Evaluating Non-Clairvoyant Dynamic Mechanisms: Theory and Experiment

45 Pages Posted: 13 Sep 2024 Last revised: 9 Dec 2024

See all articles by Shan Gui

Shan Gui

Shanghai University of Finance and Economics - School of Economics

Daniel Houser

Interdisciplinary Center for Economic Science

Abstract

Dynamic mechanisms provide a powerful means for optimizing revenue in repeated auctions. However, they are complex to implement, as they assume clairvoyance, i.e., that all participants accurately predict future demands. Recently, Mirrokni et al. (2020) introduced an optimal non-clairvoyant dynamic mechanism (NC) with a simple structure that does not rely on future knowledge. Here, we design an experiment to test the performance of NC as compared to another non-clairvoyant mechanism, the optimal repeated static mechanism (RS) (Myerson, 1981). Results show that non-clairvoyant mechanisms work as intended: NC either outperforms or underperforms RS according to theory predictions. However, contrary to theory, the optimal clairvoyant mechanism using full information performs no better than the non-clairvoyant mechanisms. Our results highlight the practical importance of non-clairvoyant mechanisms as implementable approaches to dynamic auction design.

Keywords: Non-clairvoyant, Dynamic, Mechanism, Experiment

Suggested Citation

Gui, Shan and Houser, Daniel, Evaluating Non-Clairvoyant Dynamic Mechanisms: Theory and Experiment. GMU Working Paper in Economics No. 24-33, Available at SSRN: https://ssrn.com/abstract=4955491 or http://dx.doi.org/10.2139/ssrn.4955491

Shan Gui (Contact Author)

Shanghai University of Finance and Economics - School of Economics ( email )

777 Guoding Road
Shanghai, 200433
China

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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