Barrett, The Honors College Thesis/Creative Project Collection
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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Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis examines the impact of price changes of select microprocessors on the market share and 5-year gross profit net present values of Company X in the networking market through a multi-step analysis. The networking market includes segments including media processing, cloud services, security, routers & switches, and access points. For this thesis our team focused on the routers & switches, as well as the security segments. Company X wants to capitalize on the expected growth of the networking market as it transitions to its fifth generation (henceforth referred to as 5G) by positioning itself favorably in its customers eyes through high quality products offered at competitive prices. Our team performed a quantitative analysis of benchmark data to measure the performances of Company X's products against those of its competitors. We collected this data from third party computer reviewers, as well as the published reports of Company X and its competitors. Through the use of a preference matrix, we then normalized this performance data to adjust for different scales. In order to provide a well-rounded analysis, we adjusted these normalized performances for power consumption (using thermal design power as a proxy) as well as price. We believe these adjusted performances are more valuable than raw benchmark data, as they appeal to the demands of price-sensitive customers. Based on these comparisons, our team was able to assess price changes for their market and discounted financial impact on Company X. Our findings challenge the current pricing of one of the two products being analyzed and suggests a 9% decrease in the price of said product. This recommendation most effectively positions Company X for the development of 5G by offering the best balance of market share and NPV.
ContributorsArias, Stephen (Co-author) / Masson, Taylor (Co-author) / McCall, Kyle (Co-author) / Dimitroff, Alex (Co-author) / Hardy, Sebastian (Co-author) / Simonson, Mark (Thesis director) / Haller, Marcie (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis takes the form of a market research report with the goal of analyzing the implications of the United Kingdom (UK) leaving the European Union (EU) (known as “Brexit”) on London’s office commercial real estate market. The ultimate goal of this report is to make a prediction, firmly grounded in quantitative and qualitative research conducted over the past several months, as to the direction of London’s commercial real estate market going forward (post-Brexit). Within the commercial real estate sector, this paper narrows its focus to the office segment of the London market.
Understanding the political landscape is crucial to formulating a reasonable prediction as to the future of the London market. Aside from research reports and articles, our main insights into the political direction of Brexit come from our recordings from meetings in March of 2017 with two high-ranking members of Parliament and one member of the House of Lords—all of whom are members of the Tory Party (the meetings being held under the condition of anonymity). The below analysis will be followed by a discussion of the economics of Brexit, primarily focusing on the economic risks and uncertainties which have emerged after the vote, and which currently exist today. Such risks include the UK losing its financial passporting rights, weakening GDP and currency value, the potential for a reduction in foreign direct investment (FDI), and the potential loss of the service sector in the city of London due to not being able to access the European Single Market.
The report will shift focus to analyzing three competing viewpoints of the direction of the London market based on recordings from interviews of stakeholders in the London real estate market. One being an executive of one of the largest REITs in the UK, another being the Global Head of Real Estate at a top asset management firm, and another being a director at a large property consulting firm. The report includes these differing “sub-theses” in order to try to make sense of the vast market uncertainties post-Brexit as well as to contrast their viewpoints with where the market is currently and with the report’s investment recommendation.
The remainder of the report will consist of the methods used for analyzing market trends including how the data was modeled in order to make the investment recommendation. The report will analyze real estate and market metrics pre-Brexit, immediately after the vote, post-Brexit, and will conclude with future projections encapsulating the investment recommendation.
Understanding the political landscape is crucial to formulating a reasonable prediction as to the future of the London market. Aside from research reports and articles, our main insights into the political direction of Brexit come from our recordings from meetings in March of 2017 with two high-ranking members of Parliament and one member of the House of Lords—all of whom are members of the Tory Party (the meetings being held under the condition of anonymity). The below analysis will be followed by a discussion of the economics of Brexit, primarily focusing on the economic risks and uncertainties which have emerged after the vote, and which currently exist today. Such risks include the UK losing its financial passporting rights, weakening GDP and currency value, the potential for a reduction in foreign direct investment (FDI), and the potential loss of the service sector in the city of London due to not being able to access the European Single Market.
