A game theoretic approach is applied to analyze competitive bidding in the construction industry because previous models do not consider the conflict of interest that exists among competitors. The game theoretic model improves corporate performance when compared to previous Bayesian analyses.

The game theoretic model is discussed in conjunction with construction contracting practice. Competitive bidding is formulated as a game theoretic model in which a contractor optimizes his bid price to maximize his utility or corporate performance. Using available historical data, order statistics are employed to access the distribution of estimated costs among bidders for a project. The winner's curse problem related to biased estimated cost is also solved by means of order statistics. An empirical approach is proposed to define the degree of the winner's curse in a local market.

A basic model is derived using complex mathematics. This is followed by a simplified solution that enhances the understanding and application of game theory in the construction industry. The simplified model is in a linear form that makes it practical for use in a business environment.

The historical bidding data of two contractors engaged in the construction industry are used to evaluate the proposed simplified model. The results show that, even in its linear form, the model improves the contractors' performance significantly when compared to previous Bayesian analyses.

Future research directions in game theoretic modelling for competitive bidding are suggested.