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- Creators: School of Accountancy
- Creators: Computer Science and Engineering Program
- Member of: Theses and Dissertations
- Resource Type: Text
- Status: Published
The next question: What do these changes in the roles and responsibilities look like for the auditors of the future? Cognitive technology will assuredly present new issues for which humans will have to find solutions.
• How will humans be able to test the accuracy and completeness of the decisions derived by cognitive systems?
• If cognitive computing systems rely on supervised learning, what is the most effective way to train systems?
• How will cognitive computing fair in an industry that experiences ever-changing industry regulations?
• Will cognitive technology enhance the quality of audits?
In order to answer these questions and many more, I plan on examining how cognitive technologies evolved into their use today. Based on this historic trajectory, stakeholder interviews, and industry research, I will forecast what auditing jobs may look like in the near future taking into account rapid advances in cognitive computing.
The conclusions forecast a future in auditing that is much more accurate, timely, and pleasant. Cognitive technologies allow auditors to test entire populations of transactions, to tackle audit issues on a more continuous basis, to alleviate the overload of work that occurs after fiscal year-end, and to focus on client interaction.
Historically, the predominant strategy for evaluating baseball pitchers has been through statistics created directly from the offensive production against the pitcher, such as ERA. Such statistics are inherently relative to the abilities and competition level of the opposing offense and the field defense, which the pitcher has no control over, making it difficult to compare pitchers across leagues. In this paper, I use cutting edge pitch-tracking data to develop a pitch evaluation model that is intrinsic to the attributes of the pitches themselves, and not influenced directly by the outcomes of each individual pitch. I train four different classifiers to predict the probability of each pitch belonging to different subsets of outcomes, then multiply the probability of each outcome by that outcome’s average run value to arrive at an expected run value for the pitch. I compare the performance of each classifier to a baseline, examine the most impactful features, and compare the top pitchers identified by the model to those identified by a different baseball statistics resource, ultimately concluding that three of the four classification models are productive and that the overall intrinsic evaluation model accurately identifies the sports top performers.
For years, Commissioner Rob Manfred has hinted and brought about the idea of adding two more teams to Major League Baseball (Mitchell). The growth of the game is of utmost importance, and they have made many changes to try to expand the growth of fans the past few years particularly catered to new and young fans. New rules like a pitch clock and mound visit limitations are examples of in game changes made to speed up the game, but they have also experimented with spring training and regular season games internationally or at new venues. In just the past decade, games have been played or planned (due to COVID-19 cancellations) in Monterrey, Mexico City, London, Tokyo, San Juan, Montreal, Las Vegas, Williamsport, and even Iowa. With the exception of the Williamsport Little League Classic and the Field of Dreams game in Iowa, all these locations had games to see what the atmosphere and logistics would be like with expansion in mind as a possibility in the future. With this in mind, this thesis will analyze and come to a conclusion on the following cities for the best fits for expansion: Monterrey, Mexico City, San Juan, Vancouver, Montreal, Las Vegas, Portland, Nashville, Raleigh, and San Antonio.