A Language to Discuss Biases

Total Page:16

File Type:pdf, Size:1020Kb

A Language to Discuss Biases Pattern-recognition biases A language lead us to recognize patterns even where there are none. to discuss biases Confirmation bias. The over- Power of storytelling. The Psychologists and behavioral economists have identified dozens of cognitive weighting of evidence consistent with tendency to remember and to believe biases. The typology we present here is not meant to be exhaustive but rather a favored belief, underweighting more easily a set of facts when they to focus on those biases that occur most frequently and that have the largest of evidence against a favored belief, are presented as part of a coherent impact on business decisions. As these groupings make clear, one of the insidious or failure to search impartially for story. things about cognitive biases is their close relationship with the rules of thumb evidence. and mind-sets that often serve managers well. For example, many a seasoned Champion bias. The tendency executive rightly prides herself on pattern-recognition skills cultivated over Management by example. to evaluate a plan or proposal based This bias typology was the years. Similarly, seeking consensus when making a decision is often not a Generalizing based on examples that on the track record of the person prepared by Dan Lovallo failing but a condition of success. And valuing stability rather than “rocking are particularly recent or memorable. presenting it, more than on the facts and Olivier Sibony. the boat” or “fixing what ain’t broke” is a sound management precept. supporting it. False analogies—especially, misleading experiences. Action-oriented biases Relying on comparisons with situations drive us to take action less thoughtfully than we should. that are not directly comparable. Excessive optimism. The tendency Overconfidence. Overestimating for people to be overoptimistic our skill level relative to others’, leading Stability biases about the outcome of planned actions, us to overestimate our ability to create a tendency toward inertia in the presence of uncertainty. to overestimate the likelihood of affect future outcomes, take credit for positive events, and to underestimate past outcomes, and neglect the role the likelihood of negative ones. of chance. Anchoring and insufficient Sunk-cost fallacy. Paying adjustment. Rooting oneself to an attention to historical costs that are Competitor neglect. The tendency initial value, leading to insufficient not recoverable when considering to plan without factoring in competi- adjustments of subsequent estimates. future courses of action. tive responses, as if one is playing tennis against a wall, not a live opponent. Loss aversion. The tendency to feel Status quo bias. Preference losses more acutely than gains of for the status quo in the absence of Interest biases the same amount, making us more risk- pressure to change it. arise in the presence of conflicting incentives, including nonmonetary averse than a rational calculation and even purely emotional ones. would suggest. Misaligned individual Inappropriate attachments. Social biases incentives. Incentives for individuals Emotional attachment of individuals arise from the preference for harmony over conflict. in organizations to adopt views or to people or elements of the business to seek outcomes favorable to their unit (such as legacy products or brands), or themselves, at the expense of creating a misalignment of interests.1 Groupthink. Striving for consensus Sunflower management. the overall interest of the company. at the cost of a realistic appraisal of Tendency for groups to align with These self-serving views are often held Misaligned perception of alternative courses of action. the views of their leaders, genuinely, not cynically. corporate goals. Disagreements whether expressed or assumed. (often unspoken) about the hierarchy or relative weight of objectives pursued by the organization and about the trade- offs between them. To listen to the authors narrate a more comprehensive presentation of 1 Sydney Finkelstein, Jo Whitehead, and Andrew Campbell, Think Again: Why Good these biases and the ways they can combine to create dysfunctional patterns Leaders Make Bad Decisions and How to Keep It fromHappening to You, Boston: Harvard in corporate cultures, visit mckinseyquarterly.com. Business Press, 2008..
