Technical Papers
Apr 11, 2024

Potential AI-Driven Algorithmic Collusion and Influential Factors in Construction Bidding

Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 4

Abstract

Artificial intelligence (AI) is increasingly aiding human decision makers in construction bidding processes by analyzing competitors’ bidding patterns. However, concerns are emerging about the potential for AI-driven algorithmic collusion, which might inflate prices and disrupt fair competition in various sectors. Given the unique dynamics of the construction sector and its growing reliance on AI, understanding the impact of these algorithms on the bidding landscape is essential, both academically and practically. Thus, this study investigates the impact of AI in the construction bidding market on bid pricing patterns to predict how the landscape of the market might change as AI starts to play a more prominent role. We subjected AI bidding agents to repeated competitions with each other in computer-simulated construction bidding marketplaces. We focused on the markup decisions made by the AI bidders. Our findings indicate that AI bidders tend to develop cooperative strategies over time, leading to higher bids overall compared to lower, competitive bids. This collusive behavior was facilitated by frequent interactions (previous bidding competitions over time) between AI bidders. This collusive behavior was enabled by algorithms that aimed to maximize the profit and was hindered by algorithms that aimed to maximize the number of project wins. These findings highlight potential fairness and competitiveness issues in construction bidding with dominant AI bidders.

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Data Availability Statement

Data will be made available upon reasonable request.

Acknowledgments

This work was supported by Creative-Pioneering Researchers Program through Seoul National University and the Institute of Engineering Research at Seoul National University.

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Go to Journal of Computing in Civil Engineering
Journal of Computing in Civil Engineering
Volume 38Issue 4July 2024

History

Received: Aug 30, 2023
Accepted: Jan 9, 2024
Published online: Apr 11, 2024
Published in print: Jul 1, 2024
Discussion open until: Sep 11, 2024

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Authors

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Chan Heo, S.M.ASCE [email protected]
Ph.D. Student, Dept. of Architecture and Architectural Engineering, Seoul National Univ., Seoul 08826, Republic of Korea. Email: [email protected]
Moonseo Park, M.ASCE [email protected]
Professor, Dept. of Architecture and Architectural Engineering, Seoul National Univ., Seoul 08826, Republic of Korea. Email: [email protected]
Associate Professor, Dept. of Architecture and Architectural Engineering, Seoul National Univ., Seoul 08826, Republic of Korea (corresponding author). ORCID: https://orcid.org/0000-0002-6733-2216. Email: [email protected]

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