Autonomous AI agents in digital markets: Economic implications for competition, pricing, and regulation

The competitive process for price-setting in digital markets is being dramatically altered through the use of autonomous artificial intelligence (AI) to make pricing decisions on behalf of humans. These systems operate autonomously and interact with each other in continuous cycles. They react in real time to market data and adapt their pricing strategies accordingly. This research analyzes the effects of various combinations of market transparency and algorithmic autonomy on price behavior, the competitive process, and consumer welfare outcomes. The analysis is conducted using a controlled simulation model that compares four pricing regimes: human-supervised, fully autonomous, isolated, and mediated. The results show that the degree of market transparency and the degree of platform oversight of AI decision-making have a far greater impact on the market's final outcome than the level of algorithmic autonomy. Some configurations increase efficiency and profit while increasing the risk of coordination failure, volatility, and concentration. In addition, the findings demonstrate inherent structural trade-offs between market efficiency, market stability, and competitive processes in markets where AI is used as an autonomous decision-making agent. These results also highlight the limitations of attempting to regulate AI through intent-based regulations and provide insights into how autonomous AI decision-making agents alter the structure of digital markets.