Trump's $2 Trillion Mistake, the “War on Energy Efficiency
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A Task-Based Taxonomy of Cognitive Biases for Information Visualization
A Task-based Taxonomy of Cognitive Biases for Information Visualization Evanthia Dimara, Steven Franconeri, Catherine Plaisant, Anastasia Bezerianos, and Pierre Dragicevic Three kinds of limitations The Computer The Display 2 Three kinds of limitations The Computer The Display The Human 3 Three kinds of limitations: humans • Human vision ️ has limitations • Human reasoning 易 has limitations The Human 4 ️Perceptual bias Magnitude estimation 5 ️Perceptual bias Magnitude estimation Color perception 6 易 Cognitive bias Behaviors when humans consistently behave irrationally Pohl’s criteria distilled: • Are predictable and consistent • People are unaware they’re doing them • Are not misunderstandings 7 Ambiguity effect, Anchoring or focalism, Anthropocentric thinking, Anthropomorphism or personification, Attentional bias, Attribute substitution, Automation bias, Availability heuristic, Availability cascade, Backfire effect, Bandwagon effect, Base rate fallacy or Base rate neglect, Belief bias, Ben Franklin effect, Berkson's paradox, Bias blind spot, Choice-supportive bias, Clustering illusion, Compassion fade, Confirmation bias, Congruence bias, Conjunction fallacy, Conservatism (belief revision), Continued influence effect, Contrast effect, Courtesy bias, Curse of knowledge, Declinism, Decoy effect, Default effect, Denomination effect, Disposition effect, Distinction bias, Dread aversion, Dunning–Kruger effect, Duration neglect, Empathy gap, End-of-history illusion, Endowment effect, Exaggerated expectation, Experimenter's or expectation bias, -
The Evolutionary Roots of Decision Biases: Erasing and Exacerbating Loss Aversion
Evolution and Loss Aversion 1 Running head: EVOLUTION AND LOSS AVERSION In press, Journal of Personality and Social Psychology Economic Decision Biases in Evolutionary Perspective: How Mating and Self-Protection Motives Alter Loss Aversion Yexin Jessica Li, Douglas T. Kenrick Arizona State University Vladas Griskevicius University of Minnesota Steven L. Neuberg Arizona State University Evolution and Loss Aversion 2 Abstract Much research shows that people are loss-averse, meaning that they weigh losses more heavily than gains. From an evolutionary perspective, loss aversion would be expected to increase or decrease as a function of adaptive context. For example, loss aversion could have helped deal with challenges in the domain of self- protection, but would not have been beneficial for men in the domain of mating. Three experiments examine how loss aversion is influenced by mating and self- protection motives. Findings reveal that mating motives selectively erased loss aversion in men. In contrast, self-protective motives led both men and women to become more loss-averse. Overall, loss aversion appears to be sensitive to evolutionarily-important motives, suggesting that it may be a domain-specific bias operating according to an adaptive logic of recurring threats and opportunities in different evolutionary domains. Key words: Evolutionary psychology, mating, self-protection, decision-biases, loss aversion Evolution and Loss Aversion 3 Economic Decision Biases in Evolutionary Perspective: How Mating and Self-Protection Motives Alter Loss -
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, -
Pleasantness Bias in Flashbulb Memories: Positive and Negative Flashbulb Memories of the Fall of the Berlin Wall Among East and West Germans
Memory & Cognition 2007, 35 (3), 565-577 Pleasantness bias in flashbulb memories: Positive and negative flashbulb memories of the fall of the Berlin Wall among East and West Germans ANNETTE BOHN AND DORTHE BERNTSEN Aarhus University, Aarhus, Denmark Flashbulb memories for the fall of the Berlin Wall were examined among 103 East and West Germans who considered the event as either highly positive or highly negative. The participants in the positive group rated their memories higher on measures of reliving and sensory imagery, whereas their memory for facts was less accurate than that of the participants in the negative group. The participants in the negative group had higher ratings on amount of consequences but had talked less about the event and considered it less central to their personal and national identity than did the participants in the positive group. In both groups, rehearsal and the centrality of the memory to the person’s identity and life story correlated positively with memory qualities. The results suggest that positive and negative emotions have different effects on the processing and long-term reten- tion of flashbulb memories. On Thursday, November 9, 1989, the Berlin Wall fell Wall, and how well do they remember factual details in re- after having divided East and West Germany for 28 years. lation to the event? These are the chief questions raised in On that day at 6:57 p.m., Günther Schabowski, a leading the present article. By addressing these questions, we wish member of the ruling communist party in East Germany, to investigate whether positive versus negative affect is as- had casually announced to a stunned audience during a sociated with different qualities of flashbulb memories. -
Adaptive Bias Correction for Satellite Data in a Numerical Weather Prediction System
