Technical Papers
Sep 15, 2015

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

Publication: Journal of Construction Engineering and Management
Volume 142, Issue 2

Abstract

In the construction industry, competitive bidding has long been used as a method for contractor selection. Because the true cost of construction is not known until the completion of the project, adverse selection is a major concern. Adverse selection is when the winner of the contract has underestimated the project’s true cost. Thus, the winning contractor will most likely earn negative or at least below normal profits. The winner’s curse is when the winning bidder submits an underestimated bid and is thus cursed by being selected to undertake the project. In the multistage bidding environment, where subcontractors are hired by a general contractor, the winner’s curse may be compounded. In general, contractors suffer from the winner’s curse for a variety of reasons including inaccurate estimates of project cost; new contractors entering the construction market; minimizing losses in case of recession of the construction industry; strong competition within the construction market; differential opportunity costs, which can affect the behavior of contractors; and the intention to win the project and then remedy the losses through change orders, claims, and other mechanisms. Using a game theory approach, this paper aims to analyze—and potentially reduce—industry exposure to the effects of the winner’s curse in construction bidding. To this end, the authors identify the degree of the winner’s curse in two common construction bidding environments; namely, single-stage bidding and multistage bidding. The objective is to compare the aforementioned two construction bidding environments and determine how learning from past bidding decisions and experiences can mitigate the winner’s curse. To this end, and through defining the relationship between the construction bidding and auction theory, the authors utilized a three-step research methodology that involved (1) presenting the symmetric risk neutral Nash equilibrium (SRNNE) as an optimal bid function; (2) developing simulation models for single and multistage construction bidding processes; and (3) analyzing the results of the simulation models, which is based on an actual dataset of projects provided by the California Department of Transportation. This research demonstrated that the majority of general contractors and subcontractors suffer from the winner’s curse in both single-stage and multistage bidding environments. Moreover, from a winner’s curse perspective, the multistage bidding environment incurs more losses than the single-stage bidding environment. However, through learning from past experiences, the multistage bidding environment provides contractors with a better opportunity to avoid the winner’s curse if compared with the single-stage bidding environment. This research should be beneficial for the profession to better understand the bidding decision-making processes. For future work, cooperative game theory can be applied with the integrated project delivery principles to help all associated parties mutually achieve their project objectives.

Get full access to this article

View all available purchase options and get full access to this article.

