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Andrew T Hayashi - One of the best experts on this subject based on the ideXlab platform.
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occasionally libertarian experimental evidence of self serving omission bias
Journal of Law Economics & Organization, 2013Co-Authors: Andrew T HayashiAbstract:People evaluate outcomes, in part, by how those outcomes came about and who caused them. For example, attitudes about the proper amount of redistributive taxation reflect beliefs about the Causal roles played by luck and human agency in creating the pre-tax income distribution. Causal Attribution, however, is a process both complicated and subject to bias. I generate individual-level data from a variation on the dictator game in which the participants' initial endowments are manipulated to identify one aspect of how people care about Causal Attribution. The data are inconsistent with models of preferences defined solely over outcomes and also with a general bias toward inaction. Subjects care independently, but conditionally, about the effects of their own actions and demonstrate a bias toward inaction only when it is in their self-interest. (JEL A12, A13, C91, D63) The Author 2013. Published by Oxford University Press on behalf of Yale University. All rights reserved. For Permissions, please email: journals.permissions@oup.com, Oxford University Press.
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occasionally libertarian experimental evidence of self serving omission bias
2012Co-Authors: Andrew T HayashiAbstract:People evaluate outcomes, in part, by how those outcomes came about and who caused them. For example, attitudes about the proper amount of redistributive taxation reflect beliefs about the Causal roles played by luck and human agency in creating the pre-tax income distribution. Causal Attribution, however, is a process both complicated and subject to bias. I generate individual-level data from a variation on the dictator game in which the participants’ initial endowments are manipulated to identify one aspect of how people care about Causal Attribution. The data are inconsistent with models of preferences defined solely over outcomes and also with a general bias toward inaction. Subjects care independently, but conditionally, about the effects of their own actions and demonstrate a bias toward inaction only when it is in their self-interest.
Benoit Mayer - One of the best experts on this subject based on the ideXlab platform.
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climate migration and the politics of Causal Attribution a case study in mongolia
Migration for Development, 2016Co-Authors: Benoit MayerAbstract:Migration is always multi-Causal. Ascribing a specific cause to migration, such as through the concept of ‘climate migration’, participates consequently to a political exercise – a play of shade and light where attention is focused on the responsibilities of certain actors, rather than others. This is the case, this article argues, regarding internal migration in Mongolia, whereby, during the last two decades, nomadic or semi-nomadic herders as well as inhabitants from small urban centres come to settle in insalubrious suburbs of the capital, Ulaanbaatar. The Mongolian authorities are keen to highlight the changing environmental conditions that can be traced to climate change: a change in precipitation patterns and an increase in average temperatures contribute to cause large loss of livestock during harsh winters (dzud). Yet, a multitude of other factors concurrently influence the migratory behaviour of Mongolia’s nomads: unregulated and unsustainable pastoral practices, the insufficient provision of bas...
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climate migration and the politics of Causal Attribution a case study in mongolia
Social Science Research Network, 2015Co-Authors: Benoit MayerAbstract:Migration is always multi-Causal. Ascribing a specific cause to migration, such as through the concept of “climate migration,” participates consequently to a political exercise – a play of shade and light where attention is focused on the responsibilities of certain actors rather than others. This is the case, this article argues, regarding internal migration in Mongolia, whereby, during the last two decades, nomadic or semi-nomadic herders as well as inhabitants from small urban centres come to settle in insalubrious suburbs of the capital, Ulaanbaatar. The Mongolian authorities are keen to highlight changing environmental conditions that can be traced to climate change: a change in precipitation patterns and an increase of average temperatures contribute to cause large loss of livestock during harsh winters (dzud). Yet, a multitude of other factors concurrently influence the migratory behaviour of Mongolia’s nomads: unregulated and unsustainable pastoral practices, the insufficient provision of basic and support services in the countryside, or, more generally, the lack of public support to the agricultural sector. Identifying concurring causes of migration suggests alternative response measures, and this article argues that Mongolia should urgently rectify its development policies to provide a room for each of its citizens.
Ruth Ottman - One of the best experts on this subject based on the ideXlab platform.
