For centuries, religion has been a part of people's lives, impacting their well-being. Well-being includes both positive and negative emotions that make people feel satisfied. Religion can affect how people handle their physical health, social behavior, support, and stress. Interestingly, more people have been abandoning religion since 1990, even though it has positive effects on well-being. In this study, I sought to understand the extent to which leaving religion is associated with all aspects of well-being, including spiritual well-being. I asked 606 Arizona State University undergraduates to rate their well-being from two different time periods in their life. Participants were first asked if they were currently religious or nonreligious;135 said they had left/changed religions. If participants indicated that they did not leave/change religions, they were asked about their well-being before and after starting at ASU. I examined perceived change in well-being to understand how religion can influence many aspects of an individual’s life. Leaving or changing religion was positively associated with well-being. Those who reported leaving religion reported better environmental, emotional, social, educational, occupational, and spiritual well-being. In the control group (ASU), educational and spiritual well-being had a significant increase, which is interesting. The results showed that changing/leaving religion had a positive association on well-being.

Autonomous Vehicles (AVs), or self-driving cars, are poised to have an enormous impact on the automotive industry and road transportation. While advances have been made towards the development of safe, competent autonomous vehicles, there has been inadequate attention to the control of autonomous vehicles in unanticipated situations, such as imminent crashes. Even if autonomous vehicles follow all safety measures, accidents are inevitable, and humans must trust autonomous vehicles to respond appropriately in such scenarios. It is not plausible to program autonomous vehicles with a set of rules to tackle every possible crash scenario. Instead, a possible approach is to align their decision-making capabilities with the moral priorities, values, and social motivations of trustworthy human drivers.Toward this end, this thesis contributes a simulation framework for collecting, analyzing, and replicating human driving behaviors in a variety of scenarios, including imminent crashes. Four driving scenarios in an urban traffic environment were designed in the CARLA driving simulator platform, in which simulated cars can either drive autonomously or be driven by a user via a steering wheel and pedals. These included three unavoidable crash scenarios, representing classic trolley-problem ethical dilemmas, and a scenario in which a car must be driven through a school zone, in order to examine driver prioritization of reaching a destination versus ensuring safety. Sample human driving data in CARLA was logged from the simulated car’s sensors, including the LiDAR, IMU and camera. In order to reproduce human driving behaviors in a simulated vehicle, it is necessary for the AV to be able to identify objects in the environment and evaluate the volume of their bounding boxes for prediction and planning. An object detection method was used that processes LiDAR point cloud data using the PointNet neural network architecture, analyzes RGB images via transfer learning using the Xception convolutional neural network architecture, and fuses the outputs of these two networks. This method was trained and tested on both the KITTI Vision Benchmark Suite dataset and a virtual dataset exclusively generated from CARLA. When applied to the KITTI dataset, the object detection method achieved an average classification accuracy of 96.72% and an average Intersection over Union (IoU) of 0.72, where the IoU metric compares predicted bounding boxes to those used for training.

Autonomous vehicle technology has been evolving for years since the Automated Highway System Project. However, this technology has been under increased scrutiny ever since an autonomous vehicle killed Elaine Herzberg, who was crossing the street in Tempe, Arizona in March 2018. Recent tests of autonomous vehicles on public roads have faced opposition from nearby residents. Before these vehicles are widely deployed, it is imperative that the general public trusts them. For this, the vehicles must be able to identify objects in their surroundings and demonstrate the ability to follow traffic rules while making decisions with human-like moral integrity when confronted with an ethical dilemma, such as an unavoidable crash that will injure either a pedestrian or the passenger.

Testing autonomous vehicles in real-world scenarios would pose a threat to people and property alike. A safe alternative is to simulate these scenarios and test to ensure that the resulting programs can work in real-world scenarios. Moreover, in order to detect a moral dilemma situation quickly, the vehicle should be able to identify objects in real-time while driving. Toward this end, this thesis investigates the use of cross-platform training for neural networks that perform visual identification of common objects in driving scenarios. Here, the object detection algorithm Faster R-CNN is used. The hypothesis is that it is possible to train a neural network model to detect objects from two different domains, simulated or physical, using transfer learning. As a proof of concept, an object detection model is trained on image datasets extracted from CARLA, a virtual driving environment, via transfer learning. After bringing the total loss factor to 0.4, the model is evaluated with an IoU metric. It is determined that the model has a precision of 100% and 75% for vehicles and traffic lights respectively. The recall is found to be 84.62% and 75% for the same. It is also shown that this model can detect the same classes of objects from other virtual environments and real-world images. Further modifications to the algorithm that may be required to improve performance are discussed as future work.

The Autonomous Vehicle (AV), also known as self-driving car, promises to be a game changer for the transportation industry. This technology is predicted to drastically reduce the number of traffic fatalities due to human error [21].

However, road driving at any reasonable speed involves some risks. Therefore, even with high-tech AV algorithms and sophisticated sensors, there may be unavoidable crashes due to imperfection of the AV systems, or unexpected encounters with wildlife, children and pedestrians. Whenever there is a risk involved, there is the need for an ethical decision to be made [33].

