Will Regulation Break the Algorithmic Cartel? A Comparative Analysis of Strategic Adaptation Among AI Models in Oligopolistic Markets

In today's strategic management landscape, autonomous pricing algorithms are changing how companies make decisions and compete in oligopolistic markets. Today's scientific research has warned that, because AI-driven systems operate without formal oversight or guidance, they may be able to evolve on their own, developing tacit collusion strategies that will create tension between a company's fiduciary duty to increase its profits and its duty to comply with regulations and avoid antitrust violations. This study investigates if regulatory changes can disrupt these algorithmic cartels and how different levels of shock to regulatory environments affect the strategic pricing behavior of AI models. As part of this research methodology, a longitudinal, asynchronous stochastic simulation was designed using the Open Router API. Thereafter, four LLMs were used as the autonomous executive officers of corporations operating in a dynamic oligopoly. The simulations progressed through sequential market phase thereby causing the models to continually calculate new expected value calculations and adjust their risk-taking exposures to asymmetrically enforced antitrust penalties. Results indicate that when properly monitored and calibrated, regulatory authorities' actions will disrupt autonomous collusion, thus compelling a shift by the corporation's manager(s) from price maximizing strategies to a focus on compliant strategies. The study indicates that managers need to add algorithmic auditing to their risk management processes immediately so they can protect themselves from significant antitrust risks. Lastly, the study adds to the strategic management body of knowledge by providing empirical evidence for a behavioral model of autonomous agents, showing that proactively managing compliance is an essential component to achieving sustained performance and corporate viability in the age of AI.