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Depression and genetic Causal Attribution of epilepsy in multiplex epilepsy families
Epilepsia, 2016Co-Authors: Shawn T. Sorge, Dale C. Hesdorffer, Jo C. Phelan, Melodie R. Winawer, Sara Shostak, Jeffrey D. Goldsmith, Wendy K. Chung, Ruth OttmanAbstract:SummaryObjectives Rapid advances in genetic research and increased use of genetic testing have increased the emphasis on genetic causes of epilepsy in patient encounters. Research in other disorders suggests that genetic Causal Attributions can influence patients' psychological responses and coping strategies, but little is known about how epilepsy patients and their relatives will respond to genetic Attributions of epilepsy. We investigated the possibility that among members of families containing multiple individuals with epilepsy, depression, the most frequent psychiatric comorbidity in the epilepsies, might be related to the perception that epilepsy has a genetic cause. Methods A self-administered survey was completed by 417 individuals in 104 families averaging 4 individuals with epilepsy per family. Current depression was measured with the Patient Health Questionnaire. Genetic Causal Attribution was assessed by three questions addressing the following: perceived likelihood of having an epilepsy-related mutation, perceived role of genetics in causing epilepsy in the family, and (in individuals with epilepsy) perceived influence of genetics in causing the individual's epilepsy. Relatives without epilepsy were asked about their perceived chance of developing epilepsy in the future, compared with the average person. Results Prevalence of current depression was 14.8% in 182 individuals with epilepsy, 6.5% in 184 biologic relatives without epilepsy, and 3.9% in 51 individuals married into the families. Among individuals with epilepsy, depression was unrelated to genetic Attribution. Among biologic relatives without epilepsy, however, prevalence of depression increased with increasing perceived chance of having an epilepsy-related mutation (p = 0.02). This association was not mediated by perceived future epilepsy risk among relatives without epilepsy. Significance Depression is associated with perceived likelihood of carrying an epilepsy-related mutation among individuals without epilepsy in families containing multiple affected individuals. This association should be considered when addressing mental health issues in such families.
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genetic Causal Attribution of epilepsy and its implications for felt stigma
Epilepsia, 2015Co-Authors: Maya Sabatello, Shawn T. Sorge, Dale C. Hesdorffer, Jo C. Phelan, Melodie R. Winawer, Sara Shostak, Wendy K. Chung, Jeff Goldsmith, Ruth OttmanAbstract:Summary Objective Research in other disorders suggests that genetic Causal Attribution of epilepsy might be associated with increased stigma. We investigated this hypothesis in a unique sample of families containing multiple individuals with epilepsy. Methods One hundred eighty-one people with epilepsy and 178 biologic relatives without epilepsy completed a self-administered survey. In people with epilepsy, felt stigma was assessed through the Epilepsy Stigma Scale (ESS), scored 1–7, with higher scores indicating more stigma and >4 indicating some felt stigma. Felt stigma related to having epilepsy in the family was assessed through the Family Epilepsy Stigma Scale (FESS), created by replacing “epilepsy” with “epilepsy in my family” in each ESS item. Genetic Attribution was assessed through participants' perceptions of the (1) role of genetics in causing epilepsy in the family, (2) chance they had an epilepsy-related mutation, and (3) (in people with epilepsy) influence of genetics in causing their epilepsy. Results Among people with epilepsy, 22% met criteria for felt stigma (ESS score >4). Scores were increased among individuals who were aged ≥60 years, were unemployed, reported epilepsy-related discrimination, or had seizures within the last year or >100 seizures in their lifetime. Adjusting for other variables, ESS scores in people with epilepsy were significantly higher among those who perceived genetics played a “medium” or “big” role in causing epilepsy in the family than in others (3.4 vs. 2.7, p = 0.025). Only 4% of relatives without epilepsy had felt stigma. Scores in relatives were unrelated to genetic Attribution. Significance In these unusual families, predictors of felt stigma in individuals with epilepsy are similar to those in other studies, and stigma levels are low in relatives without epilepsy. Felt stigma may be increased in people with epilepsy who believe epilepsy in the family has a genetic cause, emphasizing the need for sensitive communication about genetics.
James P Bagrow - One of the best experts on this subject based on the ideXlab platform.
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efficient crowd exploration of large networks the case of Causal Attribution
Proceedings of the ACM on Human-Computer Interaction, 2018Co-Authors: Daniel Berenberg, James P BagrowAbstract:Accurately and efficiently crowdsourcing complex, open-ended tasks can be difficult, as crowd participants tend to favor short, repetitive "microtasks". We study the crowdsourcing of large networks where the crowd provides the network topology via microtasks. Crowds can explore many types of social and information networks, but we focus on the network of Causal Attributions, an important network that signifies cause-and-effect relationships. We conduct experiments on Amazon Mechanical Turk (AMT) testing how workers can propose and validate individual Causal relationships and introduce a method for independent crowd workers to explore large networks. The core of the method, Iterative Pathway Refinement, is a theoretically-principled mechanism for efficient exploration via microtasks. We evaluate the method using synthetic networks and apply it on AMT to extract a large-scale Causal Attribution network. Worker interactions reveal important characteristics of Causal perception and the generated network data can help improve our understanding of Causality and Causal inference.
