Impact of Altering the Bid Selection Method to Below-Average Method: An Agent-Based Modeling Approach

Abstract

Numerous research proposed alternative selection methods to compensate for the drawbacks of the lowest qualified bidder (LQB) approach, including the below-average-bidder (BAB) method. While previous work has been carried out to simulate contractors’ behavior when altering the bid selection method, the literature lacks a tool that investigates how long it will take for both public and private owners to adopt the newly proposed alternative bid selection approach. To this end, this paper presents an agent-based model (ABM) that simulates the different bidding processes and tracks the impact on the owners’ utilities of switching from LQB to BAB. The ABM simulates the contractors’ learning behaviors to optimize their bidding strategies and markups throughout transitioning from LQB to BAB. Meanwhile, the ABM accounts for the owners’ utilities from using both selection methods and their preferences in utilizing one over the other using social learning. The findings of this research are validated by experts’ insights through a questionnaire survey. The model is tested against the ratio of public to private projects to provide insights into the owners’ decision-making processes and the potential market dynamics due to the proposed changes. One of the main findings of this research is that the ratio of public to private projects has a significant impact on the adoption of an alternative bid selection method. This paper contributes to the body of knowledge by proposing an approach to quantify market perturbations, the expected equilibrium, and how long the market might need to restabilize using BAB as the main selection method. Ultimately, the proposed approach would aid policymakers in understanding the impact of alternating the bid selection method, and adequately plan for the transition. In addition, the utilized methodology can be used in other managerial domains that require simulating different what-if scenarios.