Non-Clairvoyant Dynamic Mechanism Design: Experimental Evidence

36 Pages Posted: 28 Feb 2023

See all articles by Shan Gui

Shan Gui

Interdisciplinary Center for Economic Science, George Mason University

Daniel Houser

Interdisciplinary Center for Economic Science

Abstract

Dynamic mechanisms provide a powerful means for optimizing repeated auctions. Imple- menting them is complicated, however, due to a number of conditions that are difficult to satisfy in practice. These include the fact 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 mech- anism (NC) and showed it is optimal within the class of dynamic mechanisms that do not rely on strong assumptions about the future. We showed, however, that an optimal repeated static mechanism (RS) (a Myerson auction) can sometimes outperform NC. Here, we report data from an experiment to test NC in relation to RS. Our results support the theory: NC outperforms or underperforms RS according to theory predictions. Our results highlight the practical value of non-clairvoyant mechanisms as implementable approaches to dynamic auction design.

Keywords: Non-clairvoyant, Dynamic, Mechanism, experiment

Suggested Citation

Gui, Shan and Houser, Daniel, Non-Clairvoyant Dynamic Mechanism Design: Experimental Evidence. Available at SSRN: https://ssrn.com/abstract=4363300 or http://dx.doi.org/10.2139/ssrn.4363300

Shan Gui (Contact Author)

Interdisciplinary Center for Economic Science, George Mason University ( email )

Daniel Houser

Interdisciplinary Center for Economic Science ( email )

5th Floor, Vernon Smith Hall
George Mason University
Arlington, VA 22201
United States
7039934856 (Phone)

HOME PAGE: http://mason.gmu.edu/~dhouser/

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