Designing Core-Selecting Payment Rules: A Computational Search Approach

Benedikt Bünz (), Benjamin Lubin () and Sven Seuken ()
Additional contact information
Benedikt Bünz: Stanford University, Stanford, California 94305
Benjamin Lubin: Boston University, Boston, Massachusetts 02215
Sven Seuken: University of Zurich, 8050 Zürich, Switzerland; ETH AI Center, 8092 Zürich, Switzerland

Information Systems Research, 2022, vol. 33, issue 4, 1157-1173

Abstract: We study the design of core-selecting payment rules for combinatorial auctions, a challenging setting where no strategyproof rules exist. We show that the rule most commonly used in practice, the Quadratic rule, can be improved on in terms of efficiency, incentives, and revenue. We present a new computational search framework for finding good mechanisms, and we apply it toward a search for good core-selecting rules. Within our framework, we use an algorithmic Bayes–Nash equilibrium solver to evaluate 366 rules across 31 settings to identify rules that outperform the Quadratic rule. Our main finding is that our best-performing rules are large -style rules—that is, they provide bidders with large values with better incentives than does the Quadratic rule. Finally, we identify two particularly well-performing rules and suggest that they may be considered for practical implementation in place of the Quadratic rule.

Keywords: combinatorial auctions; payment rules; core (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://dx.doi.org/10.1287/isre.2022.1108 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:33:y:2022:i:4:p:1157-1173

Access Statistics for this article

More articles in Information Systems Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().