Strong activity rules for iterative combinatorial auctions
Name
HBPZ2009.pdf
Size
371.36 KB
Format
Adobe PDF
Checksum (MD5)
078f619693b0c8d7758a54e25df9ee18
Author(s)
Harsha, Pavithra
Barnhart, Cynthia
Parkes, David C.
Zhang, Haoqi
Date Issued
September 2009
Journal
Computers and Operations Research
Publisher
Elsevier
Citation
Harsha, Pavithra et al. “Strong activity rules for iterative combinatorial auctions.” Computers & Operations Research 37.7 (2010): 1271-1284.
Version
Original manuscript
Abstract
Activity rules have emerged in recent years as an important aspect of practical auction design. The role of an activity rule in an iterative auction is to suppress strategic behavior by bidders and promote simple, continual, meaningful bidding and thus, price discovery. These rules find application in the design of iterative combinatorial auctions for real world scenarios, for example in spectrum auctions, in airline landing slot auctions, and in procurement auctions. We introduce the notion of strong activity rules, which allow simple, consistent bidding strategies while precluding all behaviors that cannot be rationalized in this way. We design such a rule for auctions with budget-constrained bidders, i.e., bidders with valuations for resources that are greater than their ability to pay. Such bidders are of practical importance in many market environments, and hindered from bidding in a simple and consistent way by the commonly used revealed-preference activity rule, which is too strong in such an environment. We consider issues of complexity, and provide two useful forms of information feedback to guide bidders in meeting strong activity rules. As a special case, we derive a strong activity rule for non-budget-constrained bidders. The ultimate choice of activity rule must depend, in part, on beliefs about the types of bidders likely to participate in an auction event because one cannot have a rule that is simultaneously strong for both budget-constrained bidders and quasi-linear bidders.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.cor.2009.09.003
