Browsing by Author "Lin, Alan"

How has the COVID-19 pandemic shaped the college experience of an entire generation of students? Across the United States, a natural experiment has been taking place at universities and colleges as they each constructed varying COVID-10 policies. This paper attempts to make sense of this natural experiment by means of a nationwide survey and regression analysis to shed light on the question of what universities did to respond to COVID-19, what students at those institutions thought of those responses, and why. It investigates this previously unexplored aspect of the COVID-19 pandemic by contributing an original data set including information on the public health policies implemented by over 600 universities, colleges, and other institutions of higher learning during the early months of 2022 to respond to COVID-19, as well as data from over 1,000 students on their attitudes towards their institutions9 policies. I ûrst summarize what early research tells us about higher education in the era of COVID-19. Then, compiling survey responses, I introduce data on the prevalence of individual components of COVID-19 policies at these institutions. Combining these characteristics into a composite index, which I call the Institution Stringency Index (ISI), I analyze the impact of the ISI on student attitudes towards institutions, as well as determinants of the ISI policy components. While this paper is a limited initial foray into the policy area of COVID-19 policy in higher education, it points to important policy implications for potential future circumstances such as a new variant of COVID-19.

The Steam Community Market represents an increasingly complex digital market where users participate in the buying and selling of in-game items. Therefore, understanding how the Steam Community Market works may produce insights into the functioning of other digital markets that exist in areas not only in the world of gaming, as well as elucidate the psychology of a gamer or participant in such market.

This thesis aims to shine some light on the inner workings of the Steam Community Market by tackling the question of how items are priced on the Steam Community Market. Specifically though the lens of Counter-Strike 2 and its items, linear (OLS, Ridge, LASSO, Elastic Net) and non-linear (Polynomial, Kernel Ridge) models are used to better understand the modeling and predicting of item pricing based on their inherent features.

Through the process of modeling and predicting, the relationship between features and price prove to be more complex than a simple linear relationship. We find that modeling these relationships of item pricing based on features give insight into what features are valued more than others, which can help model prices of future items released onto the Steam Community Market as well as price current items’ sensitivity to different stimuli.