Testing a Purportedly More Learnable Auction Mechanism

We describe an auction mechanism in the class of Groves mechanisms that has received attention in the computer science literature because of its theoretical property of being more "learnable" than the standard second price auction mechanism. We bring this mechanism, which we refer to as the "clamped second price auction mechanism," into the laboratory to determine whether it helps human subjects learn to play their optimal strategy faster than the standard second price auction mechanism. Contrary to earlier results within computer science using simulated reinforcement learning agents, we find that both in settings where subjects are given complete information about auction payoff rules and in settings where they are given no information about auction payoff rules, subjects converge on playing their optimal strategy significantly faster in sequential auctions conducted with a standard second price auction mechanism than with a clamped second price auction mechanism. We conclude that while it is important for mechanism designers to think more about creating learnable mechanisms, the clamped second price auction mechanism in fact produces slower learning in human subjects than the standard second price auction mechanism. Our results also serve to highlight differences in behavior between simulated agents and human bidders that mechanism designers should take into account before placing too much faith in simulations to test the performance of mechanisms intended for human use.

[1]  John H. Kagel,et al.  1 Bidding in Common-Value Auctions: A Survey of Experimental Research , 2009 .

[2]  Laurent Mathevet,et al.  Supermodular Bayesian Implementation: Learning and Incentive Design , 2007 .

[3]  Xiaoquan Zhang,et al.  Dynamic price competition on the internet: advertising auctions , 2007, EC '07.

[4]  Kagan Tumer,et al.  Distributed agent-based air traffic flow management , 2007, AAMAS '07.

[5]  Nicole Immorlica,et al.  Dynamics of bid optimization in online advertisement auctions , 2007, WWW '07.

[6]  U. Fischbacher.  z-Tree: Zurich toolbox for ready-made economic experiments , 1999 .

[7]  Benjamin Edelman,et al.  Strategic bidder behavior in sponsored search auctions , 2007, Decis. Support Syst..

[8]  I. Erev,et al.  On adaptation, maximization, and reinforcement learning among cognitive strategies. , 2005, Psychological review.

[9]  Kagan Tumer,et al.  Multi-agent reward analysis for learning in noisy domains , 2005, AAMAS '05.

[10]  Kagan Tumer,et al.  Coordinating multi-rover systems: evaluation functions for dynamic and noisy environments , 2005, GECCO '05.

[11]  Lawrence M. Ausubel.  An Efficient Ascending-Bid Auction for Multiple Objects , 2004 .

[12]  Kagan Tumer,et al.  Collectives and Design Complex Systems , 2004 .

[13]  David C. Parkes,et al.  On Learnable Mechanism Design , 2022 .

[14]  Alvin E. Roth,et al.  An Experimental Analysis of Ending Rules in Internet Auctions , 2003 .

[15]  Kagan Tumer,et al.  Learning sequences of actions in collectives of autonomous agents , 2002, AAMAS '02.

[16]  Kagan Tumer,et al.  Collective Intelligence, Data Routing and Braess' Paradox , 2002, J. Artif. Intell. Res..

[17]  Kagan Tumer,et al.  Optimal Payoff Functions for Members of Collectives , 2001, Adv. Complex Syst..

[18]  M. Armstrong.  Optimal Multi-Object Auctions , 2000 .

[19]  Vol Cxv Issue.  EFFICIENT AUCTIONS , 2000 .

[20]  J. Kagel.  7. Auctions: A Survey of Experimental Research , 1995 .

[21]  J. Kagel,et al.  Independent Private Value Auctions: Bidder Behaviour in First-, Second- and Third-Price Auctions with Varying Numbers of Bidders , 1993 .

[22]  Paul R. Milgrom,et al.  Rationalizability, Learning, and Equilibrium in Games with Strategic Complementarities , 1990 .

[23]  Ronald M. Harstad,et al.  Information Impact and Allocation Rules in Auctions with Affiliated Private Values: A Laboratory Study , 1987 .

[24]  Paul R. Milgrom,et al.  A theory of auctions and competitive bidding , 1982 .

[25]  Roger B. Myerson,et al.  Optimal Auction Design , 1981, Math. Oper. Res..

[26]  Theodore Groves,et al.  Incentives in Teams , 1973 .

[27]  William Vickrey,et al.  Counterspeculation, Auctions, And Competitive Sealed Tenders , 1961 .