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Learning Environmental Parameters For The Design Of Optimal English Auctions With Discrete Bid Levels

Learning Environmental Parameters For The Design Of Optimal English Auctions With Discrete Bid Levels
Learning Environmental Parameters For The Design Of Optimal English Auctions With Discrete Bid Levels
In this paper we consider the optimal design of English auctions with discrete bid levels. Such auctions are widely used in online internet settings and our aim is to automate their configuration in order that they generate the maximum revenue for the auctioneer. Specifically, we address the problem of estimating the values of the parameters necessary to perform this optimal auction design by observing the bidding in previous auctions. To this end, we derive a general expression that relates the expected revenue of the auction when discrete bid levels are implemented, but the number of participating bidders is unknown. We then use this result to show that the characteristics of these optimal bid levels are highly dependent on the expected number of bidders and on their valuation distribution. Finally, we derive and demonstrate an online algorithm based on Bayesian machine learning, that allows these unknown parameters to be estimated through observations of the closing price of previous auctions. We show experimentally that this algorithm converges rapidly toward the true parameter values and, in comparison with an auction using the more commonly implemented fixed bid increment, results in an increase in auction revenue.
1-15
Springer
Rogers, A
f9130bc6-da32-474e-9fab-6c6cb8077fdc
David, E
78a842ac-f240-4b3e-bfbd-95255585b607
Schiff, J
640e8ada-b12c-4fb6-a193-451bc95a491d
Kraus, S
7af0d48e-bd2d-40e3-b99e-3d0e4ecccfb7
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
La Poutré, H
Sadeh, N
Sverker, J
Rogers, A
f9130bc6-da32-474e-9fab-6c6cb8077fdc
David, E
78a842ac-f240-4b3e-bfbd-95255585b607
Schiff, J
640e8ada-b12c-4fb6-a193-451bc95a491d
Kraus, S
7af0d48e-bd2d-40e3-b99e-3d0e4ecccfb7
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
La Poutré, H
Sadeh, N
Sverker, J

Rogers, A, David, E, Schiff, J, Kraus, S and Jennings, N. R. (2005) Learning Environmental Parameters For The Design Of Optimal English Auctions With Discrete Bid Levels. La Poutré, H, Sadeh, N and Sverker, J (eds.) In Agent-mediated Electronic Commerce, Designing Trading Agents and Mechanisms: AAMAS 2005 Workshop, AMEC 2005, Utrecht, Netherlands, July 25, 2005, and IJCAI 2005 Workshop, TADA 2005, Edinburgh, UK, August 1, 2005, Selected and Revised Papers. Springer. pp. 1-15 .

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper we consider the optimal design of English auctions with discrete bid levels. Such auctions are widely used in online internet settings and our aim is to automate their configuration in order that they generate the maximum revenue for the auctioneer. Specifically, we address the problem of estimating the values of the parameters necessary to perform this optimal auction design by observing the bidding in previous auctions. To this end, we derive a general expression that relates the expected revenue of the auction when discrete bid levels are implemented, but the number of participating bidders is unknown. We then use this result to show that the characteristics of these optimal bid levels are highly dependent on the expected number of bidders and on their valuation distribution. Finally, we derive and demonstrate an online algorithm based on Bayesian machine learning, that allows these unknown parameters to be estimated through observations of the closing price of previous auctions. We show experimentally that this algorithm converges rapidly toward the true parameter values and, in comparison with an auction using the more commonly implemented fixed bid increment, results in an increase in auction revenue.

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More information

Published date: 2005
Additional Information: Event Dates: 25th July 2005
Venue - Dates: Seventh International Workshop on Agent-Mediated E-Commerce, Netherlands, 2005-07-25
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 260834
URI: https://eprints.soton.ac.uk/id/eprint/260834
PURE UUID: 6d0c394d-4e45-40b7-a5d0-6c80ac29601d

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Date deposited: 03 May 2005
Last modified: 18 Jul 2017 09:09

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