Date of Award
Spring 5-31-2019
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computational and Data Sciences
First Advisor
David Porter
Second Advisor
Stephen Rassenti
Third Advisor
Ryan French
Abstract
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
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
K. James, K, "Estimating auction equilibria using individual evolutionary learning," Ph.D. dissertation, Chapman University, Orange, CA, 2019. https://doi.org/10.36837/chapman.000053