Comparing the Impact of Learning in Bidding Decision-Making Processes Using Algorithmic Game Theory-Reference-Cited by-同舟云学术

Comparing the Impact of Learning in Bidding Decision-Making Processes Using Algorithmic Game Theory

Published:2021-01 Issue:1 Volume:37 Page:04020099
ISSN:0742-597X
Container-title:Journal of Management in Engineering
language:en
Short-container-title:J. Manage. Eng.

Author:

Assaad Rayan¹,Ahmed Muaz O.²,El-adaway Islam H.³^ORCID,Elsayegh Amr¹,Siddhardh Nadendla Venkata Sriram⁴

Affiliation:

1. Ph.D. Candidate, Dept. of Civil, Architectural, and Environmental Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65409.

2. Ph.D. Graduate Student, Dept. of Civil, Architectural, and Environmental Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65409.

3. Hurst-McCarthy Professor of Construction Engineering and Management, Professor of Civil Engineering, and Founding Director of Missouri Consortium for Construction Innovation, Dept. of Civil, Architectural, and Environmental Engineering and Dept. of Engineering Management and Systems Engineering, Missouri Univ. of Science and Technology, Rolla, MO 65409 (corresponding author). ORCID: .

4. Assistant Professor, Dept. of Computer Science, Missouri Univ. of Science and Technology, Rolla, MO 65409.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management Science and Operations Research,Strategy and Management,General Engineering,Industrial relations

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29ME.1943-5479.0000867

Reference72 articles.

1. Construction Bidding Markup Estimation Using a Multistage Decision Theory Approach

2. Ahmed M. O. I. H. El-adaway K. T. Coatney and M. S. Eid. 2015. “Multi-stage bidding for construction contracts: A game theory approach.” In Proc. 5th Int./11th Construction Specialty Conf. Vancouver BC: Canadian Society for Civil Engineering.

3. Construction Bidding and the Winner’s Curse: Game Theory Approach

4. Anderson D. and D. Leith. 2020. “Optimality of the subgradient algorithm in the stochastic setting.” Preprint submitted September 10 2019. https://arxiv.org/abs/1909.05007.

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