Vulnerabilities to Cognitive Biases in the OODA Loop Process Tom Rieger, President, NSI, Inc.1
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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, -
1 Session 17
42nd Annual National Conference of Regulatory Attorneys Nashville, Tennessee May 5-8, 2019 Outline & Materials Session 17 - Bridge Over Troubled Water: The Arch of Ethics It is easy to fall into ethically-troubled waters. Here, a series of lively skits will show us some of the daily challenges facing attorneys who practice before the fictitious Nirvana Public Utility Commission. The situations portrayed leave us to question: Will these lawyers slip into the muddy waters or steady themselves by clinging to the Model Rules of Professional Conduct? Legal Instruction: Richard Collier, Esq. Skit Production: Eve Moran, Esq. _________________________________________________________________________ Skit I - Things Are Jumping At The Bluebird Bar & Grill Resources: Ex Parte Statutes - Tennessee (Tenn. Code Ann. § 4-5-304) Model Rule 3.5 A lawyer shall not: (a) seek to influence a judge, juror, prospective juror or other official by means prohibited by law; (b) communicate ex parte with such a person during the proceeding unless authorized to do so by law or court order; (c) communicate with a juror or prospective juror after discharge of the jury if: (1) the communication is prohibited by law or court order; (2) the juror has made known to the lawyer a desire not to communicate; or (3) the communication involves misrepresentation, coercion, duress or harassment; or (d) engage in conduct intended to disrupt a tribunal. Model Rule 8.4 It is professional misconduct for a lawyer to: (a) violate or attempt to violate the Rules of Professional Conduct, -
Identifying Present Bias from the Timing of Choices∗
Identifying Present Bias from the Timing of Choices∗ Paul Heidhues Philipp Strack DICE Yale February 22, 2021 Abstract A (partially naive) quasi-hyperbolic discounter repeatedly chooses whether to com- plete a task. Her net benefits of task completion are drawn independently between periods from a time-invariant distribution. We show that the probability of complet- ing the task conditional on not having done so earlier increases towards the deadline. Conversely, we establish non-identifiability by proving that for any time-preference pa- rameters and any data set with such (weakly increasing) task-completion probabilities, there exists a stationary payoff distribution that rationalizes the agent's behavior if she is either sophisticated or fully naive. Additionally, we provide sharp partial identifica- tion for the case of observable continuation values. ∗We thank seminar and conference audiences, and especially Nageeb Ali, Ned Augenblick, Stefano DellaVigna, Drew Fudenberg, Ori Heffetz, Botond K}oszegi, Muriel Niederle, Philipp Schmidt-Dengler, Charles Sprenger, Dmitry Taubinsky, and Florian Zimmermann for insightful and encouraging comments. Part of the work on this paper was carried out while the authors visited briq, whose hospitality is gratefully acknowledged. Philipp Strack was supported by a Sloan fellowship. Electronic copy available at: https://ssrn.com/abstract=3386017 1 Introduction Intuition and evidence suggests that many individuals are present-biased (e.g. Frederick et al., 2002; Augenblick et al., 2015; Augenblick and Rabin, 2019). Building on work by Laibson (1997) and others, O'Donoghue and Rabin (1999, 2001) illustrate within the quasi-hyperbolic discounting model that present bias, especially in combination with a lack of understanding thereof, leads individuals to procrastinate unpleasant tasks and to precrastinate pleasant experiences. -
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 -
Retention and Transfer of Cognitive Bias Mitigation Interventions: a Systematic Literature Study
SYSTEMATIC REVIEW published: 12 August 2021 doi: 10.3389/fpsyg.2021.629354 Retention and Transfer of Cognitive Bias Mitigation Interventions: A Systematic Literature Study J.E. (Hans) Korteling, Jasmin Y. J. Gerritsma and Alexander Toet* Netherlands Organisation for Applied Scientific Research (TNO) Human Factors, Soesterberg, Netherlands Cognitive biases can adversely affect human judgment and decision making and should therefore preferably be mitigated, so that we can achieve our goals as effectively as possible. Hence, numerous bias mitigation interventions have been developed and evaluated. However, to be effective in practical situations beyond laboratory conditions, the bias mitigation effects of these interventions should be retained over time and should transfer across contexts. This systematic review provides an overview of the literature on retention and transfer of bias mitigation interventions. A systematic search yielded 52 studies that were eligible for screening. At the end of the selection process, only 12 peer-reviewed studies remained that adequately studied