Self∓Other Decision Making and Loss Aversion
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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. -
Bad Is Stronger Than Good
Review of General Psychology Copyright 2001 by the Educational Publishing Foundation 2001. Vol. 5. No. 4. 323-370 1089-2680/O1/S5.O0 DOI: 10.1037//1089-2680.5.4.323 Bad Is Stronger Than Good Roy F. Baumeister and Ellen Bratslavsky Catrin Finkenauer Case Western Reserve University Free University of Amsterdam Kathleen D. Vohs Case Western Reserve University The greater power of bad events over good ones is found in everyday events, major life events (e.g., trauma), close relationship outcomes, social network patterns, interper- sonal interactions, and learning processes. Bad emotions, bad parents, and bad feedback have more impact than good ones, and bad information is processed more thoroughly than good. The self is more motivated to avoid bad self-definitions than to pursue good ones. Bad impressions and bad stereotypes are quicker to form and more resistant to disconfirmation than good ones. Various explanations such as diagnosticity and sa- lience help explain some findings, but the greater power of bad events is still found when such variables are controlled. Hardly any exceptions (indicating greater power of good) can be found. Taken together, these findings suggest that bad is stronger than good, as a general principle across a broad range of psychological phenomena. Centuries of literary efforts and religious pothesis that bad is stronger than good (see also thought have depicted human life in terms of a Rozin & Royzman, in press). That is, events struggle between good and bad forces. At the that are negatively valenced (e.g., losing metaphysical level, evil gods or devils are the money, being abandoned by friends, and receiv- opponents of the divine forces of creation and ing criticism) will have a greater impact on the harmony. -
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, -
A Dissertation Entitled Exploring Common Antecedents of Three Related Decision Biases by Jonathan E. Westfall Submitted As Parti
A Dissertation Entitled Exploring Common Antecedents of Three Related Decision Biases by Jonathan E. Westfall Submitted as partial fulfillment of the requirements for the Doctor of Philosophy in Psychology __________________________ Advisor: Dr. J. D. Jasper __________________________ Dr. S. D. Christman __________________________ Dr. R. E. Heffner __________________________ Dr. K. L. London __________________________ Dr. M. E. Doherty __________________________ Graduate School The University of Toledo August 2009 Exploring Common Antecedents ii Copyright © 2009 – Jonathan E. Westfall This document is copyrighted material. Under copyright law, no parts of this document may be reproduced in any manner without the expressed written permission of the author. Exploring Common Antecedents iii An Abstract of Exploring Common Antecedents of Three Related Decision Biases Jonathan E. Westfall Submitted as partial fulfillment of the requirements for The Doctor of Philosophy in Psychology The University of Toledo August 2009 “Decision making inertia” is a term loosely used to describe the similar nature of a variety of decision making biases that predominantly favor a decision to maintain one course of action over switching to a new course. Three of these biases, the sunk cost effect, status-quo bias, and inaction inertia are discussed here. Combining earlier work on strength of handedness and the sunk cost effect along with new findings regarding counterfactual thought, this work principally seeks to determine if counterfactual thought may drive the three decision biases of note while also analyzing common relationships between the biases, strength of handedness, and the variables of regret and loss aversion. Over a series of experiments, it was found that handedness differences did exist in the three biases discussed, that amount and type of counterfactuals generated did not predict choice within the status-quo bias, and that the remaining variables potentially thought to drive the biases presented did not link causally to them. -
1. Service Evaluations and Qualitative Research
EAHP Academy Seminars 20 - 21 September 2019 Brussels #ACASEM2019 EAHP Academy Seminars 20-21 September 2019 Introduction to service evaluation and qualitative research Jonathan Underhill, NICE and Keele University, UK Ulrika Gillespie Uppsala University Hospital, Sweden Conflicts of interest • No conflicts of interest to declare Introduction • Understanding how humans make decisions and its impact on: – Communicating findings – Conducting research • Answering questions using qualitative and quantitative studies Introduction • Understanding how humans make decisions and its impact on: – Communicating findings – Conducting research • Answering questions using qualitative and quantitative studies What do we know about how people make decisions? • Behavioural economics and cognitive psychology: – Bounded rationality (Herbert Simon 1978) – Dual process theory (Daniel Kahneman 