title: Evaluating the Balloon Analogue Risk Task (BART) as a Predictor of Risk Taking in Adolescent and Adult Male Drivers.

authors: Gordon, Mark Adam

subjects: BART

abstract: Abstract

Young drivers between the ages of 15 and 24 are overrepresented in automobile crash statistics worldwide. Despite the common assumption that young drivers are more at risk of crashing than older drivers due to inexperience, age appears to be the main factor influencing crash risk, even after experience has been taken into account. It is possible that young drivers are involved in a high number of crashes because of their risk-taking tendencies. Accident involvement is not so much influenced by errors and lapses by the driver, but by the willingness to commit driving violations intentionally. However, studies that attempted to measure the risk-taking tendencies of drivers have so far used mainly self-report questionnaires, which are limited in their ability to predict real-world behaviour. This thesis used a new behavioural measure of risk-taking known as the Balloon Analogue Risk Task (BART). In this task, participants engage in computer simulation where a balloon is pumped in order to accumulate money, but when the balloon is pumped too high it explodes, and the money that could have been gained is lost. A group of 50 male drivers were the participants of this study, and these were separated into three age groups: adolescents, aged 16-17, young adults, aged 20-21, and older adults, aged 25 years and over. In addition to the BART, the participants answered a series of questionnaires that focused on risk-related constructs, such as impulsiveness and subjective risk assessment, as well as driving attitudes and intentions. The expectation was that younger drivers would be shown to have greater risk-taking tendencies than older drivers. The results showed that the BART showed no relationship with either driving attitude scores (apart from a small correlation with attitudes towards close following), or any of the self-reported measures of risk. The other self-report risk measures, however, showed many correlations with various aspects of driving attitudes and intentions. Over age groups, the level of impulsiveness was found to decrease, and the attitudes became less in favour of taking physical risks. Adolescents were also found to be more approving of using a cell phone while driving, and of overtaking in risky circumstances. They had greater intentions to commit violations in the future, and were more likely to get a thrill from driving. The failure of the BART to reveal any significant findings may have been because so far it has only been shown to correlate with self-reported real-world behaviour, and not so much with attitudes and risk-related constructs. The other suggestion of this thesis was that the BART does not simulate risk-taking in the truest sense because there are no specific negative consequences for risk taking, only the removal of a possible benefit. The finding of greater risk taking in adolescent drivers was discussed in relation to Risk Homeostasis Theory and Problem Behaviour Theory, with a focus on how age-related factors might influence driver risk taking. As further discussed, these age-related factors might include the effect of incomplete brain development, the motives for driving, and the lifestyle of the individual.

text: Evaluating the Balloon Analogue Risk Task (BART) as a Predictor of Risk Taking in Adolescent and Adult Male Drivers. Abstract

Young drivers between the ages of 15 and 24 are overrepresented in automobile crash statistics worldwide. Despite the common assumption that young drivers are more at risk of crashing than older drivers due to inexperience, age appears to be the main factor influencing crash risk, even after experience has been taken into account. It is possible that young drivers are involved in a high number of crashes because of their risk-taking tendencies. Accident involvement is not so much influenced by errors and lapses by the driver, but by the willingness to commit driving violations intentionally. However, studies that attempted to measure the risk-taking tendencies of drivers have so far used mainly self-report questionnaires, which are limited in their ability to predict real-world behaviour. This thesis used a new behavioural measure of risk-taking known as the Balloon Analogue Risk Task (BART). In this task, participants engage in computer simulation where a balloon is pumped in order to accumulate money, but when the balloon is pumped too high it explodes, and the money that could have been gained is lost. A group of 50 male drivers were the participants of this study, and these were separated into three age groups: adolescents, aged 16-17, young adults, aged 20-21, and older adults, aged 25 years and over. In addition to the BART, the participants answered a series of questionnaires that focused on risk-related constructs, such as impulsiveness and subjective risk assessment, as well as driving attitudes and intentions. The expectation was that younger drivers would be shown to have greater risk-taking tendencies than older drivers. The results showed that the BART showed no relationship with either driving attitude scores (apart from a small correlation with attitudes towards close following), or any of the self-reported measures of risk. The other self-report risk measures, however, showed many correlations with various aspects of driving attitudes and intentions. Over age groups, the level of impulsiveness was found to decrease, and the attitudes became less in favour of taking physical risks. Adolescents were also found to be more approving of using a cell phone while driving, and of overtaking in risky circumstances. They had greater intentions to commit violations in the future, and were more likely to get a thrill from driving. The failure of the BART to reveal any significant findings may have been because so far it has only been shown to correlate with self-reported real-world behaviour, and not so much with attitudes and risk-related constructs. The other suggestion of this thesis was that the BART does not simulate risk-taking in the truest sense because there are no specific negative consequences for risk taking, only the removal of a possible benefit. The finding of greater risk taking in adolescent drivers was discussed in relation to Risk Homeostasis Theory and Problem Behaviour Theory, with a focus on how age-related factors might influence driver risk taking. As further discussed, these age-related factors might include the effect of incomplete brain development, the motives for driving, and the lifestyle of the individual.

