Collective human attitudes influenced by macro-forces that impact environmental issues are partially correlated to our behaviors for the good and the harm of the planet. In this thesis, I will explore how collective human attitudes contribute to pro-environmental behaviors, common and pre-existing frames of mind on major conservation dilemmas, and finally suggest future directions on how humans could be inclined to take on more environmental responsibility through an increase in human-environmental connectivity. It is found that humans are largely driven by institution structures, education, and social influence. In conclusion, more efforts should be placed to further analyze these structural incentives for pro-environmental behaviors and use them to make environmental stewardship more accessible for all people and diverse circumstances. This can be done by evaluating the human dimensions of what influences human attitudes and behaviors, how to use these forces to systematically influence pro-environmental choices, applying these structural forces to main conservation issues, and further incorporating moral discourse into the environmental research in order to appeal correctly to all aspects and perspectives. Only when human connectivity is understood in relation to the natural sciences will we be able to make positive change in the direction of a healthier Earth.

Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

Optimal foraging theory provides a suite of tools that model the best way that an animal will <br/>structure its searching and processing decisions in uncertain environments. It has been <br/>successful characterizing real patterns of animal decision making, thereby providing insights<br/>into why animals behave the way they do. However, it does not speak to how animals make<br/>decisions that tend to be adaptive. Using simulation studies, prior work has shown empirically<br/>that a simple decision-making heuristic tends to produce prey-choice behaviors that, on <br/>average, match the predicted behaviors of optimal foraging theory. That heuristic chooses<br/>to spend time processing an encountered prey item if that prey item's marginal rate of<br/>caloric gain (in calories per unit of processing time) is greater than the forager's<br/>current long-term rate of accumulated caloric gain (in calories per unit of total searching<br/>and processing time). Although this heuristic may seem intuitive, a rigorous mathematical<br/>argument for why it tends to produce the theorized optimal foraging theory behavior has<br/>not been developed. In this thesis, an analytical argument is given for why this<br/>simple decision-making heuristic is expected to realize the optimal performance<br/>predicted by optimal foraging theory. This theoretical guarantee not only provides support<br/>for why such a heuristic might be favored by natural selection, but it also provides<br/>support for why such a heuristic might a reliable tool for decision-making in autonomous<br/>engineered agents moving through theatres of uncertain rewards. Ultimately, this simple<br/>decision-making heuristic may provide a recipe for reinforcement learning in small robots<br/>with little computational capabilities.

This qualitative project was done as a way to learn more about the personal experiences of Asian American participants surrounding education and how it has impacted their identities, and questions how and if the model minority stereotype has impacted the Asian American student particiapnts. 14 participants were interviewed one-on-one to see if there were any patterns in values that their parents had pushed, and revealed that cultural expectations influence the participants’s educational choices, leading to self-regulation in regards to education. Because the shared trait of these participants are being current Asian American students in university at the time of their interviews, experiences range with how acculturated their parents are, the ethnic background of their families, and prior expectations with education.

As technological advancement increases and becomes more accessible to everyone around the world, many communities and support groups have begun to offer online options for their programs, whether it be a fitness program, online therapy group, or doctor’s appointment. With the COVID-19 pandemic affecting every country around the world, online and virtual communities have become more necessary than ever. Using personal experience from an online fitness community formed as a response to social isolation called“Barrett Healthy Minds, Healthy Bodies,” research was conducted to determine if online communities had the same effectiveness as in-person communities in reaching and maintaining individual health goals. Peer-reviewed scientific articles and research papers from many countries around the world were analyzed for demonstration and quantification of the efficacy of other online communities compared to in- person groups. In addition, the benefits and limitations of online communities were identified. Using all of the research and data collected, a novel fitness program was designed for implementation with an online synchronous group (OSG) and an online asynchronous option serving as a control to observe any differential adherence of participants to fitness goals. The proposed OSG consists of meetings and workouts through Zoom and is more interactive, even virtually. The control group had no interaction with others and completed the workouts alone. While this program was not distributed to the public and tested as part of this project, it was designed to be an optimized pilot program to test the impact of remote community engagement on goal attainment. It is predicted that the OSG would demonstrate improvement over control in better reaching goals and increased satisfaction with results. Scientific literature from a variety of disciplines discussed here informs this prediction.