Why So Confident? the Influence of Outcome Desirability on Selective Exposure and Likelihood Judgment
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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, -
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. -
Motivated Reasoning in a Causal Explore-Exploit Task by Zachary A. Caddick B.A. in Psychology, California State University
Motivated Reasoning in a Causal Explore-Exploit Task by Zachary A. Caddick B.A. in Psychology, California State University, San Bernardino, 2013 M.A. in Experimental and Research Psychology, San José State University, 2016 Submitted to the Graduate Faculty of the Kenneth P. Dietrich School of Arts and Sciences in partial fulfillment of the requirements for the degree of Master of Science University of Pittsburgh 2020 UNIVERSITY OF PITTSBURGH DIETRICH SCHOOL OF ARTS AND SCIENCES This thesis was presented by Zachary A. Caddick It was defended on March 17, 2020 and approved by Timothy J. Nokes-Malach, Ph.D., Associate Professor, Department of Psychology Kevin R. Binning, Ph.D., Assistant Professor, Department of Psychology Thesis Advisor: Benjamin M. Rottman, Ph.D., Associate Professor, Department of Psychology ii Copyright © by Zachary A. Caddick 2020 iii Motivated Reasoning in a Causal Explore-Exploit Task Zachary A. Caddick, M.S. University of Pittsburgh, 2020 The current research investigates how prior preferences affect causal learning. Participants were tasked with repeatedly choosing policies (e.g., increase vs. decrease border security funding) in order to maximize the economic output of an imaginary country, and inferred the influence of the policies on the economy. The task was challenging and ambiguous, allowing participants to interpret the relations between the policies and the economy in multiple ways. In three studies, we found evidence of motivated reasoning despite financial incentives for accuracy. For example, participants who believed that border security funding should be increased were more likely to conclude that increasing border security funding actually caused a better economy in the task. -
THE ROLE of PUBLICATION SELECTION BIAS in ESTIMATES of the VALUE of a STATISTICAL LIFE W
THE ROLE OF PUBLICATION SELECTION BIAS IN ESTIMATES OF THE VALUE OF A STATISTICAL LIFE w. k i p vi s c u s i ABSTRACT Meta-regression estimates of the value of a statistical life (VSL) controlling for publication selection bias often yield bias-corrected estimates of VSL that are substantially below the mean VSL estimates. Labor market studies using the more recent Census of Fatal Occu- pational Injuries (CFOI) data are subject to less measurement error and also yield higher bias-corrected estimates than do studies based on earlier fatality rate measures. These re- sultsareborneoutbythefindingsforalargesampleofallVSLestimatesbasedonlabor market studies using CFOI data and for four meta-analysis data sets consisting of the au- thors’ best estimates of VSL. The confidence intervals of the publication bias-corrected estimates of VSL based on the CFOI data include the values that are currently used by government agencies, which are in line with the most precisely estimated values in the literature. KEYWORDS: value of a statistical life, VSL, meta-regression, publication selection bias, Census of Fatal Occupational Injuries, CFOI JEL CLASSIFICATION: I18, K32, J17, J31 1. Introduction The key parameter used in policy contexts to assess the benefits of policies that reduce mortality risks is the value of a statistical life (VSL).1 This measure of the risk-money trade-off for small risks of death serves as the basis for the standard approach used by government agencies to establish monetary benefit values for the predicted reductions in mortality risks from health, safety, and environmental policies. Recent government appli- cations of the VSL have used estimates in the $6 million to $10 million range, where these and all other dollar figures in this article are in 2013 dollars using the Consumer Price In- dex for all Urban Consumers (CPI-U). -
Bias and Fairness in NLP
Bias and Fairness in NLP Margaret Mitchell Kai-Wei Chang Vicente Ordóñez Román Google Brain UCLA University of Virginia Vinodkumar Prabhakaran Google Brain Tutorial Outline ● Part 1: Cognitive Biases / Data Biases / Bias laundering ● Part 2: Bias in NLP and Mitigation Approaches ● Part 3: Building Fair and Robust Representations for Vision and Language ● Part 4: Conclusion and Discussion “Bias Laundering” Cognitive Biases, Data Biases, and ML Vinodkumar Prabhakaran Margaret Mitchell Google Brain Google Brain Andrew Emily Simone Parker Lucy Ben Elena Deb Timnit Gebru Zaldivar Denton Wu Barnes Vasserman Hutchinson Spitzer Raji Adrian Brian Dirk Josh Alex Blake Hee Jung Hartwig Blaise Benton Zhang Hovy Lovejoy Beutel Lemoine Ryu Adam Agüera y Arcas What’s in this tutorial ● Motivation for Fairness research in NLP ● How and why NLP models may be unfair ● Various types of NLP fairness issues and mitigation approaches ● What can/should we do? What’s NOT in this tutorial ● Definitive answers to fairness/ethical questions ● Prescriptive solutions to fix ML/NLP (un)fairness What do you see? What do you see? ● Bananas What do you see? ● Bananas ● Stickers What do you see? ● Bananas ● Stickers ● Dole Bananas What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store ● Bananas on shelves What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas at a store ● Bananas on shelves ● Bunches of bananas What do you see? ● Bananas ● Stickers ● Dole Bananas ● Bananas -
