Race Equity Project – Debiasing Techniques
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2020 WGEA Regional Meeting Abstract Compendium
2020 WGEA Regional Meeting Abstract Compendium Table of Contents Message from WGEA Chair ........................................................................................................... 2 Message from WGEA Conference/ Host Chairs ............................................................................ 3 Special Thanks and Acknowledgments .......................................................................................... 4 WGEA Awards ............................................................................................................................... 5 Innovation Abstracts ....................................................................................................................... 6 Research Abstracts ........................................................................................................................ 85 Small Group, Workshop and Panel Discussion Abstracts .......................................................... 155 List of WGEA Steering Committee members ............................................................................ 273 List of WGEA Planning Committee and Subcommittees members ........................................... 274 2020 WGEA Reviewer List ........................................................................................................ 275 1 Message from WGEA Chair On behalf of the Western Group on Educational Affairs (WGEA) Steering Committee, I welcome you to the WGEA 2020 Annual Spring Meeting, Finding Common Ground. We will highlight the following -
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. -
Avoiding the Conjunction Fallacy: Who Can Take a Hint?
Avoiding the conjunction fallacy: Who can take a hint? Simon Klein Spring 2017 Master’s thesis, 30 ECTS Master’s programme in Cognitive Science, 120 ECTS Supervisor: Linnea Karlsson Wirebring Acknowledgments: The author would like to thank the participants for enduring the test session with challenging questions and thereby making the study possible, his supervisor Linnea Karlsson Wirebring for invaluable guidance and good questions during the thesis work, and his fiancée Amanda Arnö for much needed mental support during the entire process. 2 AVOIDING THE CONJUNCTION FALLACY: WHO CAN TAKE A HINT? Simon Klein Humans repeatedly commit the so called “conjunction fallacy”, erroneously judging the probability of two events occurring together as higher than the probability of one of the events. Certain hints have been shown to mitigate this tendency. The present thesis investigated the relations between three psychological factors and performance on conjunction tasks after reading such a hint. The factors represent the understanding of probability and statistics (statistical numeracy), the ability to resist intuitive but incorrect conclusions (cognitive reflection), and the willingness to engage in, and enjoyment of, analytical thinking (need-for-cognition). Participants (n = 50) answered 30 short conjunction tasks and three psychological scales. A bimodal response distribution motivated dichotomization of performance scores. Need-for-cognition was significantly, positively correlated with performance, while numeracy and cognitive reflection were not. The results suggest that the willingness to engage in, and enjoyment of, analytical thinking plays an important role for the capacity to avoid the conjunction fallacy after taking a hint. The hint further seems to neutralize differences in performance otherwise predicted by statistical numeracy and cognitive reflection. -
Cognitive Bias Mitigation: How to Make Decision-Making More Rational?
Cognitive Bias Mitigation: How to make decision-making more rational? Abstract Cognitive biases distort judgement and adversely impact decision-making, which results in economic inefficiencies. Initial attempts to mitigate these biases met with little success. However, recent studies which used computer games and educational videos to train people to avoid biases (Clegg et al., 2014; Morewedge et al., 2015) showed that this form of training reduced selected cognitive biases by 30 %. In this work I report results of an experiment which investigated the debiasing effects of training on confirmation bias. The debiasing training took the form of a short video which contained information about confirmation bias, its impact on judgement, and mitigation strategies. The results show that participants exhibited confirmation bias both in the selection and processing of information, and that debiasing training effectively decreased the level of confirmation bias by 33 % at the 5% significance level. Key words: Behavioural economics, cognitive bias, confirmation bias, cognitive bias mitigation, confirmation bias mitigation, debiasing JEL classification: D03, D81, Y80 1 Introduction Empirical research has documented a panoply of cognitive biases which impair human judgement and make people depart systematically from models of rational behaviour (Gilovich et al., 2002; Kahneman, 2011; Kahneman & Tversky, 1979; Pohl, 2004). Besides distorted decision-making and judgement in the areas of medicine, law, and military (Nickerson, 1998), cognitive biases can also lead to economic inefficiencies. Slovic et al. (1977) point out how they distort insurance purchases, Hyman Minsky (1982) partly blames psychological factors for economic cycles. Shefrin (2010) argues that confirmation bias and some other cognitive biases were among the significant factors leading to the global financial crisis which broke out in 2008. -
Critical Thinking Education and Debiasing
Critical Thinking Education and Debiasing TIM KENYON University of Waterloo Department of Philosophy 200 University Avenue West Waterloo, ON N2L 3G1 Canada [email protected] GUILLAUME BEAULAC Yale University Department of Philosophy 344 College St. New Haven, CT. 06511-6629 USA [email protected] Abstract: There are empirical Résumé: Des données empiriques grounds to doubt the effectiveness of nous permettent de douter de a common and intuitive approach to l'efficacité d'une approche commune teaching debiasing strategies in et intuitive pour enseigner des critical thinking courses. We stratégies de correction de biais summarize some of the grounds cognitifs dans les cours de pensée before suggesting a broader critique. Nous résumons certains de taxonomy of debiasing strategies. ces résultats empiriques avant de This four-level taxonomy enables a suggérer une taxonomie plus useful diagnosis of biasing factors étendue de ces stratégies de and situations, and illuminates more correction de biais. Cette taxonomie strategies for more effective bias à quatre niveaux permet un mitigation located in the shaping of diagnostic utile de facteurs causant situational factors and reasoning les biais et elle met en évidence infrastructure—sometimes called davantage de stratégies permettant la “nudges” in the literature. The correction plus efficace de biais, question, we contend, then becomes stratégies situées dans des mesures how best to teach the construction modifiant les infrastructures et les and use of such infrastructures. environnements cognitifs ("nudge" dans la littérature). Nous soutenons que la question porte dès lors sur les meilleures façons d'enseigner la construction et l'utilisation de ces infrastructures. Keywords: Critical thinking, biases, debiasing, education, nudges © Tim Kenyon and Guillaume Beaulac, Informal Logic, Vol. -
