Can Government Incentive and Penalty Mechanisms Effectively Mitigate Tacit Collusion in Platform Algorithmic Operations?

Algorithmic collusion essentially constitutes a form of monopolistic agreement that utilizes algorithms as tools for signaling collusion, making it particularly challenging for both consumers and antitrust enforcement agencies to detect. Algorithmic collusion can be primarily categorized into two distinct types: explicit collusion and tacit collusion. This paper specifically investigates the phenomenon of platform-driven tacit algorithmic collusion within the platform economy. Employing an evolutionary game theory approach, we conduct a comprehensive simulation analysis of the economic system involving four key stakeholders: government regulators, platform operators, in-platform merchants, and consumers. This paper primarily investigates the conditions that may reduce the likelihood of platforms engaging in algorithmic tacit collusion, examines how government incentive–penalty mechanisms influence such collusive behaviors, and provides an in-depth analysis of the critical roles played by both in-platform merchants and consumers in detecting and exposing these practices.