Common Cognitive Biases
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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. -
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 -
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. -
Superstition in the Machine
Superstition in the Machine Alexander Riegler Center Leo Apostel, Vrije Universiteit Brussel Krijgskundestr. 33, B-1160 Brussels, Belgium [email protected] Abstract. It seems characteristic for humans to detect structural patterns in the world to anticipate future states. Therefore, scientific and common sense cogni- tion could be described as information processing which infers rule-like laws from patterns in data-sets. Since information processing is the domain of com- puters, artificial cognitive systems are generally designed as pattern discoverers. This paper questions the validity of the information processing paradigm as an explanation for human cognition and a design principle for artificial cogni- tive systems. Firstly, it is known from the literature that people suffer from conditions such as information overload, superstition, and mental disorders. Secondly, cognitive limitations such as a small short-term memory, the set- effect, the illusion of explanatory depth, etc. raise doubts as to whether human information processing is able to cope with the enormous complexity of an infi- nitely rich (amorphous) world. It is suggested that, under normal conditions, humans construct information rather than process it. The constructed information contains anticipations which need to be met. This can be hardly called information processing, since patterns from the “outside” are not used to produce action but rather to either justify an- ticipations or restructure the cognitive apparatus. When it fails, cognition switches to pattern processing, which, given the amorphous nature of the experiential world, is a lost cause if these patterns and inferred rules do not lead to a (partial) reorganisation of internal structures such that constructed anticipations can be met again. -
Cognitive Biases: 15 More to Think About
Tribunals, Autumn 2016 The JAC is not complacent and recognises there is more to do to increase judicial diversity. In 2015–16, 9% of JAC selections were BAME individuals; we want to improve that. We also want to see more candidates applying from different professional backgrounds, such as academia and the public sector. Diversity is improving across the judiciary with faster progress in some areas than others. Last year, 45% of the JAC’s recommended candidates were women. Recent statistics published by the Judicial Office show that 46% of tribunal judges are women and 12% are BAME. They also showed that the younger cohorts of judges are more diverse, a positive indicator for the future. We want the judiciary to reflect the society it serves and will continue to work with the government, judiciary and legal profession to ensure further progress is made. lori Frecker is Head of Equality and Diversity at the JAC Back to contents Cognitive biases: 15 more to think about DECISION MAKING By leslie Cuthbert In Lydia Seymour’s article in the Spring 2014 edition of Tribunals, she explained about a key unconscious bias known as ‘confrmation bias’. In the Autumn 2015 edition, I then described about the risks involved in being overconfdent. However, these are only two of the many cognitive biases that exist. Here are 15 other common cognitive, or unconscious, biases that we are all prone to falling foul of whether as witness, party or decision-maker. 1) Anchoring. This involves people being over-reliant on the frst piece of information that they receive. -
Copyrighted Material
Index academic style 97–102 critical reading 112–115 ad hominem fallacy 13–14 critical thinking, defi nitions advertising 28–31 of 2–3 apophenia 51,52 appeal to questionable authority data mining 47 15–16 depth, in essays 103 argument, defi ned 1–2 Dewey, Paul 2 argument from ignorance 10 discourse analysis 65 argumentum ad hominem – see ad domino eff ect 7 hominem fallacy argumentum ad ignorantiam – see externality 109–110 argument from ignorance argumentum ad verecundiam – fallacy 5 see appeal to questionable false binary opposition 12–13 authority feedback 94–106 arts, the 22–28 fringe science 58 astrology 59–61 availability heuristic 74–75 gambler’s fallacy 51, 53–54 graphology 61–64 begging the question 14–15 Grice’s Maxims 19, 21–22 blind/double-blind procedures 67 British Crime Survey 72 hyperbole 20–21 burden of proof, shifting 11 Bush, George W. 12 impressionists, the 24 butterfl y eff ect 7 jargon 55–56 correlation 66,COPYRIGHTED 69 MATERIAL clinical signifi cance 70–71 libel 31 clustering illusion 51, 53 complementary and alternative Markov chain 8 medicine 75–79 mindfulness 109, 110–112 confi rmation bias 75 control groups 66–67 Pareidolia 51, 52 crime statistics 71–73 Paul, Richard 3 bbindex.inddindex.indd 113535 112/03/122/03/12 77:46:46 AAMM 136 INDEX phrenology 64–65 refl ection 43–44 propositional logic 5 regression 69 protoscience 54, 57–58 research methods and statistics 65–73 non sequitur 6 rhetoric 20 Nostradamus 46–48 slippery slope 6–7 observational methods 67–68 snowball eff ect 7 Occam’s Razor 35–36, 38, 85 Socratic -
