Prof. Jason Hartline presented a tutorial highlighting his current research, titled, "Bayesian Mechanism Design", at the 28th AAAI Conference on Artificial Intelligence (AAAI-14), held on Monday, July 28, 2014 at the Québec Convention Center, Québec City, Québec, Canada.
The Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI-14) is held July 27–31, 2014 in Québec City, Québec, Canada. The purpose of this conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines.
Prof. Hartline's tutorial surveys the classical economic theory of Bayesian mechanism design and recent advances from the perspective of algorithms and approximation. Classical economics gives simple characterizations of Bayes-Nash equilibrium and optimal mechanisms when the agents' preferences are linear and single-dimensional. The mechanisms it predicts are often complex and overly dependent on details of the model. Approximation complements this theory and suggests that simple and less-detail-dependent mechanisms can be nearly optimal. Furthermore, techniques from approximation and algorithms can be used to describe good mechanisms beyond the single-dimensional, linear model of agent preferences.
Prof. Hartline's research introduces design and analysis methodologies from computer science to understand and improve outcomes of economic systems. Optimal behavior and outcomes in complex environments are complex and, therefore, should not be expected; instead, the theory of approximation can show that simple and natural behaviors are approximately optimal in complex environments. This approach is applied to auction theory and mechanism design in his graduate textbook Mechanism Design and Approximation which is under preparation.