2002) – Most decisions are informed by brief reading and talking to other people - please find a piece of paper and a pen - a list of words follows look at them once, do not re-read them - when you have read the list close your eyes Flange Routemaster Laggard Sausages Automaton Approach Antichrist Research Slipper Haggle Fridge Locomotive Bracket Confused Telesales Professor Stool pigeon Hale Banquet Irrelevance Write down as many words as you can remember Flange How many words Routemaster A that you remembered Laggard are in each group? Sausages Automaton Approach B Antichrist Research Slipper Haggle C Fridge Locomotive Bracket Confused D Telesales Professor Stool pigeon Hale E Banquet Irrelevance Herbert Simon 1978 Economics Bounded rationality Satisfycing Please list all the medicines which have a potential interaction with warfarin – both increasing and decreasing its effect Drug interactions with warfarin – decreased effect • Amobarbital • Primidone Butabarbital Ribavirin Carbamazepine Rifabutin Cholestyramine Rifampin Dicloxacillin Secobarbital Griseofulvin Sucralfate Mercaptopurine Vitamin K Mesalamine Coenzyme Q10 Nafcillin Ginseng Phenobarbital St. -
Gender De-Biasing in Speech Emotion Recognition
INTERSPEECH 2019 September 15–19, 2019, Graz, Austria Gender de-biasing in speech emotion recognition Cristina Gorrostieta, Reza Lotfian, Kye Taylor, Richard Brutti, John Kane Cogito Corporation fcgorrostieta,rlotfian,ktaylor,rbrutti,jkaneg @cogitocorp.com Abstract vertisement delivery [9], object classification [10] and image search results for occupations [11] . Machine learning can unintentionally encode and amplify neg- There are several factors which can contribute to produc- ative bias and stereotypes present in humans, be they conscious ing negative bias in machine learning models. One major cause or unconscious. This has led to high-profile cases where ma- is incomplete or skewed training data. If certain demographic chine learning systems have been found to exhibit bias towards categories are missing from the training data, models developed gender, race, and ethnicity, among other demographic cate- on this can fail to generalise when applied to new data contain- gories. Negative bias can be encoded in these algorithms based ing those missing categories. The majority of models deployed on: the representation of different population categories in the in modern technology applications are based on supervised ma- training data; bias arising from manual human labeling of these chine learning and much of the labeled data comes from people. data; as well as modeling types and optimisation approaches. In Labels can include movie reviews, hotel ratings, image descrip- this paper we assess the effect of gender bias in speech emotion tions, audio transcriptions, and perceptual ratings of emotion. recognition and find that emotional activation model accuracy is As people are inherently biased, and because models are an es- consistently lower for female compared to male audio samples. -
Information to Users
INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6” x 9” black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. UMI A Bell & Howell Information Company 300 North Zeeb Road, Ann Arbor MI 48106-1346 USA 313/761-4700 800/521-0600 BIASES IN IMPRESSION FORMATION. A DEMONSTRATION OF A BIVARIATE MODEL OF EVALUATION DISSERTATION Presented in Partial Fulfillment for the Requirements for the Degree Doctor of Philosophy in the Graduate School of the Ohio State University By Wendi L. -
Optimism Bias and Differential Information Use in Supply Chain Forecasting
Optimism bias and differential information use in supply chain forecasting Robert Fildes Dept. Management Science, Lancaster University, LA1 4YX, UK Email: [email protected] Paul Goodwin*, School of Management, University of Bath Bath, BA2 7AY,UK Tel: 44-1225-383594 Fax: 44-1225-386473 Email: [email protected] August 2011 * Corresponding author Acknowledgements This research was supported by a grant from SAS and the International Institute of Forecasters. 1 Abstract Companies tend to produce over optimistic forecasts of demand. Psychologists have suggested that optimism, at least in part, results from selective bias in processing information. When high demand is more desirable than low demand information favouring high demand may carry more weight than information that suggests the opposite. To test this, participants in an experiment used a prototypical forecasting support system to forecast the demand uplift resulting from sales promotion campaigns. One group was rewarded if demand exceeded an uplift of 80%. All of the participants were also supplied with information some of which supported an uplift of greater than 80% and some of which suggested that the uplift would fall below this value. This enabled an assessment to be made of the extent to which those who were rewarded if the uplift exceeded 80% paid more attention to the positive information than those who were not rewarded. While the experiment provided strong evidence of desirability bias there was no evidence that the group receiving rewards for sales exceeding the threshold made greater use of positive reasons. Possible explanations for this are discussed, together with the implications for forecasting support system design. -
