The low bid method has been the most common competitive bid selection approach used for public projects in the U.S. construction industry. This method is usually coupled with a prequalification process to ensure that the lowest bidder has the financial capacity, the necessary experience, and enough bonding capacity to take charge of the project and to perform the work according to the project's requirements. However, driven by their bad financial status or by their urgent need for work, some contractors tend to abuse the free and price-directed competitive nature of the low bid method by deliberately submitting extremely low bid prices in order to enhance their chance of winning and to at least cover their general and administrative expenses. Thus it is possible for the project to be awarded to an accidental or deliberate unrealistic low bid. This often leads to cost overruns, schedule delays, claims and further disputes between parties during construction.

This research investigates alternative competitive bidding methods that have the potential to remedy the aforementioned drawback of the low bid method. Monte Carlo simulation approach is used to study and model average-based bidding methods where the winning bid is defined in relation to the average of submitted bids. This research also studies two other competitive bidding methods: the second low bid method where the second lowest bid is awarded the project and the median bid method where the winner is defined to be the median bid. The merits and shortcomings of these methods are analyzed and compared relative to each other and to the low bid method through produced nomograms that depict the winning probability, the optimum markup and the optimum expected profit under each.

After studying the aforementioned bidding methods from the perspective of a risk-neutral contractor, this research uses established principles of decision analysis and utility theory to develop a risk-sensitive bidding model that can be applied to each of the studied bidding methods. This model helps a contractor determine his optimal markup for a project given his risk attitude and his uncertainty about the project's estimated and final costs.