Polarization and Media Usage: Disentangling Causality
Polarization and Media Usage: Disentangling Causality Abstract: This chapter examines the literature concerning media choice and partisan polarization. The past few decades have seen enormous growth in the number of television and internet news sources, giving consumers dramatically increased choices. Previous research has suggested two distinct links between media choice and partisan polarization: partisan media as a reflection of polarization, as partisans self-select into media that conforms with their preexisting views, or as a cause of polarization, when outlets present one-sided stories that persuade people to adopt more extreme views. This chapter discusses how the literature in these two research traditions has diverged, as well as more recent research attempting to bridge this divide. Using novel methods, these studies have drawn together both self-selection and causal research designs to provide a more complete picture of media choice effects, and expanded the literature to more recent mediums, including the internet and social media. Justin de Benedictis-Kessner, Harvard University and Northeastern University ([email protected]) Matthew A. Baum, Harvard University ([email protected])* Adam J. Berinsky, Massachusetts Institute of Technology ([email protected]) Keywords: media choice, polarization, self-selection, persuasion, causality *corresponding author In recent years, pundits, politicians, and ordinary citizens have expressed growing concern over political polarization in the United States. A great deal of this outcry has focused on the rise of partisan news media, and how its growth has allowed people to choose the media that they consume. The typical U.S. household now receives about 190 television channels, more than a tenfold increase since 1980 and up by nearly half since 2008.i The options for different news sources on the internet are even more numerous. -
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. -
Rethinking the Link Between Cognitive Sophistication and Politically Motivated Reasoning
RETHINKING SOPHISTICATION AND MOTIVATED REASONING 1 Rethinking the link between cognitive sophistication and politically motivated reasoning Ben M. Tappin 1, Gordon Pennycook 2, David G. Rand 1,3 1 Sloan School of Management, Massachusetts Institute of Technology 2 Hill/Levene School of Business, University of Regina 3 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology Forthcoming in the Journal of Experimental Psychology: General. Author note This article contains supplementary information accessible at https://osf.io/yt3kd/. We are grateful to Antonio Arechar for assistance with data collection, and to Dan Kahan and Philip Hanser for comments on and discussion regarding an earlier draft. We acknowledge funding from the Economic and Social Research Council, the Ethics and Governance of Artificial Intelligence Initiative of the Miami Foundation, the William and Flora Hewlett Foundation, the John Templeton Foundation, the Social Sciences and Humanities Research Council of Canada, and the Cogito Foundation. Correspondence concerning this article should be addressed to Ben Tappin, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142, United States of America. Email: [email protected] RETHINKING SOPHISTICATION AND MOTIVATED REASONING 2 Abstract Partisan disagreement over policy-relevant facts is a salient feature of contemporary American politics. Perhaps surprisingly, such disagreements are often the greatest among opposing partisans who are the most cognitively sophisticated. -
Motivated Reasoning Without Partisanship? Fake News in the 2018 Brazilian Elections
Motivated Reasoning Without Partisanship? Fake News in the 2018 Brazilian Elections Frederico Batista Pereira1, Natália S. Bueno2, Felipe Nunes3, and Nara Pavão4 1Assistant Professor, University of North Carolina at Charlotte 2Assistant Professor, Emory University 3Assistant Professor, Universidade Federal de Minas Gerais 4Assistant Professor, Universidade Federal de Pernambuco Abstract Studies suggest that rumor acceptance is driven by motivated reasoning and that people’s desire to conclude what is suggested by their partisanship undermines the effectiveness of corrective information. This paper explores this process in a context where party attachments are weaker and less stable than elsewhere. We conducted a survey experiment during the 2018 elections in Brazil to examine the extent of rumor acceptance and the effectiveness of fact-checking corrections to fake news stories disseminated about the country’s most important political group. We find that about a third of our respondents believe the rumors used in the study and that, just like what is found in developed nations, belief in misinformation in Brazil is correlated with (anti)partisan attitudes. However, fact-checking corrections are particularly weak in Brazil compared to the developed world. While weak parti- sanship reduces the rates of rumor acceptance in Brazil, ineffective corrections are unlikely to prevent the circulation of fake news in the country. Please do not quote or cite without permission Fake news, understood as false information whose purpose is to generate and reinforce misperceptions of reality, is a growing concern in politics, due to its potential to distort public debate and disrupt elections (Lazer et al. 2018). The vast majority of the knowl- edge we currently have about political misperceptions comes from research conducted in the developed world, particularly in the United States (Walter and Murphy 2018; Nieminen and Rapeli 2019). -
