm. © 2012 Psychology Press Includes bibliographical references and Typeset in Century Old Style and Futura by index. Refi neCatch Ltd, Bungay, Suffolk 1. Reasoning (Psychology) Cover design by Andrew Ward 2. Thought and thinking. 3. Cognition. 4. Decision making. All rights reserved. No part of this book may I. Title. be reprinted or reproduced or utilised in any BF442.M354 2012 form or by any electronic, mechanical, or 153.4'2--dc23 other means, now known or hereafter invented, including photocopying and 2011031284 recording, or in any information storage or retrieval system, without permission in writing ISBN: 978-1-84169-740-6 (hbk) from the publishers. ISBN: 978-1-84169-741-3 (pbk) Trademark notice : Product or corporate ISBN: 978-0-203-11546-6 (ebk) names may be trademarks or registered trademarks, and are used -
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). -
Croskerry MD, Phd, FRCP(Edin)
Clinical Decision Making + Strategies for Cognitive Debiasing Pat Croskerry MD, PhD, FRCP(Edin) International Association of Endodontists Scottsdale, Arizona June 2019 Financial Disclosures or other Conflicts of Interest None It is estimated that an American adult makes 35,000 decisions a day i.e. about 2200 each waking hour Sollisch J: The cure for decision fatigue. Wall Street Journal, 2016 Decision making ‘The most important decision we need to make in Life is how we are going to make decisions’ Professor Gigerenzer Is there a problem with the way we think and make decisions? 3 domains of decision making Patients Healthcare leadership Healthcare providers Patients Leading Medical Causes of Death in the US and their Preventability in 2000 Cause Total Preventability (%) Heart disease 710,760 46 Malignant neoplasms 553,091 66 Cerebrovascular 167,661 43 Chronic respiratory 122,009 76 Accidents 97,900 44 Diabetes mellitus 69,301 33 Acute respiratory 65,313 23 Suicide 29,350 100 Chronic Liver disease 26,552 75 Hypertension/renal 12,228 68 Assault (homicide) 16,765 100 All other 391,904 14 Keeney (2008) Healthcare leadership Campbell et al, 2017 Healthcare providers US deaths in 2013 • 611,105 Heart disease • 584,881 Cancer • 251,454 Medical error Medical error is the 3rd leading cause of death Estimated number of preventable hospital deaths due to diagnostic failure annually in the US 40,000 – 80,000 Leape, Berwick and Bates JAMA 2002 Diagnostic failure is the biggest problem in patient safety Newman-Toker, 2017 Sources of Diagnostic Failure The System 25% The Individual 75% Graber M, Gordon R, Franklin N.