A Numerical Study with Experimental Data on Risk-Averse Subcontractors in Procurement Auctions with Subcontract Bids

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Prime contractors often solicit estimates for part of their work from potential subcontractors who can perform that work on their behalf with lower costs, prior to submitting their own estimates in procurement auctions. In a simple model for capturing this aspect of procurement auctions, this note clarifies through a numerical study with experimental data an important factor for obtaining clearer results in the experimental sessions. It was shown that unclear results observed in the sessions conducted previously would be due to the presence of extreme patterns in the distributions of risk aversion rates among the participants. We need to control the extreme risk aversion rates of participants for designing the experiment for future research.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3495-3499
Number of pages5
ISBN (Electronic)9781665439022
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
Duration: 2021 Dec 152021 Dec 18

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference2021 IEEE International Conference on Big Data, Big Data 2021
Country/TerritoryUnited States
CityVirtual, Online
Period21/12/1521/12/18

Keywords

  • experimental data
  • numerical study
  • procurement auction
  • risk aversion rate
  • subcontractors

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems

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