SPEAKER: Kevin Leyton-Brown
Department of Computer Science
University of British Columbia
TITLE: Beyond Equilibrium: Predicting Human Behavior in Normal Form Games
It is standard in multiagent settings to assume that agents will adopt Nash equilibrium strategies. However, studies in experimental economics demonstrate that Nash equilibrium is a poor description of human player's initial behavior in normal-form games. In this paper, we consider a wide range of widely-studied models from behavioral game theory. For what we believe is the first time, we evaluate each of these models in a meta-analysis, taking as our data set large-scale and publicly-available experimental data from the literature. We then show how to analyze the parameters of the best-performing model, and propose modifications to it that we believe make it more suitable for practical prediction of initial play by humans in normal-form games.
Kevin Leyton-Brown is an associate professor in computer science at the University of British Columbia. He received a B.Sc. from McMaster University (1998), and an M.Sc. and PhD from Stanford University (2001, 2003). Much of his work is at the intersection of computer science and microeconomics, addressing computational problems in economic contexts and incentive issues in multiagent systems. He also studies the application of machine learning to the automated design and analysis of algorithms for solving hard computational problems. He has co-written two books, "Multiagent Systems" and "Essentials of Game Theory," and over seventy peer-refereed technical articles. He is an associate editor of the Journal of Artificial Intelligence Research (JAIR), the Artificial Intelligence Journal (AIJ), and ACM Transactions on Economics and Computation. He has served as a consultant for Trading Dynamics Inc., Ariba Inc., and Cariocas Inc., and was scientific advisor to Zite Inc until it was acquired by CNN in 2011.