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Strategy proof electronic markets
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ACM International Conference Proceeding Series; Vol. 258 archive
Proceedings of the ninth international conference on Electronic commerce table of contents
Minneapolis, MN, USA
SESSION: Session M2: mechanisms and institutions I table of contents
Pages: 45 - 54  
Year of Publication: 2007
ISBN:978-1-59593-700-1
Authors
A. R. Dani  IDRBT, Hyderabad, India
Arun K. Pujari  University of Hyderabad, Hyderabad, India
V. P. Gulati  TCS, Hyderabad, India
Sponsors
SIGART: ACM Special Interest Group on Artificial Intelligence
ACM: Association for Computing Machinery
SIGEcom: ACM Special Interest Group on Electronic Commerce
Publisher
ACM  New York, NY, USA
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ABSTRACT

In electronic double auctions, property of incentive compatibility is very important. Incentive compatibility ensures that truthful bidding is the dominant strategy. Other important properties in electronic auctions are budget balance (BB) and individual rational (IR). The former ensures that the auction does not run in loss whereas the latter ensures voluntary participation. However these can be achieved only after sacrificing efficiency. The mechanisms based on uniform clearing price have been proposed in literature. Such mechanisms satisfy the properties of BB and IR. They are incentive compatible. However uniform price auction mechanism suffers from the problem of demand shading. Due to demand reduction, agents can acquire units at a lower price. This affects the property of incentive compatibility. Another problem with this approach is that it is not false name proof, meaning that agents can submit bids under different names to improve their utility. In electronic markets, where bids and asks are submitted remotely this property is very important. In this paper we propose discriminatory price mechanism, which is strategy proof, individually rational and budget balance. It is also false name proof, meaning agents cannot improve their utility by submitting false name bids.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
A. R. Dani: colleagues
Arun K. Pujari: colleagues
V. P. Gulati: colleagues