The report will shift focus to analyzing three competing viewpoints of the direction of the London market based on recordings from interviews of stakeholders in the London real estate market. One being an executive of one of the largest REITs in the UK, another being the Global Head of Real Estate at a top asset management firm, and another being a director at a large property consulting firm. The report includes these differing “sub-theses” in order to try to make sense of the vast market uncertainties post-Brexit as well as to contrast their viewpoints with where the market is currently and with the report’s investment recommendation.
The remainder of the report will consist of the methods used for analyzing market trends including how the data was modeled in order to make the investment recommendation. The report will analyze real estate and market metrics pre-Brexit, immediately after the vote, post-Brexit, and will conclude with future projections encapsulating the investment recommendation.
ContributorsHorn, Jonathan (Co-author) / Sidi, Adam (Co-author) / Bonadurer, Werner (Thesis director) / McDaniel, Cara (Committee member) / Department of Finance (Contributor) / School of Politics and Global Studies (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
When making investment decisions many different indicators are taken into consideration before picking a stock/corporation to invest in (retail or institutional). Traditionally these indicators tend to be financial measures such as earnings per share, price to earnings ratio, price to book value ratio, dividend yield/payout ratio, etc. Often these indicators do not take into consideration the actual running intricacies of a company as they are simply based on historical financial statements, thus limiting an investor's decision-making ability. In this paper I analyze several companies stock performance to see if analyzing operational factors such as supply chain management before making an investment decision would have resulted in a profitable investment and thus prove as a reliable investment indicator. To do this I focused my analysis over a period of 5 years on two companies within three different industries; Fast Food, Processing, and Ecommerce. These industries were selected as the nature of their businesses require intensive supply chains thus this strategy would be most applicable to them as opposed to a software or IT company. Of the two companies selected from each respective industry one company would be listed/analyzed in Gartner's ranking of the "Annual Supply Chain Top 25" while the other company would not be. This Gartner ranking would serve as a measure of whether or not a company had a good supply chain. These companies then had their traditional financial metrics evaluated to see if supply chain analysis indirectly encapsulated some of these metrics as well. The goal of this analysis was to find if there was a strong correlation between companies listed on Gartner's rating scale and strong stock performance. If this was true this would suggest that there is a benefit to be captured by investors through using supply chain analysis as an indicator when making investment decisions.
ContributorsThompson, Tyler Thomas (Author) / Kellso, James (Thesis director) / Smith, Geoffrey (Committee member) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: 1. Understand if there is monetization potential in autonomous vehicle data 2. Create a financial model of what detailing the viability of AV data monetization 3. Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data.
ContributorsCarlton, Corrine (Co-author) / Clark, Rachael (Co-author) / Quintana, Alex (Co-author) / Shapiro, Brandon (Co-author) / Sigrist, Austin (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Dodd-Frank should be celebrated for its success in stabilizing the financial sector following the last financial crisis. Some of its measures have not only contained financial disaster but contributed to economic growth. These elements of Dodd-Frank have been identified as "clear wins" and include the increase of financial institutions' capital requirements, the single-point-of-entry approach to regulating financial firms, and the creation of the Consumer Financial Protection Bureau (CFPB). The single-point-of-entry strategy (SPOE), specifically, has done much to bring an end to the age of "too big to fail" institutions. By identifying firms that could expect to be aided in case of financial crisis, the SPOE approach reduces uncertainty among financial institutions. Moreover, SPOE eliminates the significant source of risk by establishing clear protocols for resolving failed financial firms. Dodd-Frank has also taken measures to better protect consumers with the creation of the CFPB. Some of the CFPB's stabilizing actions have included the removal of deceptive financial products, setting guidelines for qualified mortgages, and other regulatory safeguards on money transfers. Despite the CFPB's many triumphs, however, there is room for improvement, especially in the agency's ability to reduce regulatory redundancies in supervision and collaboration with other financial sector controllers. The significant strengths of Dodd-Frank are evident in its elements that have secured financial stability. However, it is important to also consider any potential to stifle healthy economic growth. There are several areas for legislative amendments and reforms in order to improve the performance of Dodd-Frank given its sweeping regulatory impact. Several governing redundancies now exist with the creation of new regulatory authorities. Special efforts to increase the authority of the Financial Sector Oversight Council (FSOC) and preserving the impartiality of the Office of Financial Research (OFR) are specific examples of reforms still needed to elevate the effectiveness of Dodd-Frank. In addition, Dodd-Frank could do more to clarify the Volcker Rule in order to ease banks' burden to comply with excessive oversight. Going forward, policymakers must be willing to adjust parts of Dodd-Frank that encroach too far on the private sector's ability to foster efficiency or development. In addition, identifying and monitoring areas of the legislation deemed "too soon to tell" will provide insight on the accuracy and benefit of some Dodd-Frank measures.
