Estimating Auction Equilibria using Individual Evolutionary Learning

<p>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</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computational and Data Sciences

Year

2019

Author dc:creator

• James, Kevin

Contributors dc:contributor

• David Porter
• Stephen Rassenti
• Ryan French

• genetic algorithms
• Behavioral Economics

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_dissertations/1

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_dissertations-1000