Collusion and corruption in public procurement undermine competition, inflate contract prices, distort the allocation of public resources, and reduce the social value of awarded projects. These practices pose persistent regulatory challenges, especially in institutional contexts where enforcement is imperfect and agent behavior evolves over time.
We propose an evolutionary game model using replicator dynamics, in which three types of agents interact within the procurement process: the contracting authority, the bidders, and the regulatory agency. Each agent has two available strategies and seeks to maximize its payoff, which depends on its own decisions and those of the other agents. In our setup, bidders may engage in collusive behavior, and the contracting authority may act corruptly, capturing real-world scenarios such as bid rigging facilitated by bribery or informal agreements.
Evolutionary game theory provides a dynamic framework to study how strategies like collusion and corruption emerge and persist in populations of boundedly rational agents. Unlike static equilibrium models, it captures adaptive behavior through imitation and selection processes, without assuming full information or perfect foresight. This makes it particularly suitable for analyzing regulatory environments where incentives, sanctions, and behaviors evolve jointly.
The model parameters reflect the benefits, costs, and potential sanctions associated with each strategic combination, representing the institutional trade-offs faced by agents in diverse settings. We conduct a stability analysis of the system, characterize the eight pure-strategy equilibrium points, and explore dynamic trajectories through simulations under various relevant parameter configurations, allowing us to examine the evolution of strategies over time.
The results provide insights into how the interaction between collusion and corruption shapes procurement outcomes and inform the design of regulatory policies aimed at fostering more transparent and efficient environments.
