A Subject Experiment of an Approximate DGS Algorithm: Price Increment, Allocative Efficiency, and Seller's Revenue

Yoichi Izunaga; Satoshi Takahashi; Naoki Watanabe

doi:10.1109/BigData55660.2022.10020612

A Subject Experiment of an Approximate DGS Algorithm: Price Increment, Allocative Efficiency, and Seller's Revenue

Yoichi Izunaga, Satoshi Takahashi, Naoki Watanabe

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

For multiple-item auctions with unitary demands, the approximate Demange-Gale-Sotomayor (DGS) algorithm has an easy-to-understand rule but it needs a considerably longer time to settle, as compared with the Vickrey-Clarke-Groves (VCG) mechanism. A possible way to make the time shorter is to raise asking prices in larger increments, but concerns about deterioration of allocative efficiency and the seller's revenue arise when the increments are large. In a subject experiment, we observed under the approximate DGS algorithm that there was an appropriate increment in asking prices by which the seller's revenue and allocative efficiency did not significantly differ from those in a smaller increment. The theoretical approximation boundaries of the winning prices were not necessarily satisfied because human subjects did not choose sincere bidding.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3273-3280
Number of pages	8
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020612
Publication status	Published - 2022
Externally published	Yes
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: 2022 Dec 17 → 2022 Dec 20

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	22/12/17 → 22/12/20

Keywords

approximate DGS algorithm
multiple-item auctions
sincere bidding
subject experiment
unitary demand

ASJC Scopus subject areas

Modelling and Simulation
Computer Networks and Communications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality
Control and Optimization

Access to Document

10.1109/BigData55660.2022.10020612

Cite this

Izunaga, Y., Takahashi, S., & Watanabe, N. (2022). A Subject Experiment of an Approximate DGS Algorithm: Price Increment, Allocative Efficiency, and Seller's Revenue. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 3273-3280). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10020612

A Subject Experiment of an Approximate DGS Algorithm: Price Increment, Allocative Efficiency, and Seller's Revenue. / Izunaga, Yoichi; Takahashi, Satoshi; Watanabe, Naoki.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 3273-3280 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Izunaga, Y, Takahashi, S & Watanabe, N 2022, A Subject Experiment of an Approximate DGS Algorithm: Price Increment, Allocative Efficiency, and Seller's Revenue. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., pp. 3273-3280, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 22/12/17. https://doi.org/10.1109/BigData55660.2022.10020612

Izunaga Y, Takahashi S, Watanabe N. A Subject Experiment of an Approximate DGS Algorithm: Price Increment, Allocative Efficiency, and Seller's Revenue. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 3273-3280. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020612

Izunaga, Yoichi ; Takahashi, Satoshi ; Watanabe, Naoki. / A Subject Experiment of an Approximate DGS Algorithm : Price Increment, Allocative Efficiency, and Seller's Revenue. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 3273-3280 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

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A Subject Experiment of an Approximate DGS Algorithm: Price Increment, Allocative Efficiency, and Seller's Revenue

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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