| dc.description.abstract | One of the most important considerations in winning a competitive bid is the determination of an optimum strategy developed by predicting the competitor's most probable actions. There may be some common factors for different contractors in establishing their bid prices, such as references for cost estimating, construction materials, site conditions, or labor prices. Those dependencies from past bids can be used to improve the strategy to predict future bids. By identifying the interrelationships between bidders with statistical correlations, this study provides an overview of how correlations among bidders influence the bidders winning probability. With data available for over 7,000 Michigan Department of Transportation highway projects that can be used to calculate correlations between the different contractors, a Monte Carlo simulation is used to generate correlated random variables and the probability of winning from the results of the simulation. The primary focus of this paper outlines the use of conditional probability for predicting the probability of winning to establish a contractor's strategy for remaining bids with their estimated bid price and known information about competitors from past data. If a contractor estimated his/her bid price to be lower than his/her average bid, a higher probability of winning would be achieved with competitors who have a low correlation with the contractor. Conversely, the lower probability of winning decreases as the contractor bid with highly correlated contractors when their bid price is estimated to be higher than the average bid. | en |