ContributorsConrad, Cody Lee (Author) / Sadusky, Brian (Thesis director) / Hoffman, David (Committee member) / School of Politics and Global Studies (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
My Honors Thesis is about answering a central question regarding the business of real estate: "What is the return on investment of obtaining a real estate license?" I focused my research on the monetary, time, and other value factors that affect the initial cost of securing a real estate salesperson license in the State of Arizona (costs) and the amount of money a licensed salesperson makes as a result of having a salesperson license (income). Licensees make this trade-off: the cost in terms of real dollars to obtain a license, as well as the opportunity costs associated with the time to secure, start using, and begin to earn money by way of a salesperson license. To answer the central question I conducted a survey of active licensees in order to determine the value ascribed to holding a real estate salesperson license. Through my research, I concluded that there is not a single number that can be assigned to a real estate license that indicates its value, but the data collected reveals that the return on investment has the potential to be great. Upfront costs and fees necessary to obtain a license are insignificant when the commission a licensee can then make from a single transaction is enough to cover those expenses. Therefore, based on the survey results and research into the initial costs associated with obtaining a real estate license, there appears to be sufficient data to support a positive return on investment and warrant obtaining a real estate license.
ContributorsSanders, Sarah (Author) / Stapp, Mark (Thesis director) / Koblenz, Blair (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: Understand if there is monetization potential in autonomous vehicle data Create a financial model of what detailing the viability of AV data monetization Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data. First, in order to brainstorm how this data could be monetized, we generated potential use cases, defined probable customers of these use cases, and how the data could generate value to customers as a means to understand what the "price" of autonomous vehicle data might be. While we came up with an extensive list of potential data monetization use cases, we evaluated our list of use cases against six criteria to narrow our focus into the following five: Government, Insurance Companies, Mapping, Marketing purposes, and Freight. Based on our research, we decided to move forward with the insurance industry as a proof of concept for autonomous vehicle data monetization. Based on our modeling, we concluded there is a significant market for autonomous vehicle data monetization moving forward. Data accessibility is a key driver in how profitable a particular company and their competitors can be in this space. In order to effectively monetize this data, it would first be important to understand the method by which a company obtains access to the data in the first place. Ultimately, based on our analysis, Company X has positioned itself well to take advantage of the new trends in autonomous vehicle technology. With more strategic investments and innovation, Company X can be a key benefactor of this unprecedented space in the near future.
ContributorsShapiro, Brandon (Co-author) / Quintana, Alex (Co-author) / Sigrist, Austin (Co-author) / Clark, Rachael (Co-author) / Carlton, Corrine (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Social media sites are platforms in which individuals discuss a wide range of topics and share a huge amount of information about themselves and their interests. So much of this information is encoded through unstructured text that users post on the these types of sites. There has been a considerable amount of work done in respect to sentiment analysis on these sites to infer users' opinions and preferences. However there is a gap where it may be difficult to infer how a user feels about particular pages or topics that they have not conveyed their sentiment for in a observable form. Collaborative filtering is a common method used to solve this problem with user data, but has only infrequently been used with sentiment information in order to make inferences about users preferences. In this paper we extend previous work on leveraging sentiment in collaborative filtering, specifically to approximate user sentiment and subsequently their vote for candidates in an online election. Sentiment is shown to be an effective tool for making these types of predictions in the absence of other more explicit user preference information. In addition to this, we present an evaluation of sentiment analysis methods and tools that are used in state of the art sentiment analysis systems in order to understand which of these methods to leverage in our experiments.
ContributorsBaird, James Daniel (Author) / Liu, Huan (Thesis director) / Wang, Suhang (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05