The Experts below are selected from a list of 28155 Experts worldwide ranked by ideXlab platform
May C I Van Schalkwyk - One of the best experts on this subject based on the ideXlab platform.
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dark nudges and sludge in big alcohol behavioral economics Cognitive Biases and alcohol industry corporate social responsibility
Milbank Quarterly, 2020Co-Authors: Mark Petticrew, Nason Maani, Luisa M Pettigrew, Harry Rutter, May C I Van SchalkwykAbstract:Policy Points: Nudges steer people toward certain options but also allow them to go their own way. "Dark nudges" aim to change consumer behavior against their best interests. "Sludge" uses Cognitive Biases to make behavior change more difficult. We have identified dark nudges and sludge in alcohol industry corporate social responsibility (CSR) materials. These undermine the information on alcohol harms that they disseminate, and may normalize or encourage alcohol consumption. Policymakers and practitioners should be aware of how dark nudges and sludge are used by the alcohol industry to promote misinformation about alcohol harms to the public. CONTEXT: "Nudges" and other behavioral economic approaches exploit common Cognitive Biases (systematic errors in thought processes) in order to influence behavior and decision-making. Nudges that encourage the consumption of harmful products (for example, by exploiting gamblers' Cognitive Biases) have been termed "dark nudges." The term "sludge" has also been used to describe strategies that utilize Cognitive Biases to make behavior change harder. This study aimed to identify whether dark nudges and sludge are used by alcohol industry (AI)-funded corporate social responsibility (CSR) organizations, and, if so, to determine how they align with existing nudge conceptual frameworks. This information would aid their identification and mitigation by policymakers, researchers, and civil society. METHODS: We systematically searched websites and materials of AI CSR organizations (e.g., IARD, Drinkaware, Drinkwise, Educ'alcool); examples were coded by independent raters and categorized for further analysis. FINDINGS: Dark nudges appear to be used in AI communications about "responsible drinking." The approaches include social norming (telling consumers that "most people" are drinking) and priming drinkers by offering verbal and pictorial cues to drink, while simultaneously appearing to warn about alcohol harms. Sludge, such as the use of particular fonts, colors, and design layouts, appears to use Cognitive Biases to make health-related information about the harms of alcohol difficult to access, and enhances exposure to misinformation. Nudge-type mechanisms also underlie AI mixed messages, in particular alternative causation arguments, which propose nonalcohol causes of alcohol harms. CONCLUSIONS: Alcohol industry CSR bodies use dark nudges and sludge, which utilize consumers' Cognitive Biases to promote mixed messages about alcohol harms and to undermine scientific evidence. Policymakers, practitioners, and the public need to be aware of how such techniques are used to nudge consumers toward industry misinformation. The revised typology presented in this article may help with the identification and further analysis of dark nudges and sludge.
Corinde E Wiers - One of the best experts on this subject based on the ideXlab platform.
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comparing three Cognitive Biases for alcohol cues in alcohol dependence
Alcohol and Alcoholism, 2016Co-Authors: Corinde E Wiers, Thomas E Gladwin, Vera U Ludwig, Sonja Gropper, Heiner Stuke, Christiane K GawronAbstract:Aims There is accumulating evidence that automatic processes play a large role in alcohol dependence, which may be related to alcohol craving and consumption. The aim of this study is to investigate associations between Cognitive Biases in alcohol-dependent patients, and how these measures relate to drinking behavior. Methods Thirty alcohol-dependent patients and 15 healthy controls (matched for age, intelligence and education; all male) completed three Cognitive bias tasks: the Implicit Association Test (IAT: alcohol-approach association), Approach Avoidance Task (AAT: alcohol approach bias) and Dot Probe Task (DPT: alcohol attentional bias). Task scores were compared between groups and correlated with each other, as well as with craving scores and drinking behavior. Results Patients with alcohol dependence showed stronger alcohol-approach associations on the IAT compared with controls, but there were no group differences for approach or attentional Biases. Within the patient group, the alcohol approach bias (AAT) correlated positively with the attend-alcohol attentional bias (DPT), but negatively with alcohol-approach associations (IAT). IAT scores were positively associated with lifetime alcohol intake. Conclusions This study demonstrates for the first time that alcohol-dependent patients have stronger alcohol-approach association scores on the IAT as compared to controls, and that this bias is associated with drinking behavior. Despite the absence of group differences for the approach and attentional Biases, the positive correlation between these Biases in alcoholics is in line with incentive salience models of addiction that propose that attentional and approach tendencies have a common underlying mechanism, distinct from that underlying alcohol-approach associations measured by the IAT. Short Summary The study investigates associations between Cognitive Biases involving alcohol cues. Patients with alcohol dependence showed stronger alcohol-approach associations on an Implicit Association Test than controls, but there were no group differences for approach or attentional Biases. Alcohol-approach and attentional bias correlated positively in the patient group.
