Enhancing Pricing Practices of Subcontractors: A Comparative Analysis Using Simulation Modeling
Publication: Computing in Civil Engineering 2023
ABSTRACT
The construction industry often relies on subcontracting, where subcontractors bid for portions of a project before general contractors bid for the entire project in a process referred to as multi-stage bidding (MSG). MSG can be complex, and winning bidders may be burdened with underestimating their bids and encountering the winner’s curse. Despite various studies investigating this issue, further research is necessary to examine bidding strategies for subcontractors. This paper addresses this research need by exploring and comparing how bidding strategies based on reinforcement learning and game theory could aid subcontractors in mitigating the winner’s curse in MSG. The authors used a multi-step research methodology comprised of (1) formulating an MSG framework; (2) incorporating MSG game-theoretic bid function as the adopted game theory-based strategy, and modified Roth-Erev algorithm as the adopted reinforcement learning-based strategy; and (3) comparing the results of the two bidding strategies using an MSG simulation model. Results revealed that the reinforcement learning-based bidding strategy was more effective in mitigating the winner’s curse for the subcontractors in MSG compared to the game theory-based bidding strategy. Ultimately, this research may improve subcontractors’ pricing practices and support them in managing the complexities and uncertainties inherent in MSG decision-making.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Ahmed, M. O. (2015). Construction bidding and the winner’s curse. Master’s Thesis. Civil and Environmental Engineering Dept, Mississippi State University, Mississippi, USA.
Ahmed, M. O., and El-adaway, I. H. (2022). An integrated game-theoretic and reinforcement learning modeling for multi-stage construction and infrastructure bidding. Construction Management and Economics, 1–25.
Ahmed, M. O., El-adaway, I. H., and Coatney, K. T. (2022). Solving the negative earnings dilemma of multistage bidding in public construction and infrastructure projects: A game theory–based approach. Journal of management in engineering, 38(2), 04021087.
Ahmed, M. O., El-adaway, I. H., Coatney, K. T., and Eid, M. S. (2015). Multi-stage bidding for construction contracts: a game theory approach. In Proc., 5th International/ 11th Construction Specialty Conference, Vancouver, British Columbia.
Ahmed, M. O., El-adaway, I. H., Coatney, K. T., and Eid, M. S. (2016). Construction bidding and the winner’s curse: Game theory approach. Journal of Construction Engineering and Management, 142(2): 04015076.
Assaad, R., Ahmed, M. O., El-adaway, I. H., Elsayegh, A., and Nadendla, V. S. S. (2021). Comparing the Impact of Learning in Bidding Decision-Making Processes Using Algorithmic Game Theory. Journal of Management in Engineering, 37(1): 04020099.
Chao, L. C., and Liaw, S. J. (2019). Fuzzy Logic Model for Determining Minimum Overheads-Cum-Markup Rate. J. of Const. Engineering and Management, 145(4), 04019008.
Friedman, L. (1956). A competitive-bidding Strategy. Operations Research, 4(1), 104–112.
Goeree, J. K., and Offerman, T. (2002). Efficiency in Auctions with Private and Common Values: Experimental Study. The American Economic Review, 92(3), 625–643.
Hedges, J. (2018). Backward induction for repeated games.
Kagel, J. H., and Levin, D. (2002). Common Value Auctions and The Winner’s Curse. Princeton: Princeton University Press.
Leśniak, A., and Plebankiewicz, E. (2015). “Modeling the decision-making process concerning participation in construction bidding.” Journal of Management in Engineering, 31(2): 04014032.
Myerson, R. B. (1991). Game Theory: Analysis of Conflict. Harvard University Press, Cambridge, MA., USA.
Pentapalli, M. (2008). A comparative study of Roth-Erev and modified Roth-Erev reinforcement learning algorithms for uniform-price double auctions. Master of Science Thesis, Department of Computer Science: Iowa State University, Ames, Iowa, USA.
Rastegar, H., Shirani, B. A., Mirmohammadi, S. H., and Bajegani, E. A. (2021). Stochastic Programming Model for Bidding Price Decision in Construction Projects. Journal of Construction Engineering and Management, 147(4), 04021025.
Sutton, R. S., and Barto, A. G. (2018). Reinforcement Learning: An Introduction. Second edition, The MIT Press. Cambridge, MA, USA.
Tan, W., and Suranga, H. (2008). The winner’s curse in the Sri Lankan construction industry. International Journal of Construction Management, 8(1), 29–35.
Wang, X., Ye, K., and Arditi, D. (2021). Embodied Cost of Collusive Bidding: Evidence from China’s Construction Industry. J. of Constr. Eng. and Management, 147(6), 04021037.
Yiu, C. Y., and Tam, C. S. (2006). Rational under‐pricing in bidding strategy: A real options model. Construction Management and Economics, 24(5), 475–484.
Information & Authors
Information
Published In
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Bids
- Business management
- Comparative studies
- Construction engineering
- Construction management
- Contracts and subcontracts
- Decision making
- Engineering fundamentals
- Game theory
- Methodology (by type)
- Mitigation and remediation
- Models (by type)
- Practice and Profession
- Pricing
- Project management
- Research methods (by type)
- Simulation models
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.