Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Potential AI-Driven Algorithmic Collusion and Influential Factors in Construction Bidding
https://orcid.org/0000-0002-6733-2216
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.
Potential AI-Driven Algorithmic Collusion and Influential Factors in Construction Bidding
https://orcid.org/0000-0002-6733-2216
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.
Potential AI-Driven Algorithmic Collusion and Influential Factors in Construction Bidding
https://orcid.org/0000-0002-6733-2216
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.
Potential AI-Driven Algorithmic Collusion and Influential Factors in Construction Bidding
J. Comput. Civ. Eng.
Heo, Chan (Autor:in) / Park, Moonseo (Autor:in) / Ahn, Changbum R. (Autor:in)
01.07.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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