retention over a period of at least 14 days (all 12 studies) or transfer to different tasks and contexts (one study). Edited by: Eleven of the relevant studies investigated the effects of bias mitigation training using Rick Thomas, Georgia Institute of Technology, game- or video-based interventions. These 11 studies showed considerable overlap United States regarding the biases studied, kinds of interventions, and decision-making domains. Most Reviewed by: of them indicated that gaming interventions were effective after the retention interval and Elizabeth Veinott, that games were more effective than video interventions. The study that investigated Michigan Technological University, United States transfer of bias mitigation training (next to retention) found indications of transfer across Dan Diaper, contexts. -
Addressing Present Bias in Movie Recommender Systems and Beyond
Addressing Present Bias in Movie Recommender Systems and Beyond Kai Lukoffa aUniversity of Washington, Seattle, WA, USA Abstract Present bias leads people to choose smaller immediate rewards over larger rewards in the future. Recom- mender systems often reinforce present bias because they rely predominantly upon what people have done in the past to recommend what they should do in the future. How can recommender systems over- come this present bias to recommend items in ways that match with users’ aspirations? Our workshop position paper presents the motivation and design for a user study to address this question in the domain of movies. We plan to ask Netflix users to rate movies that they have watched in the past for thelong- term rewards that these movies provided (e.g., memorable or meaningful experiences). We will then evaluate how well long-term rewards can be predicted using existing data (e.g., movie critic ratings). We hope to receive feedback on this study design from other participants at the HUMANIZE workshop and spark conversations about ways to address present bias in recommender systems. Keywords present bias, cognitive bias, algorithmic bias, recommender systems, digital wellbeing, movies 1. Introduction such as Schindler’s List [2]. Recommender systems (RS), algorithmic People often select smaller immediate re- systems that predict the preference a user wards over larger rewards in the future, a would give to an item, often reinforce present phenomenon that is known as present bias bias. Today, the dominant paradigm of rec- or time discounting. This applies to deci- ommender systems is behaviorism: recom- sions such as what snack to eat [1,2], how mendations are selected based on behavior much to save for retirement [3], or which traces (“what users do”) and they largely ne- movies to watch [2]. -
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 -
Poverty and Decision-Making
Poverty and decision-making How behavioural science can improve opportunity in the UK Kizzy Gandy, Katy King, Pippa Streeter Hurle, Chloe Bustin and Kate Glazebrook October 2016 1 © Behavioural Insights Ltd Acknowledgements Many people contributed to this report. Particular thanks go to David Halpern, Andy Hollingsworth, Raj Chande, Tiina Likki, Ben Curtis and Susannah Hume from the Behavioural Insights Team who provided expert advice on the literature and policy context. Many people inside government also provided review, and several leading academic were an invaluable source of insights and knowledge, particularly Eldar Shafir, Jonathan Morduch and Evrim Altintas. Finally, we thank the Joseph Rowntree Foundation for providing useful guidance along the way and for their enthusiasm and support for our intellectual ambition. 2 © Behavioural Insights Ltd Contents Glossary of terms ............................................................................................. 6 Executive summary ............................................................................................ 7 Chapter 1 - Introduction: applying behavioural science to the study of poverty .... 11 Section 1.1 Background and principal audiences for the report .................. 11 Section 1.2 Report aims and research questions ....................................... 11 Section 1.3 The psychology of decision-making ........................................ 12 Section 1.4 Structure of the report .......................................................... 14 Chapter 2 -
Health Insurance & Behavioral Economics