Recommended publications
  • The Status Quo Bias and Decisions to Withdraw Life-Sustaining Treatment
    HUMANITIES | MEDICINE AND SOCIETY The status quo bias and decisions to withdraw life-sustaining treatment n Cite as: CMAJ 2018 March 5;190:E265-7. doi: 10.1503/cmaj.171005 t’s not uncommon for physicians and impasse. One factor that hasn’t been host of psychological phenomena that surrogate decision-makers to disagree studied yet is the role that cognitive cause people to make irrational deci- about life-sustaining treatment for biases might play in surrogate decision- sions, referred to as “cognitive biases.” Iincapacitated patients. Several studies making regarding withdrawal of life- One cognitive bias that is particularly show physicians perceive that nonbenefi- sustaining treatment. Understanding the worth exploring in the context of surrogate cial treatment is provided quite frequently role that these biases might play may decisions regarding life-sustaining treat- in their intensive care units. Palda and col- help improve communication between ment is the status quo bias. This bias, a leagues,1 for example, found that 87% of clinicians and surrogates when these con- decision-maker’s preference for the cur- physicians believed that futile treatment flicts arise. rent state of affairs,3 has been shown to had been provided in their ICU within the influence decision-making in a wide array previous year. (The authors in this study Status quo bias of contexts. For example, it has been cited equated “futile” with “nonbeneficial,” The classic model of human decision- as a mechanism to explain patient inertia defined as a treatment “that offers no rea- making is the rational choice or “rational (why patients have difficulty changing sonable hope of recovery or improvement, actor” model, the view that human beings their behaviour to improve their health), or because the patient is permanently will choose the option that has the best low organ-donation rates, low retirement- unable to experience any benefit.”) chance of satisfying their preferences.
    [Show full text]
  • Ambiguity Seeking As a Result of the Status Quo Bias
    Ambiguity Seeking as a Result of the Status Quo Bias Mercè Roca 1, Robin M. Hogarth 2, A. John Maule 3 1ESRC Postgraduate Researcher, Centre for Decision Research, Leeds University Business School, The University of Leeds, Leeds, LS2 9JT, United Kingdom 2ICREA Research Professor, Departament d’Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 08005, Barcelona, Spain 3Professor, Centre for Decision Research, Leeds University Business School, The University of Leeds, Leeds, LS2 9JT, United Kingdom Contact details: Mercè Roca, [email protected] , Telephone: (0044)07709557423. Leeds University Business School, Maurice Keyworth Building. The University of Leeds, Leeds, LS2 9JT, United Kingdom * Funds for the experiments were provided by the Centre for Decision Research of Leeds University Business School. The authors are grateful for the comments received from Gaëlle Villejoubert, Albert Satorra and Peter Wakker. This paper was presented at SPUDM20 in Stockholm. 1 Abstract Several factors affect attitudes toward ambiguity. What happens, however, when people are asked to exchange an ambiguous alternative in their possession for an unambiguous one? We present three experiments in which individuals preferred to retain the former. This status quo bias emerged both within- and between-subjects, with and without incentives, with different outcome distributions, and with endowments determined by both the experimenter and the participants themselves. Findings emphasize the need to account for the frames of reference under which evaluations of probabilistic information take place as well as modifications that should be incorporated into descriptive models of decision making. Keywords Ambiguity, risk, status quo bias, decision making, uncertainty. JEL code: C91, D81. 2 The phenomenon of ambiguity aversion – or the preference for gambles with known as opposed to unknown probabilities – has been well documented in the literature on decision making in both psychology and economics (see, e.g., Ellsberg, 1961; Camerer & Weber, 1992; Keren & Gerritsen, 1999).
