Joshua F. Boitnott
Contact Information:
Simon Fraser University
Web: https://sites.google.com/site/joshuafboitnott/
Department of Economics
Phone: (306)715-2760
8888 University Drive
E-mail : jfb@sfu.ca
Burnaby, BC Canada V5A 1S6
Education:
PhD Economics Simon Fraser University
2009-Present
Dissertation: Three Essays on Learning Algorithms and Experimental Economics
Primary Supervisor: Jasmina Arifovic
Expected Completion Date: June 2016
MA
Economics Simon Fraser University
2008-2009
BA
Economics Simon Fraser University
2005-2008
Research and Teaching Fields:
Learning Algorithms, Public Economics, Experimental Economics, Development Economics
Conference Presentations:
2014 Canadian Economics Association
“Learning Correlated Equilibria: An Evolutionary Approach”
Vancouver, BC
Teaching Experience:
2013
Instructor, SFU
The World Economy
2013
Tutor Marker, SFU
Macroeconomic Theory & Policy (MA course)
2008-2013
Teaching Assistant, SFU
Principle of Microeconomics, Microeconomic Theory I: Competitive Behavior
Principles of Macroeconomics, The World Economy (x3), Economics and Government,
Environmental Economics, Canadian Macroeconomic Policy, Money and Banking (x2),
Introduction to Economic Concepts and Issues, Economic History of Canada,
Economic Development (x2), Economics of Popular Culture, Public Economics: Taxation,
Public Economics: The Role of Government
Research Experience:
2012 Research Assistant to Jasmina Arifovic and John Ledyard (Cal Tech)
2013 Research Associate with Centre for Researchin Adaptive Behaviour in Economics (CRABE)
Volunteer Experience:
2013
Provincial Judge, Odyssey of the Mind
2006 - 2011
Provincial Head Judge, Odyssey of the Mind
Computer Skills:
Advanced:
Basic:
MatLab, Excel, LaTeX , Word, PowerPoint
STATA, R, HTML
Personal Information:
Citizenship:
U.S.A.
Permanent Resident: Canada
Languages:
Fluent English, Basic Spanish
Port Moody, BC
Various Locations, BC
Research Papers:
“The Behavior of Learning Algorithms in Experimental Games with Congestion,” Job Market Paper
Abstract:Learning algorithms represent an important step in modeling the dynamic behavior of human
subjects observed in experimental settings, yet many existing algorithms have limited application
outside of a single game or across different experimental settings when parameter values are fixed.
Furthermore, while previous studies have investigated algorithmic agent performance relative to
experimental data, they have relied upon data from a single source. In this study, we use a horse-race
approach to compare the performance of four learning algorithms (Impulse-Matching Learning,
Self-Tuning Experience Weighted Attractions, and Individual Evolutionary Learning(x2)) across five
different sets of experimental data generated by multiple researchers where the subjects face congestion.
We examine the ability of each algorithm to replicate experimental data independent of the training
data set used to determine the initial parameter values via the root mean squared error for each
algorithm relative to the data produced by the human subjects and the ability of the algorithms to
match multiple experimental equilibria despite adjusting the payoffs, the number of players, and the
number of repetitions played. We determined that the Individual Evolutionary Learning model with a
modified set of assumptions has the closest fit of the regularities seen in the experimental data when
human subjects were given full information measured by the average mean squared distance, but not
in experiments with limited information.
“Learning Correlated Equilibria: An Evolutionary Approach,” joint with Jasmina Arifovic and John Duffy
(UC Irvine)
Abstract: Correlated equilibrium (Aumann 1974, 1987) is an important generalization of the Nash
equilibrium concept for multiplayer non-cooperative games. In a correlated equilibrium, players
rationally condition their strategies on realizations of a common external randomization device and,
as a consequence are able to achieve payoffs that Pareto dominate any of the game’s Nash equilibria.
In this paper we explore whether such correlated equilibria can be learned over time using evolutionary
learning model where agents do not start with any knowledge of the distribution of random draws
made by the external randomization device. Furthermore, we validate our learning algorithm findings
by comparing the limiting behavior of simulations of our algorithm with both the correlated
equilibrium of the game and the behavior of human subjects that play that same game. Our results
suggest that our evolutionary learning model is capable of learning the correlated equilibria of these
games in a manner that approximates well the learning behavior of human subjects.
“Beliefs, Learning, and the Linear Public Goods Game: Adjusting the Individual Evolutionary Learning
with Other Regarding Preferences Model,” (In Progress)
References:
Jasmina Arifovic
Simon Fraser University
E-mail : arifovic@sfu.ca
(778)782-5603
Erik Kimbrough
Simon Fraser University
E-mail : ekimbrough@gmail.com
(778)782-9383
John Duffy
University of California, Irvine
E-mail : duffy@uci.edu
(949)824-8341