year: 2007

title: Machine Learning for PAC1D and SESE

authors: Kurz, Jason A.

abstract: This document outlines various machine learning approaches that were taken in an effort to surrogate numerical models Python Ablation Code 1-Dimension (PAC1D) and Scalable Effects Simulation Environment (SESE), with the ultimate objective of discovering the most efficient method for approximating SESE with sparse data utilization. The methods explored include; physics-informed neural networks, deep Galerkin method, deep mixed residual methods, operator network, deep operator network, Fourier neural operator, physics-informed Fourier neural operator, and physics-informed kernel neural operator. Many of the methods showed strengths and weaknesses in their performance, with the physics-informed kernel neural operator showing the most potential for approximating SESE’s behavior.

text: Machine Learning for PAC1D and SESE This document outlines various machine learning approaches that were taken in an effort to surrogate numerical models Python Ablation Code 1-Dimension (PAC1D) and Scalable Effects Simulation Environment (SESE), with the ultimate objective of discovering the most efficient method for approximating SESE with sparse data utilization. The methods explored include; physics-informed neural networks, deep Galerkin method, deep mixed residual methods, operator network, deep operator network, Fourier neural operator, physics-informed Fourier neural operator, and physics-informed kernel neural operator. Many of the methods showed strengths and weaknesses in their performance, with the physics-informed kernel neural operator showing the most potential for approximating SESE’s behavior.

year: 2023-04-03

title: Ngā takenga o te riri

authors: Murphy, Ēnoka

subjects: Pakanga

abstract: Ko te kaupapa ake o tēnei tuhinga, ko te whakawhāriki i ētahi kōrero mō te takenga mai o te riri, o te pakanga ki tā rā ngā kōrero a te Māori. Ko aua take mai rā kei ngā kōrero o tua whakarere, e hokia ana ki a Ranginui rāua ko Papatūānuku me tā rāua hūnuku. Ko tāna e pātai ake nei, nō hea ake te riri Māori, otirā, te pakanga a te Māori? Nō hea anō hoki āna tikanga? He aha ā ērā kōrero tauiratanga mai ki te toa o te Māori ki te whawhai. Ka mutu, ko te wāhi uaua pea o ngā pātai nei, ko te āhua ki ngā kōrero a te Māori kāore nei i te tapatahi mō te oroko hīmatanga. Ahakoa rā, tērā ōna rite, ōna rerekē. Kua tuhia nuitia te kaupapa o te oroko tīmatanga mai o te ao e ngā momo o te kaituhi Pākehā, ā, tērā anō hoki ko ngā tuhinga a te Māori ake. Inā te rerekē o ngā kōrero mō te oroko hīmatanga mai o te ao, tērā noa ētahi kōrero kua tīkina hei mātaitanga mō tēnei rangahau, mō tēnei tuhinga. Ka mutu, ko tā tēnei tuhinga, he whakaatu i te takenga mai o te riri, o te matawhāura Māori.

text: Ngā takenga o te riri Ko te kaupapa ake o tēnei tuhinga, ko te whakawhāriki i ētahi kōrero mō te takenga mai o te riri, o te pakanga ki tā rā ngā kōrero a te Māori. Ko aua take mai rā kei ngā kōrero o tua whakarere, e hokia ana ki a Ranginui rāua ko Papatūānuku me tā rāua hūnuku. Ko tāna e pātai ake nei, nō hea ake te riri Māori, otirā, te pakanga a te Māori? Nō hea anō hoki āna tikanga? He aha ā ērā kōrero tauiratanga mai ki te toa o te Māori ki te whawhai. Ka mutu, ko te wāhi uaua pea o ngā pātai nei, ko te āhua ki ngā kōrero a te Māori kāore nei i te tapatahi mō te oroko hīmatanga. Ahakoa rā, tērā ōna rite, ōna rerekē. Kua tuhia nuitia te kaupapa o te oroko tīmatanga mai o te ao e ngā momo o te kaituhi Pākehā, ā, tērā anō hoki ko ngā tuhinga a te Māori ake. Inā te rerekē o ngā kōrero mō te oroko hīmatanga mai o te ao, tērā noa ētahi kōrero kua tīkina hei mātaitanga mō tēnei rangahau, mō tēnei tuhinga. Ka mutu, ko tā tēnei tuhinga, he whakaatu i te takenga mai o te riri, o te matawhāura Māori.

year: 2017