Cognitive Biases: 15 More to Think About
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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 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 -
Communication Science to the Public
David M. Berube North Carolina State University ▪ HOW WE COMMUNICATE. In The Age of American Unreason, Jacoby posited that it trickled down from the top, fueled by faux-populist politicians striving to make themselves sound approachable rather than smart. (Jacoby, 2008). EX: The average length of a sound bite by a presidential candidate in 1968 was 42.3 seconds. Two decades later, it was 9.8 seconds. Today, it’s just a touch over seven seconds and well on its way to being supplanted by 140/280- character Twitter bursts. ▪ DATA FRAMING. ▪ When asked if they truly believe what scientists tell them, NEW ANTI- only 36 percent of respondents said yes. Just 12 percent expressed strong confidence in the press to accurately INTELLECTUALISM: report scientific findings. ▪ ROLE OF THE PUBLIC. A study by two Princeton University researchers, Martin TRENDS Gilens and Benjamin Page, released Fall 2014, tracked 1,800 U.S. policy changes between 1981 and 2002, and compared the outcome with the expressed preferences of median- income Americans, the affluent, business interests and powerful lobbies. They concluded that average citizens “have little or no independent influence” on policy in the U.S., while the rich and their hired mouthpieces routinely get their way. “The majority does not rule,” they wrote. ▪ Anti-intellectualism and suspicion (trends). ▪ Trump world – outsiders/insiders. ▪ Erasing/re-writing history – damnatio memoriae. ▪ False news. ▪ Infoxication (CC) and infobesity. ▪ Aggregators and managed reality. ▪ Affirmation and confirmation bias. ▪ Negotiating reality. ▪ New tribalism is mostly ideational not political. ▪ Unspoken – guns, birth control, sexual harassment, race… “The amount of technical information is doubling every two years. -
When Do Employees Perceive Their Skills to Be Firm-Specific?
r Academy of Management Journal 2016, Vol. 59, No. 3, 766–790. http://dx.doi.org/10.5465/amj.2014.0286 MICRO-FOUNDATIONS OF FIRM-SPECIFIC HUMAN CAPITAL: WHEN DO EMPLOYEES PERCEIVE THEIR SKILLS TO BE FIRM-SPECIFIC? JOSEPH RAFFIEE University of Southern California RUSSELL COFF University of Wisconsin-Madison Drawing on human capital theory, strategy scholars have emphasized firm-specific human capital as a source of sustained competitive advantage. In this study, we begin to unpack the micro-foundations of firm-specific human capital by theoretically and empirically exploring when employees perceive their skills to be firm-specific. We first develop theoretical arguments and hypotheses based on the extant strategy literature, which implicitly assumes information efficiency and unbiased perceptions of firm- specificity. We then relax these assumptions and develop alternative hypotheses rooted in the cognitive psychology literature, which highlights biases in human judg- ment. We test our hypotheses using two data sources from Korea and the United States. Surprisingly, our results support the hypotheses based on cognitive bias—a stark contrast to expectations embedded within the strategy literature. Specifically, we find organizational commitment and, to some extent, tenure are negatively related to employee perceptions of the firm-specificity. We also find that employer-provided on- the-job training is unrelated to perceived firm-specificity. These results suggest that firm-specific human capital, as perceived by employees, may drive behavior in ways unanticipated by existing theory—for example, with respect to investments in skills or turnover decisions. This, in turn, may challenge the assumed relationship between firm-specific human capital and sustained competitive advantage. -
Infographic I.10