    [Show full text]
  • Gender Bias in Sexual Assault Response And
    End Violence Against Women International (EVAWI) Gender Bias in Sexual Assault Response and Investigation Part 1: Implicit Gender Bias Heather Huhtanen Contributions by Kimberly A. Lonsway, PhD Sergeant Joanne Archambault (Ret.) November 2017 Updated October 2020 . This project is supported by Grant No. 2016-TA-AX-K010 awarded by the Office on Violence Against Women, US Department of Justice. The opinions, findings, conclusions, and recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the Department of Justice, Office on Violence Against Women. Gender Bias in Sexual Assault Response and Investigation October Part 1: Implicit Gender Bias Huhtanen 2020 Public Domain Notice Unless something is excerpted directly from a copyrighted source, all the material in this document is in the public domain and may be reproduced or copied without specifically requesting permission from End Violence Against Women International (EVAWI) or the authors. Any direct quotes or excerpts should be properly cited, however. No one may reproduce or distribute this material for a fee without the specific, written authorization of End Violence Against Women International (EVAWI). Electronic Access The publication may be downloaded from End Violence Against Women International’s Resource Library. Recommended Citation Huhtanen, H. (2020). Gender Bias in Sexual Assault Response and Investigation. Part 1: Implicit Gender Bias. End Violence Against Women International. End Violence Against Women International 2 www.evawintl.org Gender Bias in Sexual Assault Response and Investigation October Part 1: Implicit Gender Bias Huhtanen 2020 Authors Heather Huhtanen is currently based in Geneva, Switzerland where she works to promote gender equality in the context of international development, security and justice reform and peace and transition processes.
    [Show full text]
  • •Understanding Bias: a Resource Guide
    Community Relations Services Toolkit for Policing Understanding Bias: A Resource Guide Bias Policing Overview and Resource Guide The Community Relations Service (CRS) is the U.S. Justice Department’s “peacemaker” agency, whose mission is to help resolve tensions in communities across the nation, arising from differences of race, color, national origin, gender, gender identity, sexual orientation, religion, and disability. CRS may be called to help a city or town resolve tensions that stem from community perceptions of bias or a lack of cultural competency among police officers. Bias and a lack of cultural competency are often cited interchangeably as challenges in police- community relationships. While bias and a lack of cultural competency may both be present in a given situation, these challenges and the strategies for addressing them differ appreciably. This resource guide will assist readers in understanding and addressing both of these issues. What is bias, and how is it different from cultural competency? The Science of Bias Bias is a human trait resulting from our tendency and need to classify individuals into categories as we strive to quickly process information and make sense of the world.1 To a large extent, these processes occur below the level of consciousness. This “unconscious” classification of people occurs through schemas, or “mental maps,” developed from life experiences to aid in “automatic processing.”2 Automatic processing occurs with tasks that are very well practiced; very few mental resources and little conscious thought are involved during automatic processing, allowing numerous tasks to be carried out simultaneously.3 These schemas become templates that we use when we are faced with 1.
    [Show full text]
  • 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.
    [Show full text]
  • Cognitive Bias in Emissions Trading
    sustainability Article Cognitive Bias in Emissions Trading Jae-Do Song 1 and Young-Hwan Ahn 2,* 1 College of Business Administration, Chonnam National University, Gwangju 61186, Korea; [email protected] 2 Korea Energy Economics Institute, 405-11 Jongga-ro, Jung-gu, Ulsan 44543, Korea * Correspondence: [email protected]; Tel.: +82-52-714-2175; Fax: +82-52-714-2026 Received: 8 February 2019; Accepted: 27 February 2019; Published: 5 March 2019 Abstract: This study investigates whether cognitive biases such as the endowment effect and status quo bias occur in emissions trading. Such cognitive biases can serve as a barrier to trade. This study’s survey-based experiments, which include hypothetical emissions trading scenarios, show that the endowment effect does occur in emissions trading. The status quo bias occurs in only one of the three experiments. This study also investigates whether accumulation of experience can reduce cognitive bias as discovered preference hypothesis expects. The results indicate that practitioners who are supposed to have more experience show no evidence of having less cognitive bias. Contrary to the conventional expectation, the practitioners show significantly higher level of endowment effect than students and only the practitioners show a significant status quo bias. A consignment auction situation, which is used in California’s cap-and-trade program, is also tested; no significant difference between general permission trading and consignment auctions is found. Keywords: emissions trading; cognitive bias; consignment auction; climate policy 1. Introduction Emissions trading allows entities to achieve emission reduction targets in a cost-effective way through buying and selling emission allowances in emissions trading markets [1,2].