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY Q. J. R. Meteorol. Soc. 133: 631–642 (2007) Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/qj.56 Adaptive bias correction for satellite data in a numerical weather prediction system T. Auligne,*´ A. P. McNally and D. P. Dee European Centre for Medium-Range Weather Forecasts, Reading, UK ABSTRACT: Adaptive bias corrections for satellite radiances need to separate the observation bias from the systematic errors in the background in order to prevent the analysis from drifting towards its own climate. The variational bias correction scheme (VarBC) is a particular adaptive scheme that is embedded inside the assimilation system. VarBC is compared with an offline adaptive and a static bias correction scheme. In simulation, the three schemes are exposed to artificial shifts in the observations and the background. The VarBC scheme can be considered as a good compromise between the static and the offline adaptive schemes. It shows some skill in distinguishing between the background-error and the observation biases when other unbiased observations are available to anchor the system. Tests of VarBC in a real numerical weather prediction (NWP) environment show a significant reduction in the misfit with radiosonde observations (especially in the stratosphere) due to NWP model error. The scheme adapts to an instrument error with only minimal disruption to the analysis. In VarBC, the bias is constrained by the fit to observations – such as radiosondes – that are not bias-corrected to the NWP model. In parts of the atmosphere where no radiosonde observations are available, the radiosonde network still imposes an indirect constraint on the system, which can be enhanced by applying a mask to VarBC. -
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 -
Detection of Confirmation and Distinction Biases in Visual
EuroVis Workshop on Trustworthy Visualization (TrustVis) (2019) R. Kosara, K. Lawonn, L. Linsen, and N. Smit (Editors) Detection of Confirmation and Distinction Biases in Visual Analytics Systems . A. Nalcaci , D. Girgin , S. Balki , F. Talay, H. A. Boz and S. Balcisoy 1Sabanci University, Faculty of Engineering & Natural Sciences, Istanbul, Turkey Abstract Cognitive bias is a systematic error that introduces drifts and distortions in the human judgment in terms of visual decompo- sition in the direction of the dominant instance. It has a significant role in decision-making process by means of evaluation of data visualizations. This paper elaborates on the experimental depiction of two cognitive bias types, namely Distinction Bias and Confirmation Bias, through the examination of cognate visual experimentations. The main goal of this implementation is to indicate the existence of cognitive bias in visual analytics systems through the adjustment of data visualization and crowdsourc- ing in terms of confirmation and distinction biases. Two distinct surveys that include biased and unbiased data visualizations which are related to a given data set were established in order to detect and measure the level of existence of introduced bias types. Practice of crowdsourcing which is provided by Amazon Mechanical Turk have been used for experimentation purposes through prepared surveys. Results statistically indicate that both distinction and confirmation biases has substantial effect and prominent significance on decision-making process. CCS Concepts • Human-centered computing ! Empirical studies in visualization; Visualization design and evaluation methods; 1. Introduction cording to existing studies, usual heuristic errors involve confirma- tion bias, which characterizes people’s approaches to receive the Visual perception is the combination of physical and thoughtful de- confirmatory corroboration of a pre-existing hypothesis and dis- sign that provides the ability to interpret the environment and the miss contrary information. -
A Neural Network Framework for Cognitive Bias
fpsyg-09-01561 August 31, 2018 Time: 17:34 # 1 HYPOTHESIS AND THEORY published: 03 September 2018 doi: 10.3389/fpsyg.2018.01561 A Neural Network Framework for Cognitive Bias Johan E. Korteling*, Anne-Marie Brouwer and Alexander Toet* TNO Human Factors, Soesterberg, Netherlands Human decision-making shows systematic simplifications and deviations from the tenets of rationality (‘heuristics’) that may lead to suboptimal decisional outcomes (‘cognitive biases’). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases. To enhance our understanding of cognitive heuristics and biases we propose a neural network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic (‘Type 1’) decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. To substantiate our viewpoint, Edited by: we discern and explain four basic neural network principles: (1) Association, (2) Eldad Yechiam, Technion – Israel Institute Compatibility, (3) Retainment, and (4) Focus. These principles are inherent to (all) neural of Technology, Israel networks which were originally optimized to perform concrete biological, perceptual, Reviewed by: and motor functions. They form the basis for our inclinations to associate and combine Amos Schurr, (unrelated) information, to prioritize information that is compatible with our present Ben-Gurion University of the Negev, Israel state (such as knowledge, opinions, and expectations), to retain given information Edward J. -
Memory Category Fluency, Memory Specificity, And
Journal of Experimental Psychology: General © 2019 The Author(s) 2020, Vol. 149, No. 1, 198–206 0096-3445/20/$12.00 http://dx.doi.org/10.1037/xge0000617 BRIEF REPORT Memory Category Fluency, Memory Specificity, and the Fading Affect Bias for Positive and Negative Autobiographical Events: Performance on a Good Day–Bad Day Task in Healthy and Depressed Individuals Caitlin Hitchcock, Jill Newby, Emma Timm, Willem Kuyken Rachel M. Howard, and Ann-Marie Golden University of Oxford University of Cambridge Tim Dalgleish University of Cambridge and Cambridgeshire and Peterborough NHS Foundation Trust In mentally healthy individuals, autobiographical memory is typically biased toward positive events, which may help to maintain psychological well-being. Our aim was to assess a range of important positive memory biases in the mentally healthy and explore the possibility that these biases are mitigated in those with mental health problems. We administered a novel recall paradigm that required recollection of multiple good and bad past events (the Good Day–Bad Day task) to healthy and depressed individuals. This allowed us to explore differences in memory category fluency (i.e., the ability to generate integrated sets of associated events) for positive and negative memories, along with memory specificity, and fading affect bias—a greater reduction in the intensity of memory-related affect over time for negative versus positive events. We found that healthy participants demonstrated superior category fluency for positive relative to negative events but that this effect was absent in depressed participants. Healthy participants exhibited a strong fading affect bias that was significantly mitigated, although still present, in depression. -