The Digital Health Revolution: Leaving No One Behind The global AI in healthcare market is growing fast, with an expected increase from $4.9 billion in 2020 to $45.2 billion by 2026. There are new solutions introduced every day that address all areas: from clinical care and diagnosis, to remote patient monitoring to EHR support, and beyond. But, AI is still relatively new to the industry, and it can be difficult to determine which solutions can actually make a difference in care delivery and business operations. 59 Jan 2021 % of Americans believe returning Jan-June 2019 to pre-coronavirus life poses a risk to health and well being. 11 41 % % ...expect it will take at least 6 The pandemic has greatly increased the 65 months before things get number of US adults reporting depression % back to normal (updated April and/or anxiety.5 2021).4 Up to of consumers now interested in telehealth going forward. $250B 76 57% of providers view telehealth more of current US healthcare spend % favorably than they did before COVID-19.7 could potentially be virtualized.6 The dramatic increase in of Medicare primary care visits the conducted through 90% $3.5T telehealth has shown longevity, with rates in annual U.S. health expenditures are for people with chronic and mental health conditions. since April 2020 0.1 43.5 leveling off % % Most of these can be prevented by simple around 30%.8 lifestyle changes and regular health screenings9 Feb. 2020 Apr. 2020 OCCAM’S RAZOR • CONJUNCTION FALLACY • DELMORE EFFECT • LAW OF TRIVIALITY • COGNITIVE FLUENCY • BELIEF BIAS • INFORMATION BIAS Digital health ecosystems are transforming• AMBIGUITY BIAS • STATUS medicineQUO BIAS • SOCIAL COMPARISONfrom BIASa rea• DECOYctive EFFECT • REACTANCEdiscipline, • REVERSE PSYCHOLOGY • SYSTEM JUSTIFICATION • BACKFIRE EFFECT • ENDOWMENT EFFECT • PROCESSING DIFFICULTY EFFECT • PSEUDOCERTAINTY EFFECT • DISPOSITION becoming precise, preventive,EFFECT • ZERO-RISK personalized, BIAS • UNIT BIAS • IKEA EFFECT and • LOSS AVERSION participatory. -
John Collins, President, Forensic Foundations Group
On Bias in Forensic Science National Commission on Forensic Science – May 12, 2014 56-year-old Vatsala Thakkar was a doctor in India but took a job as a convenience store cashier to help pay family expenses. She was stabbed to death outside her store trying to thwart a theft in November 2008. Bloody Footwear Impression Bloody Tire Impression What was the threat? 1. We failed to ask ourselves if this was a footwear impression. 2. The appearance of the impression combined with the investigator’s interpretation created prejudice. The accuracy of our analysis became threatened by our prejudice. Types of Cognitive Bias Available at: http://en.wikipedia.org/wiki/List_of_cognitive_biases | Accessed on April 14, 2014 Anchoring or focalism Hindsight bias Pseudocertainty effect Illusory superiority Levels-of-processing effect Attentional bias Hostile media effect Reactance Ingroup bias List-length effect Availability heuristic Hot-hand fallacy Reactive devaluation Just-world phenomenon Misinformation effect Availability cascade Hyperbolic discounting Recency illusion Moral luck Modality effect Backfire effect Identifiable victim effect Restraint bias Naive cynicism Mood-congruent memory bias Bandwagon effect Illusion of control Rhyme as reason effect Naïve realism Next-in-line effect Base rate fallacy or base rate neglect Illusion of validity Risk compensation / Peltzman effect Outgroup homogeneity bias Part-list cueing effect Belief bias Illusory correlation Selective perception Projection bias Peak-end rule Bias blind spot Impact bias Semmelweis -
Survivorship Bias Mitigation in a Recidivism Prediction Tool
EasyChair Preprint № 4903 Survivorship Bias Mitigation in a Recidivism Prediction Tool Ninande Vermeer, Alexander Boer and Radboud Winkels EasyChair preprints are intended for rapid dissemination of research results and are integrated with the rest of EasyChair. January 15, 2021 SURVIVORSHIP BIAS MITIGATION IN A RECIDIVISM PREDICTION TOOL Ninande Vermeer / Alexander Boer / Radboud Winkels FNWI, University of Amsterdam, Netherlands; [email protected] KPMG, Amstelveen, Netherlands; [email protected] PPLE College, University of Amsterdam, Netherlands; [email protected] Keywords: survivorship bias, AI Fairness 360, bias mitigation, recidivism Abstract: Survivorship bias is the fallacy of focusing on entities that survived a certain selection process and overlooking the entities that did not. This common form of bias can lead to wrong conclu- sions. AI Fairness 360 is an open-source toolkit that can detect and handle bias using several mitigation techniques. However, what if the bias in the dataset is not bias, but rather a justified unbalance? Bias mitigation while the ‘bias’ is justified is undesirable, since it can have a serious negative impact on the performance of a prediction tool based on machine learning. In order to make well-informed product design decisions, it would be appealing to be able to run simulations of bias mitigation in several situations to explore its impact. This paper de- scribes the first results in creating such a tool for a recidivism prediction tool. The main con- tribution is an indication of the challenges that come with the creation of such a simulation tool, specifically a realistic dataset. 1. Introduction The substitution of human decision making with Artificial Intelligence (AI) technologies increases the depend- ence on trustworthy datasets. -