References

Ahmad, I., and Minkarah, I. (1988). “Questionnaire survey on bidding in construction.” J. Manage. Eng., 229–243.
Asgari, M. S., and Afshar, A. (2008). “Modeling subcontractors cooperation in time; Cooperative game theory approach.” 1st Int. Conf. on Construction in Developing Countries (ICCIDC-I), Advancing and Integrating Construction Education, Research & Practice, Karachi, Pakistan.
Ashenfelter, O., and Genesore, D. (1992). “Testing for price anomalies in real estate auctions.” American Economic Review: Papers and Proc., Vol. 82, Pittsburgh, PA, 501–505.
Bagies, A., and Fortune, C. (2006). “Bid/ no-bid decision modelling for construction projects.” 22nd Annual ARCOM Conf., Association of Researchers in Construction Management, Nottingham, U.K., 511–521.
Capen, E. C., Clapp, R. V., and Campbell, W. M. (1971). “Competitive bidding in high-risk situation.” J. Pet. Technol., 23(06), 641–653.
Coatney, K. T., Shaffer, S. L., and Menkhaus, D. J. (2012). “Auction prices, market share, and a common agent.” J. Econ. Behav. Organiz., 81(1), 61–73.
Dessauer, J. P. (1981). Book publishing, Bowker, New York.
Drew, D., and Skitmore, M. (2006). “Testing Vickery’s revenue equivalence theory in construction auctions.” J. Constr. Eng. Manage., 425–428.
Dyer, D., and Kagel, J. H. (1996). “Bidding in common value auctions: How the commercial construction industry corrects for the winner’s curse.” Manage. Sci., 42(10), 1463–1475.
Dyer, D., Kagel, J. H., and Levin, D. (1989). “A comparison of naive and experienced bidders in common value offer auctions: A laboratory analysis.” Econ. J., 99(394), 108–115.
Erev, I., and Roth, A. E. (1998). “Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria.” Am. Econ. Rev., 88(4), 848–881.
Fayek, A. (1998). “Competitive bidding strategy model and software system for bid preparation.” J. Constr. Eng. Manage., 1–10.
Friedman, L. (1956). “A competitive-bidding strategy.” Oper. Res., 4(1), 104–112.
Gates, M. (1967). “Bidding strategy and probabilities.” J. Constr. Div., 93(CO1), 74–107.
Ho, S. P. (2001). “Real options and game theoretic valuation, financing, and tendering for investments on build-operate-transfer projects.” Ph.D. thesis, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL.
Ho, S. P. (2005). “Bid compensation decision model for projects with costly bid preparation.” J. Constr. Eng. Manage., 151–159.
Ho, S. P., and Hsu, Y. (2014). “Bid compensation theory and strategies for projects with heterogeneous bidders: A game theoretic analysis.” J. Manage. Eng., 04014022.
Ho, S. P., and Liu, L. Y. (2004). “Analytical model for analyzing construction claims and opportunistic bidding.” J. Constr. Eng. Manage., 94–104.
Ioannou, P. G., and Awwad, R. E. (2010). “Below-average bidding method.” J. Constr. Eng. Manage., 936–946.
Kagel, J. H., and Levin, D. (2002). Common value auctions and the winner’s curse, Princeton University Press, Princeton, NJ.
Karl, C. K. (2014). “Simulation and gaming in construction business. Design of a module-oriented modeling approach based on system dynamics and its prototypical implementation in research and education.” Ph.D. dissertation, Dept. of Engineering Sciences, Univ. of Duisburg-Essen, Essen, Germany.
King, M., and Mercer, A. (1985). “Problems in determining bidding strategies.” J. Oper. Res. Soc., 36(10), 915–923.
Kululanga, G. K., Koutcha, W., McCaffer, R., and Edum-Fotwe, F. (2001). “Construction contractors claim process framework.” J. Constr. Eng. Manage., 309–314.
Myerson, R. B. (1991). Game theory: Analysis of conflict, Harvard University Press, Cambridge, MA.
Nash, J. (1950). “The bargaining problem.” Econometrica, 18(2), 155–162.
NetBeans IDE 7.4 [Computer software]. 〈https://netbeans.org/community/releases/74/〉.
Park, W. R., and Chapin, W. B. (1992). Construction bidding: Strategic pricing for profit, 2nd Ed., Wiley, New York.
Polat, G., Bingol, B., and Uysalol, E. (2014). “Modeling bid/no bid decision using adaptive neuro fuzzy inference system (ANFIS): A case study.” Construction Research Congress 2014, ASCE, Reston, VA, 1083–1092.
Roll, R. (1986). “The hubris hypothesis of corporate takeovers.” J. Bus., 59(2), 197–216.
Runeson, G., and Skitmore, R. M. (1999). “Tendering theory revisited.” Constr. Manage. Econ., 17(3), 285–296.
Seydel, J. (2003). “Evaluating and comparing bidding optimization effectiveness.” J. Constr. Eng. Manage., 285–292.
Turocy, T. L., and Stengel, B. V. (2001). “Game theory.”, Encyclopedia of Information Systems, Academic Press, San Diego, CA.
Unsal, H., and Taylor, J. (2011). “Modeling interfirm dependency: Game theoretic simulation to examine the holdup problem in project networks.” J. Constr. Eng. Manage., 284–293.
Wanous, M., Boussabaine, A. H., and Lewis, J. (2000). “To bid or not to bid, a parametric solution.” Constr. Manage. Econ., 18(4), 457–466.
Wilson, R. (1977). “A bidding model of perfect competition.” Rev. Econ. Stud., 44(3), 511–518.

Information & Authors

Information

Published In

Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 142Issue 2February 2016

History

Received: Feb 24, 2015
Accepted: Jul 17, 2015
Published online: Sep 15, 2015
Published in print: Feb 1, 2016
Discussion open until: Feb 15, 2016

Permissions

Request permissions for this article.

Authors

Affiliations

Muaz O. Ahmed [email protected]
M.Sc. Student, Dept. of Civil and Environmental Engineering, Mississippi State Univ., 501 Hardy Rd., 235 Walker Engineering Bldg., Mississippi State, MS 39762. E-mail: [email protected]
Islam H. El-adaway, M.ASCE [email protected]
Associate Professor and Construction Engineering and Management Program Coordinator, Dept. of Civil and Environmental Engineering, Univ. of Tennessee, 851 Neyland Dr., 417 John D. Tickle Bldg., Knoxville, TN 37996 (corresponding author). E-mail: [email protected]
Kalyn T. Coatney [email protected]
Associate Professor, Dept. of Agricultural Economics, Mississippi State Univ., 255 Tracy Dr., 102 Lloyd-Ricks Watson Bldg., Mississippi State, MS 39762. E-mail: [email protected]
Mohamed S. Eid [email protected]
Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Univ. of Tennessee, 851 Neyland Dr., 324 John D. Tickle Bldg., Knoxville, TN 37996. E-mail: [email protected]

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.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share