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efficient crowd exploration of large networks the case of Causal Attribution
arXiv: Human-Computer Interaction, 2018Co-Authors: Daniel Berenberg, James P BagrowAbstract:Accurately and efficiently crowdsourcing complex, open-ended tasks can be difficult, as crowd participants tend to favor short, repetitive "microtasks". We study the crowdsourcing of large networks where the crowd provides the network topology via microtasks. Crowds can explore many types of social and information networks, but we focus on the network of Causal Attributions, an important network that signifies cause-and-effect relationships. We conduct experiments on Amazon Mechanical Turk (AMT) testing how workers propose and validate individual Causal relationships and introduce a method for independent crowd workers to explore large networks. The core of the method, Iterative Pathway Refinement, is a theoretically-principled mechanism for efficient exploration via microtasks. We evaluate the method using synthetic networks and apply it on AMT to extract a large-scale Causal Attribution network, then investigate the structure of this network as well as the activity patterns and efficiency of the workers who constructed this network. Worker interactions reveal important characteristics of Causal perception and the network data they generate can improve our understanding of Causality and Causal inference.
Guilherme Borges - One of the best experts on this subject based on the ideXlab platform.
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multi level analysis of Causal Attribution of injury to alcohol and modifying effects data from two international emergency room projects
Drug and Alcohol Dependence, 2006Co-Authors: Cheryl J Cherpitel, Jason Bond, Guilherme Borges, Robin Room, Vladimir Poznyak, Wei HaoAbstract:Although alcohol consumption and injury has received a great deal of attention in the literature, less is known about patient's Causal Attribution of the injury event to their drinking or factors which modify Attribution. Hierarchical linear modeling is used to analyze the relationships of the volume of alcohol consumed prior to injury and feeling drunk at the time of the event with Causal Attribution, as well as the association of aggregate individual-level and socio-cultural variables on these relationships. Data analyzed are from 1955 ER patients who reported drinking prior to injury included in 35 ERs from 24 studies covering 15 countries from the combined Emergency Room Collaborative Alcohol Analysis Project (ERCAAP) and the WHO Collaborative Study on Alcohol and Injuries. Half of those patients drinking prior to injury attributed a Causal association of their injury with alcohol consumption, but the rate of Causal Attribution varied significantly across studies. When controlling for gender and age, the volume of alcohol consumed and feeling drunk (controlling for volume) were both significantly predictive of Attribution and this did not vary across studies. Those who drink at least weekly were less likely to attribute Causality at a low volume level, but more likely at high volume levels than less frequent drinkers. Attribution of Causality was also less likely at low volume levels in those societies with low detrimental drinking patterns, but more likely at high volume levels or when feeling drunk compared to societies with high detrimental drinking patterns. These findings have important implications for brief intervention in the ER if motivation to change drinking behavior is greater among those attributing a Causal association of their drinking with injury.
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the Causal Attribution of injury to alcohol consumption a cross national meta analysis from the emergency room collaborative alcohol analysis project
Alcoholism: Clinical and Experimental Research, 2003Co-Authors: Cheryl J Cherpitel, Jason Bond, Guilherme BorgesAbstract:Background: Whereas a substantial literature exists documenting the association of alcohol and injuries, Causal associations are less well established. Methods: The relationship of drinking-in-the-event variables with attributing a Causal association of alcohol consumption and the injury event was examined by using meta-analysis across 13 emergency room studies from 8 countries included in the Emergency Room Collaborative Alcohol Analysis Project. Results: Pooled odds ratios for both log-transformed blood alcohol concentration at the time of the emergency room visit and the amount of alcohol consumed in the 6 hr before injury were positively predictive (1.19 and 1.80, respectively) and heterogeneous across studies. Effect size changed little when age and gender were controlled. When stratifying on reporting five or more drinks on an occasion during the last year (5+ yearly drinkers), the amount consumed was positively predictive of reporting a casual association of drinking and injury only for 5+ yearly drinkers. The effect size of feeling drunk at the time of injury, controlling for the amount of alcohol consumed, was positively predictive (2.04) but heterogeneous across studies. Meta-analysis regression found the level to which alcohol is consumed in a detrimental pattern to be a significant predictor of blood alcohol concentration, and of the amount consumed and feeling drunk at the time of injury, on Causal Attribution, with a lower detrimental pattern level with a larger effect size. Conclusions: The association of acute use of alcohol on Causal Attribution may be affected by chronic use to some extent, but this association is negatively affected by the degree to which a society exhibits harmful drinking patterns.