Graphical Techniques in Debiasing: an Exploratory Study
GRAPHICAL TECHNIQUES IN DEBIASING: AN EXPLORATORY STUDY by S. Bhasker Information Systems Department Leonard N. Stern School of Business New York University New York, New York 10006 and A. Kumaraswamy Management Department Leonard N. Stern School of Business New York University New York, NY 10006 October, 1990 Center for Research on Information Systems Information Systems Department Leonard N. Stern School of Business New York University Working Paper Series STERN IS-90-19 Forthcoming in the Proceedings of the 1991 Hawaii International Conference on System Sciences Center for Digital Economy Research Stem School of Business IVorking Paper IS-90-19 Center for Digital Economy Research Stem School of Business IVorking Paper IS-90-19 2 Abstract Base rate and conjunction fallacies are consistent biases that influence decision making involving probability judgments. We develop simple graphical techniques and test their eflcacy in correcting for these biases. Preliminary results suggest that graphical techniques help to overcome these biases and improve decision making. We examine the implications of incorporating these simple techniques in Executive Information Systems. Introduction Today, senior executives operate in highly uncertain environments. They have to collect, process and analyze a deluge of information - most of it ambiguous. But, their limited information acquiring and processing capabilities constrain them in this task [25]. Increasingly, executives rely on executive information/support systems for various purposes like strategic scanning of their business environments, internal monitoring of their businesses, analysis of data available from various internal and external sources, and communications [5,19,32]. However, executive information systems are, at best, support tools. Executives still rely on their mental or cognitive models of their businesses and environments and develop heuristics to simplify decision problems [10,16,25]. -
Cognitive Biases in Economic Decisions – Three Essays on the Impact of Debiasing
TECHNISCHE UNIVERSITÄT MÜNCHEN Lehrstuhl für Betriebswirtschaftslehre – Strategie und Organisation Univ.-Prof. Dr. Isabell M. Welpe Cognitive biases in economic decisions – three essays on the impact of debiasing Christoph Martin Gerald Döbrich Abdruck der von der Fakultät für Wirtschaftswissenschaften der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Wirtschaftswissenschaften (Dr. rer. pol.) genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Gunther Friedl Prüfer der Dissertation: 1. Univ.-Prof. Dr. Isabell M. Welpe 2. Univ.-Prof. Dr. Dr. Holger Patzelt Die Dissertation wurde am 28.11.2012 bei der Technischen Universität München eingereicht und durch die Fakultät für Wirtschaftswissenschaften am 15.12.2012 angenommen. Acknowledgments II Acknowledgments Numerous people have contributed to the development and successful completion of this dissertation. First of all, I would like to thank my supervisor Prof. Dr. Isabell M. Welpe for her continuous support, all the constructive discussions, and her enthusiasm concerning my dissertation project. Her challenging questions and new ideas always helped me to improve my work. My sincere thanks also go to Prof. Dr. Matthias Spörrle for his continuous support of my work and his valuable feedback for the articles building this dissertation. Moreover, I am grateful to Prof. Dr. Dr. Holger Patzelt for acting as the second advisor for this thesis and Professor Dr. Gunther Friedl for leading the examination board. This dissertation would not have been possible without the financial support of the Elite Network of Bavaria. I am very thankful for the financial support over two years which allowed me to pursue my studies in a focused and efficient manner. Many colleagues at the Chair for Strategy and Organization of Technische Universität München have supported me during the completion of this thesis. -
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. -
The Challenge of Debiasing Personnel Decisions: Avoiding Both Under- and Overcorrection
University of Pennsylvania ScholarlyCommons Management Papers Wharton Faculty Research 12-2008 The Challenge of Debiasing Personnel Decisions: Avoiding Both Under- and Overcorrection Philip E. Tetlock University of Pennsylvania Gregory Mitchell Terry L. Murray Follow this and additional works at: https://repository.upenn.edu/mgmt_papers Part of the Business Administration, Management, and Operations Commons Recommended Citation Tetlock, P. E., Mitchell, G., & Murray, T. L. (2008). The Challenge of Debiasing Personnel Decisions: Avoiding Both Under- and Overcorrection. Industrial and Organizational Psychology, 1 (4), 439-443. http://dx.doi.org/10.1111/j.1754-9434.2008.00084.x This paper is posted at ScholarlyCommons. https://repository.upenn.edu/mgmt_papers/32 For more information, please contact [email protected]. The Challenge of Debiasing Personnel Decisions: Avoiding Both Under- and Overcorrection Disciplines Business Administration, Management, and Operations This journal article is available at ScholarlyCommons: https://repository.upenn.edu/mgmt_papers/32 1 The Challenge of Debiasing Personnel Decisions: Avoiding Both Under- and Over-Correction Philip E. Tetlock, * Gregory Mitchell, ** and Terry L. Murray *** Introduction This commentary advances two interrelated scientific arguments. First, we endorse Landy's (2008) concerns about the insufficient emphasis placed on individuating information by scholars eager to import social-cognition work on stereotyping into employment law. Building on Landy’s analysis, we emphasize that greater attention needs to be given to the power of accountability and teamwork incentives to motivate personnel decision-makers to seek and utilize individuating information that is predictive of job-relevant behavior. Second, we note how easy it is for exchanges between proponents and skeptics of unconscious stereotyping to lead to ideological stalemates. -
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. -
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 -
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.