Barrett

Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.

Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.

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Preferences for Group Risk and Inequality

Description
Economists, political philosophers, and others have often characterized social preferences regarding inequality by imagining a hypothetical choice of distributions behind "a veil of ignorance". Recent behavioral economics work has shown that subjects care about equality of outcomes, and are willing to sacrifice, in experimental contexts, some amount of personal gain…

Economists, political philosophers, and others have often characterized social preferences regarding inequality by imagining a hypothetical choice of distributions behind "a veil of ignorance". Recent behavioral economics work has shown that subjects care about equality of outcomes, and are willing to sacrifice, in experimental contexts, some amount of personal gain in order to achieve greater equality. We review some of this literature and then conduct an experiment of our own, comparing subjects' choices in two risky situations, one being a choice for a purely individualized lottery for themselves, and the other a choice among possible distributions to members of a randomly selected group. We find that choosing in the group situation makes subjects significantly more risk averse than when choosing an individual lottery. This supports the hypothesis that an additional preference for equality exists alongside ordinary risk aversion, and that in a hypothetical "veil of ignorance" scenario, such preferences may make subjects significantly more averse to unequal distributions of rewards than can be explained by risk aversion alone.
ContributorsTheisen, Alexander Scott (Co-author) / McMullin, Caitlin (Co-author) / Li, Marilyn (Co-author) / DeSerpa, Allan (Thesis director) / Schlee, Edward (Committee member) / Baldwin, Marjorie (Committee member) / Barrett, The Honors College (Contributor) / Department of Economics (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor)
Created2014-05
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Risk-reducing Infrastructure: How Much is too Much?

Description

This paper examines infrastructure spending in a model economy. Infrastructure is subdivided into two types: one that makes future production more efficient, and another that decreases the risk of devastation to the future economy. We call the first type base infrastructure, and the second type risk-reducing infrastructure. Our model assumes…

This paper examines infrastructure spending in a model economy. Infrastructure is subdivided into two types: one that makes future production more efficient, and another that decreases the risk of devastation to the future economy. We call the first type base infrastructure, and the second type risk-reducing infrastructure. Our model assumes that a single representative individual makes all the decisions within a society and optimizes their own total utility over the present and future. We then calibrate an aggregate economic, two-period model to identify the optimal allocation of today’s output into consumption, base infrastructure, and risk-reducing infrastructure. This model finds that many governments can make substantive improvements to the happiness of their citizens by investing significantly more into risk-reducing infrastructure.
ContributorsFink, Justin (Co-author) / Fuller, John "Jack" (Co-author) / Prescott, Edward (Thesis director) / Millington, Matthew (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05

The Impacts of Autonomous Vehicles With a Focus on Collision Coverage

Description
This report describes the technology, benefits, and deployment of autonomous vehicles and how they are expected to impact the insurance industry, specifically collision coverage policies. A pure premium trend analysis is done to come up with a realistic prediction of how the frequency and severity of vehicle collisions will change…

This report describes the technology, benefits, and deployment of autonomous vehicles and how they are expected to impact the insurance industry, specifically collision coverage policies. A pure premium trend analysis is done to come up with a realistic prediction of how the frequency and severity of vehicle collisions will change over time. Two additional scenarios are done to address the fact that there is still uncertainty surrounding the timing of the implementation of AVs. Lastly, the risks that come with AVs are discussed along with potential risk mitigation strategies.
ContributorsMullenmeister, Morgan (Author) / Zhou, Hongjuan (Thesis director) / Milovanovic, Jelena (Committee member) / Zicarelli, John (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of International Letters and Cultures (Contributor)
Created2022-12

Overview of Autonomous Vehicles with an Analysis of the Impact on Liability Insurance

DescriptionThis thesis explores the progress of autonomous vehicle technology, regulation, and deployment. It also studies how autonomous vehicles will affect auto insurance, in particular how liability coverage will change and how liability premiums for autonomous vehicles will be different from premiums for traditional vehicles.
ContributorsLaw, Madelyn (Author) / Zhou, Hongjuan (Thesis director) / Milovanovic, Jelena (Committee member) / Zicarelli, John (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-12
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Artificial Intelligence with Graph Neural Networks Applied to a Risk-like Board Game

Description
This project aspires to develop an AI capable of playing on a variety of maps in a Risk-like board game. While AI has been successfully applied to many other board games, such as Chess and Go, most research is confined to a single board and is inflexible to topological changes.…

This project aspires to develop an AI capable of playing on a variety of maps in a Risk-like board game. While AI has been successfully applied to many other board games, such as Chess and Go, most research is confined to a single board and is inflexible to topological changes. Further, almost all of these games are played on a rectangular grid. Contrarily, this project develops an AI player, referred to as GG-net, to play the online strategy game Warzone, which is based on the classic board game Risk. Warzone is played on a wide variety of irregularly shaped maps. Prior research has struggled to create an effective AI for Risk-like games due to the immense branching factor. The most successful attempts tended to rely on manually restricting the set of actions the AI considered while also engineering useful features for the AI to consider. GG-net uses no human knowledge, but rather a genetic algorithm combined with a graph neural network. Together, these methods allow GG-net to perform competitively across a multitude of maps. GG-net outperformed the built-in rule-based AI by 413 Elo (representing an 80.7% chance of winning) and an approach based on AlphaZero using graph neural networks by 304 Elo (representing a 74.2% chance of winning). This same advantage holds across both seen and unseen maps. GG-net appears to be a strong opponent on both small and medium maps, however, on large maps with hundreds of territories, inefficiencies in GG-net become more significant and GG-net struggles against the rule-based approach. Overall, GG-net was able to successfully learn the game and generalize across maps of a similar size, albeit further work is required for GG-net to become more successful on large maps.
ContributorsBauer, Andrew (Author) / Yang, Yezhou (Thesis director) / Harrison, Blake (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05