    [Show full text]
  • Sexism at Work: How Can We Stop It? Handbook for the EU Institutions and Agencies Acknowledgements
    Sexism at work: how can we stop it? Handbook for the EU institutions and agencies Acknowledgements This handbook was developed by the European The personal stories were collected in person Institute for Gender Equality (EIGE). It was or in writing during the drafting of the hand- adapted from JUMP’s handbook Libérez votre book. They include experiences from the EU entreprise du sexisme* by Dorothy Dalton, who institutions and agencies, as well as from bod- was contracted as an External Expert. EIGE co- ies that work closely with these organisations. ordinated the work and provided quality assur- Stories were collected by the External Expert ance (Veronica Collins, Valentina Canepa, Vasiliki during workshops and coaching sessions, as Saini). well as through conversations. All stories are On 20 September 2019, EIGE held an expert real, though some names have been changed meeting in Vilnius, Lithuania, to receive com- to protect identities. EIGE would like to thank ments on a draft of the handbook. The meeting the Directorate-General (DG) for Justice and was attended by EU institutions and agencies, Consumers and DG Human Resources and non-governmental organisations and the pri- Security of the European Commission for their vate sector. The final version of this handbook valuable input, as well as Dr Sonja Robnik, mem- reflects the input of these organisations. ber of EIGE’s Expert Forum. The European Institute for Gender Equality The European Institute for Gender Equality Email: [email protected] (EIGE) is an autonomous body of the European Union established to strengthen gender Tel. +370 52157444 equality across the EU.
    [Show full text]
  • Bias, Employment Discrimination, and Black Women's Hair: Another Way Forward
    BYU Law Review Volume 2018 Issue 4 Article 7 Winter 2-28-2019 Bias, Employment Discrimination, and Black Women's Hair: Another Way Forward Crystal Powell Follow this and additional works at: https://digitalcommons.law.byu.edu/lawreview Part of the Civil Rights and Discrimination Commons, and the Labor and Employment Law Commons Recommended Citation Crystal Powell, Bias, Employment Discrimination, and Black Women's Hair: Another Way Forward, 2018 BYU L. Rev. 933 (2019). Available at: https://digitalcommons.law.byu.edu/lawreview/vol2018/iss4/7 This Comment is brought to you for free and open access by the Brigham Young University Law Review at BYU Law Digital Commons. It has been accepted for inclusion in BYU Law Review by an authorized editor of BYU Law Digital Commons. For more information, please contact [email protected]. 004.POWELL_FIN2_NOHEADERS.DOCX (DO DELETE) 2/17/19 8:33 PM Bias, Employment Discrimination, and Black Women’s Hair: Another Way Forward CONTENTS I. INTRODUCTION .......................................................................................... 933 II. HISTORY OF BLACK HAIR, IMPLICIT BIAS, AND WORKPLACE GROOMING STANDARDS ..................................................................... 937 A. History of Black Hair Texture and Hairstyle: Centuries of Stereotyping ...................................................................................... 938 B. Clean, Neat, and Kept Versus Extreme, Eye-Catching, and Unprofessional: Workplace Grooming Policies Reflect Racial Stereotypes ............................................................................