Johannes Furnkranz - One of the best experts on this subject based on the ideXlab platform.
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a review of possible effects of Cognitive Biases on interpretation of rule based machine learning models
Artificial Intelligence, 2021Co-Authors: Tomas Kliegr, Stěpan Bahnik, Johannes FurnkranzAbstract:Abstract While the interpretability of machine learning models is often equated with their mere syntactic comprehensibility, we think that interpretability goes beyond that, and that human interpretability should also be investigated from the point of view of Cognitive science. The goal of this paper is to discuss to what extent Cognitive Biases may affect human understanding of interpretable machine learning models, in particular of logical rules discovered from data. Twenty Cognitive Biases are covered, as are possible debiasing techniques that can be adopted by designers of machine learning algorithms and software. Our review transfers results obtained in Cognitive psychology to the domain of machine learning, aiming to bridge the current gap between these two areas. It needs to be followed by empirical studies specifically focused on the machine learning domain.
Paul A Weller - One of the best experts on this subject based on the ideXlab platform.
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quantifying Cognitive Biases in analyst earnings forecasts
Journal of Financial Markets, 2006Co-Authors: Geoffrey C Friesen, Paul A WellerAbstract:This paper develops a formal model of analyst earnings forecasts that discriminates between rational behavior and that induced by Cognitive Biases. In the model, analysts are Bayesians who issue sequential forecasts that combine new information with the information contained in past forecasts. The model enables us to test for Cognitive Biases, and to quantify their magnitude. We estimate the model and find strong evidence that analysts are overconfident about the precision of their own information and also subject to Cognitive dissonance bias, but they are able to make corrections for bias in the forecasts of others. We show that our measure of overconfidence varies with book-to-market ratio in a way consistent with the findings of Daniel and Titman [1999. Market efficiency in an irrational world. Financial Analysts’ Journal 55, 28–40]. We also demonstrate the existence of these Biases in international data.
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quantifying Cognitive Biases in analyst earnings forecasts
Social Science Research Network, 2005Co-Authors: Geoffrey C Friesen, Paul A WellerAbstract:This paper develops a formal model of analyst earnings forecasts that discriminates between rational behavior and that induced by Cognitive Biases. In the model, analysts are Bayesians who issue sequential forecasts that combine new information with the information contained in past forecasts. The model enables us to test for Cognitive Biases, and to quantify their magnitude. We estimate the model and find strong evidence that analysts are overconfident about the precision of their own information and also subject to Cognitive dissonance bias. But they are able to make corrections for bias in the forecasts of others. We show that our measure of overconfidence varies with book-to-market ratio in a way consistent with the findings of Daniel and Titman (1999). We also demonstrate the existence of these Biases in international data.
Matt Field - One of the best experts on this subject based on the ideXlab platform.
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effects of a low dose of alcohol on Cognitive Biases and craving in heavy drinkers
Psychopharmacology, 2008Co-Authors: Reinout W Wiers, Tim M Schoenmakers, Matt FieldAbstract:Rationale Heavy alcohol drinking increases the incentive salience of alcohol-related cues. This leads to increased appetitive motivation to drink alcohol as measured by subjective craving and Cognitive Biases such as attentional bias and approach bias. Although these measures relate to the same construct, correlations between these variables are often very low. Alcohol consumption might not only increase different aspects of appetitive motivation, but also correlations between those aspects.
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craving and Cognitive Biases for alcohol cues in social drinkers
Alcohol and Alcoholism, 2005Co-Authors: Matt Field, Karin Mogg, Brendan P BradleyAbstract:Aims: To assess whether Cognitive Biases for drug-related cues are associated with subjective craving and behavioural indices of drug-seeking behaviour, as predicted by incentive models of addiction. Methods: Fifty social drinkers took part in a laboratory study in which their subjective craving and Cognitive Biases for alcohol cues were assessed, before they completed a progressive ratio operant task for alcohol (beer) reinforcement. Results: Social drinkers with high levels of alcohol craving at the beginning of the experiment had more pronounced attentional, approach, and evaluative Biases for alcohol cues, compared with those with low craving. There were also trends for the high craving group to show greater operant responding for beer reinforcement, but the latter findings were inconclusive, and no evidence was found of associations between the operant responding and Cognitive bias measures. Conclusions: The finding of a relationship between subjective craving and Cognitive Biases for alcohol cues is consistent with incentive models of addiction. Methodological factors may have obscured the predicted relationships between Cognitive bias and operant performance, such as the use of a specific reinforcer (beer) during the operant task, while a range of alcohol-related cues were used in the Cognitive bias tasks.