Immersive High Fidelity Simulation of Critically Ill Patients to Study Cognitive Errors
Prakash et al. BMC Medical Education (2017) 17:36 DOI 10.1186/s12909-017-0871-x RESEARCHARTICLE Open Access Immersive high fidelity simulation of critically ill patients to study cognitive errors: a pilot study Shivesh Prakash1,2*, Shailesh Bihari2, Penelope Need3, Cyle Sprick4 and Lambert Schuwirth1,5 Abstract Background: The majority of human errors in healthcare originate from cognitive errors or biases. There is dearth of evidence around relative prevalence and significance of various cognitive errors amongst doctors in their first post-graduate year. This study was conducted with the objective of using high fidelity clinical simulation as a tool to study the relative occurrence of selected cognitive errors amongst doctors in their first post-graduate year. Methods: Intern simulation sessions on acute clinical problems, conducted in year 2014, were reviewed by two independent assessors with expertise in critical care. The occurrence of cognitive errors was identified using Likert scale based questionnaire and think-aloud technique. Teamwork and leadership skills were assessed using Ottawa Global Rating Scale. Results: The most prevalent cognitive errors included search satisfying (90%), followed by premature closure (PC) (78.6%), and anchoring (75.7%). The odds of occurrence of various cognitive errors did not change with time during internship, in contrast to teamwork and leadership skills (x2 = 11.9, P = 0.01). Anchoring appeared to be significantly associated with delay in diagnoses (P = 0.007) and occurrence of PC (P = 0.005). There was a negative association between occurrence of confirmation bias and the ability to make correct diagnosis (P = 0.05). Conclusions: Our study demonstrated a high prevalence of anchoring, premature closure, and search satisfying amongst doctors in their first post-graduate year, using high fidelity simulation as a tool. -
Sad, Thus True. Negativity Bias in Judgments of Truth Benjamin E
Sad, thus true. Negativity bias in judgments of truth Benjamin E. Hilbig To cite this version: Benjamin E. Hilbig. Sad, thus true. Negativity bias in judgments of truth. Journal of Experimental Social Psychology, Elsevier, 2009, 45 (4), pp.983. 10.1016/j.jesp.2009.04.012. hal-00687909 HAL Id: hal-00687909 https://hal.archives-ouvertes.fr/hal-00687909 Submitted on 16 Apr 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Accepted Manuscript Sad, thus true. Negativity bias in judgments of truth Benjamin E. Hilbig PII: S0022-1031(09)00093-6 DOI: 10.1016/j.jesp.2009.04.012 Reference: YJESP 2250 To appear in: Journal of Experimental Social Psychology Received Date: 24 October 2008 Revised Date: 30 March 2009 Accepted Date: 4 April 2009 Please cite this article as: Hilbig, B.E., Sad, thus true. Negativity bias in judgments of truth, Journal of Experimental Social Psychology (2009), doi: 10.1016/j.jesp.2009.04.012 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. -
Negativity Bias, Negativity Dominance, and Contagion
Personality and Social Psychology Review Copyright © 2001 by 2001, Vol. 5, No. 4, 296–320 Lawrence Erlbaum Associates, Inc. Negativity Bias, Negativity Dominance, and Contagion Paul Rozin and Edward B. Royzman Department of Psychology and Solomon Asch Center for Study of Ethnopolitical Conflict University of Pennsylvania We hypothesize that there is a general bias, based on both innate predispositions and experience, in animals and humans, to give greater weight to negative entities (e.g., events, objects, personal traits). This is manifested in 4 ways: (a) negative potency (negative entities are stronger than the equivalent positive entities), (b) steeper nega- tive gradients (the negativity of negative events grows more rapidly with approach to them in space or time than does the positivity of positive events, (c) negativity domi- nance (combinations of negative and positive entities yield evaluations that are more negative than the algebraic sum of individual subjective valences would predict), and (d) negative differentiation (negative entities are more varied, yield more complex conceptual representations, and engage a wider response repertoire). We review evi- dence for this taxonomy, with emphasis on negativity dominance, including literary, historical, religious, and cultural sources, as well as the psychological literatures on learning, attention, impression formation, contagion, moral judgment, development, and memory. We then consider a variety of theoretical accounts for negativity bias. We suggest that 1 feature of negative events that make them dominant is that negative enti- ties are more contagious than positive entities. Brief contact with a cockroach will usually render taminated—that is, lowered in social status—by a delicious meal inedible. -
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.