Topic: Non-clairvoyant Dynamic Mechanism Design: Experimental Evidence
Speaker: Professor Daniel Houser, George Mason University, USA
Time and Date: 19:30, June 25th, 2022
Platform: Tencent Meeting ID: 835-303-451
Speaker Profile:
Professor Daniel Houser is the director of the Department of Economics at George Mason University and the director of the Interdisciplinary Research Center for Economic Sciences at George Mason University. Work closely with Vernon Smith, the founder of experimental economics and the winner of the Nobel Prize in economics in 2002. The main research fields are: experimental economics, behavioral economics, and neuroeconomics. He served as the editor-in-chief, associate editor or editorial board of academic journals such as Management Science, Experimental Economics, Journal of Economic Behavior and Organization, Journal of Neuroscience, Psychology and Economics, Frontiers in Neuroscience, and the reviewer of more than 20 journals and fund projects such as Science, Nature, PNAS, National Science Foundation. He has received many grants from the National Science Foundation. He has published a large number of papers in PNAS, American Economic Review, Econometrica, Journal of Finance, Leadership Quarterly, Experimental Economics and other journals.
Abstract:
Dynamic mechanisms are powerful approaches for optimizing the revenue and efficiency of repeated auctions. Implementing these approaches is made complicated, however, by a number of conditions that are difficult to satisfy in practice. These include that the auction designer must be clairvoyant, in the sense that they must have reliable forecasts of participants’ valuation distributions in all future periods. Recently, Mirrokni et al. (2020) introduced a non-clairvoyant dynamic mechanism and showed it is optimal within the class of dynamic mechanisms that do not rely on strong assumptions regarding knowledge about the future. Here we report data from an experiment designed to test the performance of their mechanism. Our results support the theory: the optimal non-clairvoyant dynamic mechanism outperforms the repeated optimal static mechanism when it is predicted to do so. Our results point to the practical importance of non-clairvoyant mechanisms as implementable approaches to dynamic auction design.