The Evidence for Motivated Reasoning in Climate Change Preference Formation
REVIEW ARTICLE https://doi.org/10.1038/s41558-018-0360-1 The evidence for motivated reasoning in climate change preference formation James N. Druckman * and Mary C. McGrath Despite a scientific consensus, citizens are divided when it comes to climate change — often along political lines. Democrats or liberals tend to believe that human activity is a primary cause of climate change, whereas Republicans or conservatives are much less likely to hold this belief. A prominent explanation for this divide is that it stems from directional motivated reason- ing: individuals reject new information that contradicts their standing beliefs. In this Review, we suggest that the empirical evidence is not so clear, and is equally consistent with a theory in which citizens strive to form accurate beliefs but vary in what they consider to be credible evidence. This suggests a new research agenda on climate change preference formation, and has implications for effective communication. widely discussed explanation for the public divide in beliefs Our starting point is a standing (or prior) belief. This belief about climate change is that people engage in ‘directional can be any climate change-relevant construct, such as beliefs that A motivated reasoning’1–4. According to this explanation, indi- climate change is occurring, that climate change is anthropogenic, viduals skeptical about climate change reject ostensibly credible about a scientific consensus on climate change, about a conspir- scientific information because it counters their standing beliefs. acy with regard to climate change, about who is responsible for Considering the threat that such a tendency poses to effectual sci- causing and/or addressing climate change, about the efficacy of entific communication, scholars and practitioners have focused on mitigation policies, about risks from climate change, about the identifying conditions that curtail or counteract directional moti- impact of climate-relevant behaviours (such as biking instead of vated reasoning5,6. -
Evaluation of Selection Bias in an Internet-Based Study of Pregnancy Planners
HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Epidemiology Manuscript Author . Author manuscript; Manuscript Author available in PMC 2016 April 04. Published in final edited form as: Epidemiology. 2016 January ; 27(1): 98–104. doi:10.1097/EDE.0000000000000400. Evaluation of Selection Bias in an Internet-based Study of Pregnancy Planners Elizabeth E. Hatcha, Kristen A. Hahna, Lauren A. Wisea, Ellen M. Mikkelsenb, Ramya Kumara, Matthew P. Foxa, Daniel R. Brooksa, Anders H. Riisb, Henrik Toft Sorensenb, and Kenneth J. Rothmana,c aDepartment of Epidemiology, Boston University School of Public Health, Boston, MA bDepartment of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark cRTI Health Solutions, Durham, NC Abstract Selection bias is a potential concern in all epidemiologic studies, but it is usually difficult to assess. Recently, concerns have been raised that internet-based prospective cohort studies may be particularly prone to selection bias. Although use of the internet is efficient and facilitates recruitment of subjects that are otherwise difficult to enroll, any compromise in internal validity would be of great concern. Few studies have evaluated selection bias in internet-based prospective cohort studies. Using data from the Danish Medical Birth Registry from 2008 to 2012, we compared six well-known perinatal associations (e.g., smoking and birth weight) in an inter-net- based preconception cohort (Snart Gravid n = 4,801) with the total population of singleton live births in the registry (n = 239,791). We used log-binomial models to estimate risk ratios (RRs) and 95% confidence intervals (CIs) for each association. We found that most results in both populations were very similar. -
Testing for Selection Bias IZA DP No
IZA DP No. 8455 Testing for Selection Bias Joonhwi Joo Robert LaLonde September 2014 DISCUSSION PAPER SERIES Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor Testing for Selection Bias Joonhwi Joo University of Chicago Robert LaLonde University of Chicago and IZA Discussion Paper No. 8455 September 2014 IZA P.O. Box 7240 53072 Bonn Germany Phone: +49-228-3894-0 Fax: +49-228-3894-180 E-mail: [email protected] Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. IZA Discussion Paper No.