Communication Science to the Public
David M. Berube North Carolina State University ▪ HOW WE COMMUNICATE. In The Age of American Unreason, Jacoby posited that it trickled down from the top, fueled by faux-populist politicians striving to make themselves sound approachable rather than smart. (Jacoby, 2008). EX: The average length of a sound bite by a presidential candidate in 1968 was 42.3 seconds. Two decades later, it was 9.8 seconds. Today, it’s just a touch over seven seconds and well on its way to being supplanted by 140/280- character Twitter bursts. ▪ DATA FRAMING. ▪ When asked if they truly believe what scientists tell them, NEW ANTI- only 36 percent of respondents said yes. Just 12 percent expressed strong confidence in the press to accurately INTELLECTUALISM: report scientific findings. ▪ ROLE OF THE PUBLIC. A study by two Princeton University researchers, Martin TRENDS Gilens and Benjamin Page, released Fall 2014, tracked 1,800 U.S. policy changes between 1981 and 2002, and compared the outcome with the expressed preferences of median- income Americans, the affluent, business interests and powerful lobbies. They concluded that average citizens “have little or no independent influence” on policy in the U.S., while the rich and their hired mouthpieces routinely get their way. “The majority does not rule,” they wrote. ▪ Anti-intellectualism and suspicion (trends). ▪ Trump world – outsiders/insiders. ▪ Erasing/re-writing history – damnatio memoriae. ▪ False news. ▪ Infoxication (CC) and infobesity. ▪ Aggregators and managed reality. ▪ Affirmation and confirmation bias. ▪ Negotiating reality. ▪ New tribalism is mostly ideational not political. ▪ Unspoken – guns, birth control, sexual harassment, race… “The amount of technical information is doubling every two years. -
Chapter 4: INFORMAL FALLACIES I
Essential Logic Ronald C. Pine Chapter 4: INFORMAL FALLACIES I All effective propaganda must be confined to a few bare necessities and then must be expressed in a few stereotyped formulas. Adolf Hitler Until the habit of thinking is well formed, facing the situation to discover the facts requires an effort. For the mind tends to dislike what is unpleasant and so to sheer off from an adequate notice of that which is especially annoying. John Dewey, How We Think Introduction In everyday speech you may have heard someone refer to a commonly accepted belief as a fallacy. What is usually meant is that the belief is false, although widely accepted. In logic, a fallacy refers to logically weak argument appeal (not a belief or statement) that is widely used and successful. Here is our definition: A logical fallacy is an argument that is usually psychologically persuasive but logically weak. By this definition we mean that fallacious arguments work in getting many people to accept conclusions, that they make bad arguments appear good even though a little commonsense reflection will reveal that people ought not to accept the conclusions of these arguments as strongly supported. Although logicians distinguish between formal and informal fallacies, our focus in this chapter and the next one will be on traditional informal fallacies.1 For our purposes, we can think of these fallacies as "informal" because they are most often found in the everyday exchanges of ideas, such as newspaper editorials, letters to the editor, political speeches, advertisements, conversational disagreements between people in social networking sites and Internet discussion boards, and so on. -
Logical Fallacies and Distraction Techniques
Sample Activity Learning Critical Thinking Through Astronomy: Logical Fallacies and Distraction Techniques Joe Heafner [email protected] Version 2017-09-13 STUDENT NOTE PLEASE DO NOT DISTRIBUTE THIS DOCUMENT. 2017-09-13 Activity0105 CONTENTS Contents QuestionsSample Activity1 Materials Needed 1 Points To Remember 1 1 Fallacies and Distractions1 Student1.1 Lying................................................. Version 1 1.2 Shifting The Burden.........................................2 1.3 Appeal To Emotion.........................................3 1.4 Appeal To The Past.........................................4 1.5 Appeal To Novelty..........................................5 1.6 Appeal To The People (Appeal To The Masses, Appeal To Popularity).............6 1.7 Appeal To Logic...........................................7 1.8 Appeal To Ignorance.........................................8 1.9 Argument By Repetition....................................... 10 1.10 Attacking The Person........................................ 11 1.11 Confirmation Bias.......................................... 12 1.12 Strawman Argument or Changing The Subject.......................... 13 1.13 False Premise............................................. 14 1.14 Hasty Generalization......................................... 15 1.15 Loaded Question........................................... 16 1.16 Feigning Offense........................................... 17 1.17 False Dilemma............................................ 17 1.18 Appeal To Authority........................................