    [Show full text]
  • Observational Studies and Bias in Epidemiology
    The Young Epidemiology Scholars Program (YES) is supported by The Robert Wood Johnson Foundation and administered by the College Board. Observational Studies and Bias in Epidemiology Manuel Bayona Department of Epidemiology School of Public Health University of North Texas Fort Worth, Texas and Chris Olsen Mathematics Department George Washington High School Cedar Rapids, Iowa Observational Studies and Bias in Epidemiology Contents Lesson Plan . 3 The Logic of Inference in Science . 8 The Logic of Observational Studies and the Problem of Bias . 15 Characteristics of the Relative Risk When Random Sampling . and Not . 19 Types of Bias . 20 Selection Bias . 21 Information Bias . 23 Conclusion . 24 Take-Home, Open-Book Quiz (Student Version) . 25 Take-Home, Open-Book Quiz (Teacher’s Answer Key) . 27 In-Class Exercise (Student Version) . 30 In-Class Exercise (Teacher’s Answer Key) . 32 Bias in Epidemiologic Research (Examination) (Student Version) . 33 Bias in Epidemiologic Research (Examination with Answers) (Teacher’s Answer Key) . 35 Copyright © 2004 by College Entrance Examination Board. All rights reserved. College Board, SAT and the acorn logo are registered trademarks of the College Entrance Examination Board. Other products and services may be trademarks of their respective owners. Visit College Board on the Web: www.collegeboard.com. Copyright © 2004. All rights reserved. 2 Observational Studies and Bias in Epidemiology Lesson Plan TITLE: Observational Studies and Bias in Epidemiology SUBJECT AREA: Biology, mathematics, statistics, environmental and health sciences GOAL: To identify and appreciate the effects of bias in epidemiologic research OBJECTIVES: 1. Introduce students to the principles and methods for interpreting the results of epidemio- logic research and bias 2.
    [Show full text]
  • Statistical Tips for Interpreting Scientific Claims
    Research Skills Seminar Series 2019 CAHS Research Education Program Statistical Tips for Interpreting Scientific Claims Mark Jones Statistician, Telethon Kids Institute 18 October 2019 Research Skills Seminar Series | CAHS Research Education Program Department of Child Health Research | Child and Adolescent Health Service ResearchEducationProgram.org © CAHS Research Education Program, Department of Child Health Research, Child and Adolescent Health Service, WA 2019 Copyright to this material produced by the CAHS Research Education Program, Department of Child Health Research, Child and Adolescent Health Service, Western Australia, under the provisions of the Copyright Act 1968 (C’wth Australia). Apart from any fair dealing for personal, academic, research or non-commercial use, no part may be reproduced without written permission. The Department of Child Health Research is under no obligation to grant this permission. Please acknowledge the CAHS Research Education Program, Department of Child Health Research, Child and Adolescent Health Service when reproducing or quoting material from this source. Statistical Tips for Interpreting Scientific Claims CONTENTS: 1 PRESENTATION ............................................................................................................................... 1 2 ARTICLE: TWENTY TIPS FOR INTERPRETING SCIENTIFIC CLAIMS, SUTHERLAND, SPIEGELHALTER & BURGMAN, 2013 .................................................................................................................................. 15 3
    [Show full text]
  • 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.
    [Show full text]
  • Correcting Sampling Bias in Non-Market Valuation with Kernel Mean Matching
    CORRECTING SAMPLING BIAS IN NON-MARKET VALUATION WITH KERNEL MEAN MATCHING Rui Zhang Department of Agricultural and Applied Economics University of Georgia [email protected] Selected Paper prepared for presentation at the 2017 Agricultural & Applied Economics Association Annual Meeting, Chicago, Illinois, July 30 - August 1 Copyright 2017 by Rui Zhang. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. Abstract Non-response is common in surveys used in non-market valuation studies and can bias the parameter estimates and mean willingness to pay (WTP) estimates. One approach to correct this bias is to reweight the sample so that the distribution of the characteristic variables of the sample can match that of the population. We use a machine learning algorism Kernel Mean Matching (KMM) to produce resampling weights in a non-parametric manner. We test KMM’s performance through Monte Carlo simulations under multiple scenarios and show that KMM can effectively correct mean WTP estimates, especially when the sample size is small and sampling process depends on covariates. We also confirm KMM’s robustness to skewed bid design and model misspecification. Key Words: contingent valuation, Kernel Mean Matching, non-response, bias correction, willingness to pay 2 1. Introduction Nonrandom sampling can bias the contingent valuation estimates in two ways. Firstly, when the sample selection process depends on the covariate, the WTP estimates are biased due to the divergence between the covariate distributions of the sample and the population, even the parameter estimates are consistent; this is usually called non-response bias.
    [Show full text]