Article
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Competitive Bidding Strategy in an Auction with Random Cutoff—Randomness Is Always Unpredictable?
Version 1
: Received: 8 August 2024 / Approved: 8 August 2024 / Online: 8 August 2024 (08:57:37 CEST)
How to cite: Lee, J. H. Competitive Bidding Strategy in an Auction with Random Cutoff—Randomness Is Always Unpredictable?. Preprints 2024, 2024080602. https://doi.org/10.20944/preprints202408.0602.v1 Lee, J. H. Competitive Bidding Strategy in an Auction with Random Cutoff—Randomness Is Always Unpredictable?. Preprints 2024, 2024080602. https://doi.org/10.20944/preprints202408.0602.v1
Abstract
This paper detects bidding behaviors in the auction with the random cutoff, where a winning bid needs to be above and the closest to the random cutoff. In this auction, each bidder is eager to predict the random cutoff in order to use the predicted random cutoff as bid. Furthermore, one of deep learning models, the transformer model is employed as one empirical method to anticipate the random cutoff. The result shows that the predicted random cutoff turns out to be a winner with probability of about 20 percent. This paper contributes to economic literature in terms of investigating the connection between a deep learning model and an auction based on the economic theory.
Keywords
reverse auction; random cutoff; transformer model
Subject
Business, Economics and Management, Economics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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