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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Allocation Of Public Resources: Bringing Order To Chaos, Lance Clifner
Allocation Of Public Resources: Bringing Order To Chaos, Lance Clifner
Computational and Data Sciences (PhD) Dissertations
Science Olympiad (SO) is a team-based academic competition involving multiple subject areas (Events) with arcane rules governing the team composition. Add to the mix parental contention over which student(s) get on the “All-Star” team, and you have a potentially explosive situation. This project brings order and logic to school-based SO programs and defuses tense milestones through the implementation of an institutional structure that: assigns students to Events based on solicited student preferences for the Events, collects objective student performance data, composes competitive teams based on student performance (aka “Moneyball”), and brings transparency to the Team Selection process through crowdsourcing. The …
A Computational And Experimental Examination Of The Fcc Incentive Auction, Logan Gantner
A Computational And Experimental Examination Of The Fcc Incentive Auction, Logan Gantner
Computational and Data Sciences (PhD) Dissertations
In 2016, the Federal Communications Commission debuted a new auction mechanism, the Incentive Auction, with the intention of obtaining high frequency television broadcasting spectrum, repurposing it for cellular use, and reselling these licenses at profitable prices. In designing this process, the traditional mechanism used for spectrum auctions, the Simulta- neous Multiple Round Auction (SMR), was modified in order to speed the process. This new mechanism, the Incentive Forward Auction (IFA), intended to reduce the number of rounds per auction by lumping similar spectrum licenses together. However, the IFA discourages straightforward bidding strategies and can result in bidders committing more in …
Estimating Auction Equilibria Using Individual Evolutionary Learning, Kevin James
Estimating Auction Equilibria Using Individual Evolutionary Learning, Kevin James
Computational and Data Sciences (PhD) Dissertations
I develop the Generalized Evolutionary Nash Equilibrium Estimator (GENEE) library. The tool is designed to provide a generic computational library for running genetic algorithms and individual evolutionary learning in economic decision-making environments. Most importantly, I have adapted the library to estimate equilibria bidding functions in auctions. I show it produces highly accurate estimates across a large class of auction environments with known solutions. I then apply GENEE to estimate the equilibria of two additional auctions with no known solutions: first-price sealed-bid common value auctions with multiple signals, and simultaneous first-price auctions with subadditive values