While ethical and moral decision-making in humans has long been studied by experts, the advent of artificial intelligence (AI) also calls for machine ethics. To study the different moral and ethical decisions made by humans, experts may use the Trolley Problem [34], which is a scenario where one must pull a switch near a trolley track to redirect the trolley to kill one person on the track or do nothing, which will result in the deaths of five people. While it is important to take into account the input of members of a society and perform studies to understand how humans crash during unavoidable accidents to help program moral and ethical decision-making into self-driving cars, using the classical trolley problem is not ideal, as it is unrealistic and does not represent moral situations that people face in the real world.

This work seeks to increase the realism of the classical trolley problem for use in studies on moral and ethical decision-making by simulating realistic driving conditions in an immersive virtual environment with unavoidable crash scenarios, to investigate how drivers crash during these scenarios. Chapter 1 gives an in-depth background into autonomous vehicles and relevant ethical and moral problems; Chapter 2 describes current state-of-the-art online tools and simulators that were developed to study moral decision-making during unavoidable crashes. Chapters 3 focuses on building the simulator and the design of the crash scenarios. Chapter 4 describes human subjects experiments that were conducted with the simulator and their results, and Chapter 5 provides conclusions and avenues for future work.

In this honors thesis certain religious and moral beliefs were analyzed and used to predict active euthanasia acceptance for Christians in the United States. The factors used to predict euthanasia acceptance for a suffering, terminally ill individual were religiosity, empathy, God representations, and attitudes toward God. Negative correlations were observed between acceptance of euthanasia and religiosity, empathy, positive attitudes toward God, and a benevolent God representation. Positive correlations were observed between acceptance of euthanasia and negative attitudes toward God, whereas there was no significant correlation between support for euthanasia and an authoritarian God representation. When acceptance of euthanasia was regressed on these independent variables, religiosity was the most significant predictor of acceptance. It was concluded that highly religious Christians may tend to prioritize their religious teachings over their instinctual empathy for a suffering person because prematurely ending a patient’s life interferes with God’s will and reduces his role in worldly affairs.

This research examined the influence that Christian and Hindu religious beliefs have on environmentalism; specifically, whether beliefs that one would return to this earth after death (i.e., a belief in reincarnation) and how the world might end may explain more positive attitudes toward the environment. Participants were 533 self-identified Christians and Hindus in the United States and India who completed an online survey assessing religiosity, positive attitudes towards environmentalism, afterlife beliefs, and eschatological beliefs. Christians showed significantly lower ratings of environmentalism compared with Hindus. There were also significant negative differences found based on beliefs about heaven, eschatology beliefs, and increased religiosity in Christians, and significant positive differences found based on reincarnation, eschatology beliefs, and increased religiosity in Hindus. Overall, these results suggest that Christians are less likely to have positive attitudes toward environmentalism compared with Hindus, and that beliefs about the afterlife and the end of the world were significant predictors of environmentalist attitudes.

Gift-giving economies are economic models that freely give resources rather than barter for them or purchase them from market. Need-based transfers fit into this economic model by freely giving resources on the basis of need, provided the giver can spare the resources. The Maasai are an East African pastoral tribe that practices need-based transfers through a tradition they call osotua. If they have a partner with an established osotua relationship, then they will give any amount of cattle that partner request, provided they can spare the cattle. Cheating each other is unheard of in this tradition, but for this simulation I am introducing cheating into this economic model through feigning need. If a cheater is not in need, they will act like they are in need. If they are in need, then the cheater will request more cattle than what they need to survive. I am testing two different responses to cheating: walking-away and punishing. In the walk-away condition, the victim ends their osotua partnership and establishes a new one. In the punishment condition, a portion of the cheater's stolen cattle is destroyed.

Perceptions of the self differ between cultures, generally between those cultures in the West and East. Some of the ways that these individuals from these cultures may differ are in their self-construal, their collectivist and individualist tendencies, and how they perceive control in their lives. The current study proposes that some of these differences are influenced by different concepts individuals hold regarding the "soul", or inner self. These concepts may be promoted by the different religious beliefs prominent in different regions. The Soul Perception Index, being developed through this study, measures belief in multiple souls, a universal soul, a single soul, or no soul. It was predicted that a belief in a single soul will correlate with an individual view of the self (individualism, independent self-construal, internal locus of control), and a universal or multi-soul belief will correlate with an interdependent view of the self (collectivism, interdependent self-construal, and external locus of control). We found that these variables did not significantly differ in their relationships with soul belief. However, Indian Hindu participants and Chinese participants seemed to score highly on all self-view variables and all soul perception types indicating that individuals from these cultures may be more predisposed to hold opposing beliefs simultaneously while US Christians are not.

Website usage on both smartphone and tablet devices is rapidly increasing. Website success hinges largely upon how well information on the site is recalled and perceived. The purpose of this study is to explore the question of whether or not the differences in display size and resolution of smartphone versus tablet devices affect the recall of website information. I hypothesize that tablets will produce greater website recall than smartphones, due to their larger screen size and higher resolution which may reduce cognitive strain. During the study, participants viewed a sample website for two minutes on either an iPhone or iPad, and then participated in a brief 20 question memory test to evaluate how well they remembered the website information. Although test scores for the iPad users were about one test point higher than test scores for the iPhone users, the difference was not statistically significant. However, the study was limited by the low sample size (n = 94). This indicates that further research may find that tablets indeed allow for increased recall of certain kinds of website content.