Common Cognitive Biases
Common Cognitive Biases Anchoring The first number or piece of information given acts as an anchor for all subsequent numbers or information. Once an anchor is set, we quickly make other judgments by adjusting away from that anchor. For example, we tend to pay more for something if the first number we see is much higher. We compare other prices to the initial figure, which makes the price we are prepared to pay look more reasonable. Anticipation of An a priori anticipation of possible regret can Regret influence decision making. When we are facing a decision, we may anticipate the regret that may arise from a negative outcome. This bias is linked to Risk Aversion. Authority Bias We are more easily influenced or convinced by figures of authority figures. A good example of this is when toothpaste is always presented by a dentist or expert wearing a lab coat. We are influenced both by real experts and also by those just appearing to be in authority. ãChequered Leopard Ltd 1 Choice Overload When we are given too many choices, we either prefer the default option or we avoid making any decision at all. As the number of options increases, our level of certainty about our choice decreases. Additionally, the anticipation that we will regret our choice increases.” When we are tired, we tend to conserve our energy by making choices based on a single factor like price, for example, rather than considering all the other determinants that go into making the best decision. Clustering Illusion We are naturally pattern-seeking because it helps speed up the process recognising people and things. -
Perioperative Management of ACE Inhibitor Therapy: Challenges of Clinical Decision Making Based on Surrogate Endpoints
EDITORIAL Perioperative Management of ACE Inhibitor Therapy: Challenges of Clinical Decision Making Based on Surrogate Endpoints Duminda N. Wijeysundera MD, PhD, FRCPC1,2,3* 1Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Ontario, Canada; 2Department of Anesthesia and Pain Management, Toronto General Hospital, Toronto, Ontario, Canada; 3Department of Anesthesia, University of Toronto, Toronto, Ontario, Canada. enin-angiotensin inhibitors, which include angio- major surgery can have adverse effects. While ACE inhibitor tensin-converting enzyme (ACE) inhibitors and an- withdrawal has not shown adverse physiological effects in the giotensin II receptor blockers (ARBs), have demon- perioperative setting, it has led to rebound myocardial isch- strated benefits in the treatment of several common emia in patients with prior myocardial infarction.6 Rcardiovascular and renal conditions. For example, they are Given this controversy, there is variation across hospitals1 prescribed to individuals with hypertension, heart failure with and practice guidelines with respect to perioperative manage- reduced ejection fraction (HFrEF), prior myocardial infarction, ment of renin-angiotensin inhibitors. For example, the 2017 and chronic kidney disease with proteinuria. Perhaps unsur- Canadian Cardiovascular Society guidelines recommend that prisingly, many individuals presenting for surgery are already renin-angiotensin inhibitors be stopped temporarily 24 hours on long-term ACE inhibitor or ARB therapy. For example, such before major inpatient -
1 Embrace Your Cognitive Bias
1 Embrace Your Cognitive Bias http://blog.beaufortes.com/2007/06/embrace-your-co.html Cognitive Biases are distortions in the way humans see things in comparison to the purely logical way that mathematics, economics, and yes even project management would have us look at things. The problem is not that we have them… most of them are wired deep into our brains following millions of years of evolution. The problem is that we don’t know about them, and consequently don’t take them into account when we have to make important decisions. (This area is so important that Daniel Kahneman won a Nobel Prize in 2002 for work tying non-rational decision making, and cognitive bias, to mainstream economics) People don’t behave rationally, they have emotions, they can be inspired, they have cognitive bias! Tying that into how we run projects (project leadership as a compliment to project management) can produce results you wouldn’t believe. You have to know about them to guard against them, or use them (but that’s another article)... So let’s get more specific. After the jump, let me show you a great list of cognitive biases. I’ll bet that there are at least a few that you haven’t heard of before! Decision making and behavioral biases Bandwagon effect — the tendency to do (or believe) things because many other people do (or believe) the same. Related to groupthink, herd behaviour, and manias. Bias blind spot — the tendency not to compensate for one’s own cognitive biases. Choice-supportive bias — the tendency to remember one’s choices as better than they actually were. -
Common Types of Biases and How to Mitigate Them Appendix to “Unconscious Biases Impacting Your Business” by Omar Khan, Senior Manager
Common Types of Biases and How to Mitigate Them Appendix to “Unconscious Biases Impacting Your Business” by Omar Khan, Senior Manager Anchoring Problem: Anchoring is a well-known bias where an initial piece of information can change how we perceive something. We often see it exploited in negotiations and marketing. Anchors can be completely arbitrary (e.g., a unrelated number like your SSN) or just a decoy choice (also known as a dominated option, click here for an example starting just after the 11 minute mark). Example: The exploitation of anchoring happens all around us all the time as described in the video referenced above. In the business world, anchoring often presents itself in business cases where we are influenced by things that aren’t related to the actual decision needing to be made, like if you’re making a choice between two pieces of software and there is a reference to a third piece of software that is not being considered (effectively a decoy option). Similarly, there are a lot of things you never pay list price for, but vendors will be quick to include this theoretical cost on the invoice (for example, showing you that the list price value of the software you’re getting is $1,000,000, but you’re also getting a discount of $900,000, so you only pay 1/10 the list price). In these cases, the realness of the list price needs to be considered, as does the contractual language, for renewal purposes. On performance reviews, it is easy for employees to self-assess to the highest rating possible and thus influence you into limiting your assessment into the highest rating and the rating just below that as options. -
Assessing SRI Fund Performance Research: Best Practices in Empirical Analysis
Sustainable Development Sust. Dev. (2011) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/sd.509 Assessing SRI Fund Performance Research: Best Practices in Empirical Analysis Andrea Chegut,1* Hans Schenk2 and Bert Scholtens3 1 Department of Finance, Maastricht University, Maastricht, The Netherlands 2 Department of Economics, Utrecht University, Utrecht, The Netherlands 3 Department of Economics, Econometrics and Finance, University of Groningen, Groningen, The Netherlands ABSTRACT We review the socially responsible investment (SRI) mutual fund performance literature to provide best practices in SRI performance attribution analysis. Based on meta-ethnography and content analysis, five themes in this literature require specific attention: data quality, social responsibility verification, survivorship bias, benchmarking, and sensitivity and robustness checks. For each of these themes, we develop best practices. Specifically, for sound SRI fund performance analysis, it is important that research pays attention to divi- dend yields and fees, incorporates independent and third party social responsibility verifica- tion, corrects for survivorship bias and tests multiple benchmarks, as well as analyzing the impact of fund composition, management influences and SRI strategies through sensitivity and robustness analysis. These best practices aim to enhance the robustness of SRI finan- cial performance analysis. Copyright © 2011 John Wiley & Sons, Ltd and ERP Environment. Received 1 September 2009; revised 2 December 2009; accepted 4 January 2010 Keywords: mutual funds; socially responsible investing; performance evaluation; best practices Introduction N THIS PAPER, WE INVESTIGATE PERFORMANCE ATTRIBUTION ANALYSIS WITH RESPECT TO SOCIALLY RESPONSIBLE investment (SRI). This analysis is relevant in the decision making process of financial institutions in construct- ing and offering SRI portfolios.