Date:
Location:
Speaker: Martin Bichler
Abstract: Equilibrium problems in Bayesian auction games can be described as systems of differential equations. Depending on the model assumptions, these equations might be such that we do not have an exact mathematical solution theory. The lack of analytical or numerical techniques with guaranteed convergence for the equilibrium problem has plagued the field and limited equilibrium analysis to rather simple auction models such as single-object auctions. Recent progress in equilibrium learning led to algorithms that find approximate equilibrium under a wide variety of model assumptions. The talk will summarize empirical results and theoretical insights on the convergence of equilibrium learning algorithms for auctions and contests.
Bio: Martin Bichler is a full professor at the Department of Computer Science of the Technical University of Munich (TUM) and heads the Decision Sciences & Systems Lab. Martin received his MSc degree from the Technical University of Vienna, and his Ph.D. as well as his Habilitation from the Vienna University of Economics and Business. He was a research fellow at UC Berkeley, and a research staff member at the IBM T. J. Watson Research Center, New York. Later, he was a visiting scholar at the University of Cambridge, at HP Labs Palo Alto, at the Department of Economics at Yale University, the Department of Economics at Stanford University, and the Simons Laufer Mathematical Sciences Institute in Berkeley. Martin was a president of the INFORMS Section on Auctions and Market Design, an editor-in-chief of the BISE journal, and he is currently the spokesperson of the research training group AdONE, which is at the intersection of Computer Science, Economics, and Mathematics. His research focuses on optimization, learning, and market design.