Pareidolia - Wikipedia, the Free Encyclopedia
Pareidolia - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Pareidolia Pareidolia From Wikipedia, the free encyclopedia Pareidolia (/pærɨˈdoʊliə/ parr-i-DOH-lee-ə) is a psychological phenomenon involving a stimulus (an image or a sound) wherein the mind perceives a familiar pattern where none actually exists. Common examples are perceived images of animals, faces, or objects in cloud formations, the "man in the moon", the "moon rabbit", and hidden messages within recorded music played in reverse or at higher- or lower-than-normal speeds. A satellite photo of a mesa Pareidolia is the visual or auditory form of in Cydonia, often called apophenia, which is the perception of patterns the Face on Mars. Later within random data. Combined with apophenia imagery from other angles and hierophany (manifestation of the sacred), did not show the illusion. pareidolia may have helped ancient societies organize chaos and make the world intelligible. [1][2] Contents 1 Etymology 2 Explanations 3 Mimetoliths 4 Projective tests 5 Art 6 Religious 7 Computer vision 8 Related phenomena 9 Examples 10 See also 11 References 12 External links Etymology The word is derived from the Greek words para (παρά, "beside, alongside, instead [of]", in this context meaning something faulty or wrong) and the noun eidōlon (εἴδωλον "image, form, shape", the diminutive of eidos). 1 sur 8 07/10/2015 17:07 Pareidolia - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Pareidolia Explanations Pareidolia can cause people to interpret random images, or patterns of light and shadow, as faces.[3]A 2009 magnetoencephalography study found that objects perceived as faces evoke an early (165 ms) activation of the fusiform face area at a time and location similar to that evoked by faces — whereas other common objects do not evoke such activation. -
COGNITIVE BIASES in INFORMATION SECURITY CAUSES, EXAMPLES and MITIGATION by Veselin Monev, Information Security and Compliance Practitioner
COGNITIVE BIASES IN INFORMATION SECURITY COGNITIVE BIASES IN INFORMATION SECURITY CAUSES, EXAMPLES AND MITIGATION By Veselin Monev, information security and compliance practitioner Abstract This article makes a contribution to the theory of the human factor in the information security by exploring how errors in thinking distort the perceptions of InfoSec issues. Besides examples from the practice, the author proposes several ideas for mitigating the negative effects of the cognitive biases through training. Keywords Information, security, bias, psychology, determinant, causes, mitigation, cognitive, training NTRODUCTION IOne of the components of a mature information information security issues in their organisations. security program is the human factor. Typically, the The key word here is “actionable”. Their experience emphasis is on maintaining a security awareness shows that professional analysis, argumentation program and mitigating risks caused by human techniques and even supporting evidence combined mistakes and lack of knowledge of security. may be insufficient for properly addressing some Security awareness is necessary but also only one of the identified problems. Although a number of aspect of the human factor. Another challenge for difficulties can be noted as causes for insufficient or security professionals is finding actionable arguments inadequate actions on information security matters, to support their analysis and recommendations on like deficiency of budget, time or human resources, cybersecurity-review.com 1 COGNITIVE BIASES IN INFORMATION SECURITY management ignorance and so forth, the picture wisdom2. This complex processing chain may be would be incomplete if the psychological phenomenon impacted by the misperception or misinterpretation of cognitive biases are of random data or events. As excluded. an example, a data leakage The cognitive biases are prevention (DLP) analyst, inherent characteristics of the tasked to inspect the DLP human nature and this way reports for irregularities, may part of everyone’s thinking.