article

Implications of a Reserve Price in an Agent-Based Common-Value Auction

Authors:

Christopher N. Boyer,

B. Wade BrorsenAuthors Info & Claims

Computational Economics, Volume 43, Issue 1

Pages 33 - 51

https://doi.org/10.1007/s10614-013-9413-8

Published: 01 January 2014 Publication History

Abstract

Auction sellers can use a reserve price to require a minimum bid before items are sold. Theoretical and experimental research has tested the influence of a reserve price in an independent private values auction, but little focus has been given to the influence of a reserve price in a first-price common-value auction. We establish an agent-based first-price common-value auction to determine the impact of the reserve price with two buyers and with three buyers. An agent-based approach to this problem is both a unique contribution to the literature and appropriate since finding analytical solutions with common-value auctions is difficult. The agent-based model approach also allows us to consider buyers that have non-symmetric bid functions. Furthermore, we introduce a combination of numerical integration techniques with a new particle swarm learning algorithm. The buyers in the model choose their expected-net-revenue-maximizing bid price, and sellers choose their expected-revenue-maximizing reserve price. In the two-buyer and three-buyer auction, a reserve price increases the equilibrium winning bid price, decreases the probability that the item is sold, and increases the seller's expected revenue. A reserve price in a two-buyer auction increases the winning bid price more than including an additional buyer in the auction with no reserve price. However, due to only receiving a salvage value when the item does not sell in the auction, the seller's expected revenue is higher in the three-buyer first-price common-value auction without a reserve price than in the two-buyer auction with a reserve price.

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cover image Computational Economics

Computational Economics Volume 43, Issue 1

January 2014

128 pages

ISSN:0927-7099

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Copyright © Copyright © 2014 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 January 2014

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Cited By

Graf CZobernig VSchmidt JKlöckl C(2024)Computational Performance of Deep Reinforcement Learning to Find Nash EquilibriaComputational Economics10.1007/s10614-022-10351-663:2(529-576)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10614-022-10351-6
Castro JEspínola RGutiérrez IGómez D(2023)Auctions: A New Method for Selling Objects with Bimodal Density FunctionsComputational Economics10.1007/s10614-022-10259-161:4(1707-1743)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s10614-022-10259-1
Alexakis CDowling MEleftheriou KPolemis M(2020)Textual Machine Learning: An Application to Computational Economics ResearchComputational Economics10.1007/s10614-020-10077-357:1(369-385)Online publication date: 27-Nov-2020
https://dl.acm.org/doi/10.1007/s10614-020-10077-3
Li XDing RLiu XLiu XZhu EZhong Y(2016)A Dynamic Pricing Reverse Auction-Based Resource Allocation Mechanism in Cloud Workflow SystemsScientific Programming10.1155/2016/76094602016(17)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1155/2016/7609460

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