Health Insurance & Behavioral Economics Alan C. Monheit, Ph.D. Rutgers University School of Public Health Rutgers Center for State Health Policy National Bureau of Economic Research Standard Model Underlying Consumer Choice • In the standard economic model of consumer choice, individuals are characterized as “. lightning quick calculators of pleasure and pain.” • Built on very strong behavioral assumptions: – Rationality – Full information on attributes of commodities and their substitutes. – Easy to assess the benefits & costs of consumption alternatives. – Individuals are constrained by a budget. – More choice is welfare enhancing. • These assumptions of rationality & full information carry over to the standard or benchmark model of health insurance choice. Center for State Health Policy Benchmark Model of Health Insurance Choice • Key assumptions: – Individuals are risk averse: • Prefer a loss with certainty rather than a gamble with the same expected loss. • Will incur a certain loss to obtain an uncertain flow of benefits. • Are willing to pay a “risk premium” above an actuarially fair insurance premium. – Can accurately assess the probability of future losses. – Can accurately assess income losses from adverse events. – Can accurately assess the benefits & costs of purchasing or not purchasing insurance. Center for State Health Policy Implications from the Standard Model • Value of the standard model is that it provides testable predictions about health insurance choices: – More risk averse individuals will purchase more insurance than those who are less risk averse. – Generally, as the probability of a loss increases, individuals will purchase more insurance. However, • Individuals will not purchase insurance for very large or very small probabilities of income loss. – More insurance is purchased as the expected income loss increases. -
Behavioural Economics for Investors
Behavioural economics for investors Guide CNMV Guide 1 Behavioural economics for investors Table of contents 3 5 Introduction Concept and basic foundations of behavioural economics 7 16 The decision-making process Phases of investment decision making 19 21 Mitigation of cognitive biases Mitigation strategies for that affect the investment cognitive biases decision process 31 Final recommendations CNMV Guide 2 Behavioural economics for investors Behavioural economics for investors I. Introduction Since the beginning, traditional economic theory has addressed the way in which people make their investment, saving and spending decisions. It is also based on the assumptions that people know what they want, use the available information to achieve their objectives and fully understand the risks and rewards of their financial decisions. In recent years, however, numerous discoveries from disciplines such as psychology, neurology or neurophysiology about how the human brain works have revealed that this is not the case. People often do not know what their preferences are, misuse available information and do not adequately understand the risks they take on. Behavioural economics studies real human behaviours in a real world to develop more precise and practical economic models than those provided by conventional economic theory. Behavioural economics takes into account those subtle and not-so-subtle factors that underlie financial decisions. CNMV Guide 3 Behavioural economics for investors This discipline is an attempt to analyse the patterns and biases of people’s behaviour and use these as a base to predict behaviour models. Research conducted in the field of behavioural economics shows that most of these patterns or biases are predictable. -
Trends and Issues
TRENDS AND ISSUES DECEMBER 2015 THE ROLE OF TIME PREFERENCES AND EXPONENTIAL- GROWTH BIAS IN RETIREMENT SAVINGS Gopi Shah Goda Matthew R. Levy Colleen Flaherty Aaron J. Sojourner Joshua Tasoff Stanford University London School of Manchester University of Minnesota Claremont Graduate and NBER Economics University of Minnesota and IZA University EXECUTIVE SUMMARY Understanding the drivers of retirement-wealth accumulation is critical given that changes in employer-provided retirement plans have shifted responsibility for retirement savings increasingly to individuals. This paper empirically investigates two factors – one cognitive and one motivational – that may lead to low levels of retirement wealth accumulation. Cognitively, individuals may underestimate the future value of saving today by neglecting compound interest when assessing returns to saving, which is known as exponential-growth bias (EG bias). The motivational factor is present bias, which is the tendency for individuals to act patient in the long term, but impatient in the near term such that they continually put off saving today. Using an online survey of a broad sample of US households, we measure individuals’ tendency for EG bias and present bias and relate them to individuals’ retirement savings. We find that both biases are prevalent and are negatively related to levels of retirement savings. In particular, 78% of the sample exhibits EG bias and 55% is present biased. However, having one bias does not increase the likelihood of having the other. As for retirement savings, individuals who have accurate understanding of exponential growth have $27,300 more in retirement savings relative to those who neglect exponential growth, while those who value time consistently have $25,200 more in savings relative to individuals who have present bias.