OU - Emerging Scholars

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https://hdl.handle.net/11244/329114
The OU - Emerging Scholars collection showcases exemplary undergraduate student scholarship, such as published articles, papers that have won the University Libraries’ Undergraduate Research Award, works arising from research fellowships, etc.

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    UndergraduateOpen Access
    Exploring The Architecture on the Campus of the University of Oklahoma
    (2020) Connor Hopper
    As the title suggests, this paper presents itself as more than merely a historical account of the evolving architecture at the University of Oklahoma. Indeed, I have spent the past few months diving into how these buildings came about, the transition from the Collegiate Gothic Style into Modern Architecture, as well as the abstract implications of what the architecture says about this university.
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    UndergraduateOpen Access
    On Reinforcement Learning, Nurturing, and the Evolution of Risk Neutral
    (2020) Kevin Robb
    Reinforcement learning depends on agents being learning individuals, and when agents rely on their instincts rather than gathering data and acting accordingly, the population tends to be less successful than a true RL population. "Riskiness" is the elementary metric for determining how willing to rely on learning an individual or a population is. With a high learning parameter, as we denote riskiness in this paper, agents find the safest option and seldom deviate from it, essentially using learning to become a non-learning individual. With a low learning rate, agents ignore recency entirely and seek out the highest reward, regardless of the risk. We attempt in this paper to evolve this "risk neutrality" in a population by adding a safe exploration nurturing period during which agents are free to explore without consequence. We discovered the environmental conditions necessary for our hypotheses to be mostly satisfied and found that nurturing enables agents to distinguish between two different risky options to evolve risk neutrality. Too long of a nurturing period causes the evolution to waver before settling on a path with essentially random results, while a short nurturing period causes a successful evolution of risk neutrality. The non-nurturing case evolves risk aversion by default as we expected from a reinforcement learning system, because agents are unable to distinguish between the good risk and bad risk, so they decide to avoid risks altogether.
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    UndergraduateOpen Access
    Residential Segregation: A Story of Health Inadequacies
    (12/4/18) Ananya Bhaktaram
    The intentional segregation of metropolitan areas in the United States during the twentieth century has resulted in rising health disparities in low-income minorities today. Contemporary medical practices like collecting health data by race and not by socioeconomic status obfuscates the problem. OneÕs geography of opportunity, meaning the opportunities one is afforded based on where you live has direct effects on your prospective health. Low income minorities are faced with greater adverse risk because they are more likely to be found in a double jeopardy situation where they are simultaneously impoverished and living in a bad neighborhood. Additionally, treatment within the healthcare system itself is often times sub-par.