ICE: An Expressive Iterative Combinatorial Exchange

Abstract

We present the design and analysis of the first fully expressive, iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language (TBBL) that is concise and expressive for CEs. Bidders specify lower and upper bounds in TBBL on their value for different trades and refine these bounds across rounds. These bounds allow price discovery and useful preference elicitation in early rounds, and allow termination with an efficient trade despite partial information on bidder valuations. All computation in the exchange is carefully optimized to exploit the structure of the bid-trees and to avoid enumerating trades. A proxied interpretation of a revealed-preference activity rule, coupled with simple linear prices, ensures progress across rounds. The exchange is fully implemented, and we give results demonstrating several aspects of its scalability and economic properties with simulated bidding strategies.

Sponsored Search Auctions with Rich Ads

“…In the current paper, while we do make some conceptual and modeling contributions, a large emphasis is on reporting about what we think is an interesting large-scale engineering task: how to solve a computationally hard market-based problem in a feasible amount of time under severe runtime constraints. That dimension of our work strongly connects with many other studies from very different domains, such as [9,10,12], to name a few.…”

iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding

“…The main challenge is that, if the intervals are sufficiently large, it may be difficult or impossible for the auctioneer to still determine a highly efficient allocation as well as payments. Therefore, we design a price-based refinement process that asks bidders to tighten some of their reported bounds in each auction round, extending earlier work on refinement processes by Lubin et al (2008). Our technical contribution here is a new price generation objective (Procedure 4), for which we prove that it reduces bidder effort, i.e., it leads to fewer refinement requests (Section 4.4).…”

The Parable of the Auctioneer: Complexity in Paul R. Milgrom’sDiscovering Prices

“…• fairness (e.g., in school choice, Abdulkadiroğlu and Sönmez (2003), or food supply, Prendergast (2016)), • revenue (e.g., in auctions, Myerson (1981); Binmore and Klemperer (2002); Varian (2007); Ostrovsky and Schwarz (2016)), • simplicity/ease of use (e.g., of bidding languages, Lubin et al (2008);Milgrom (2009Milgrom ( , 2010; Klemperer (2010); Bichler et al (2014), or ordinal preference elicitation, Pathak and Sönmez (2008)), • transparency (e.g., in financial markets, Asquith et al (2013)), • privacy (e.g., in voting systems, McSherry and Talwar (2007)), • speed (e.g., in algorithmic trading, Budish et al (2015); Kyle and Lee (2017)), • entry or competition (e.g., in auctions Levin and Smith (1994); Bajari and Hortaçsu ( 2003)), • price discovery (e.g., in stock exchanges, Biais et al (1995Biais et al ( , 1999; Cao et al Roth et al (2004); Roth (2007); Leider and Roth (2010)).…”