Jeong Yeol Kim (KDI Public Policy and Management) et al. have posted “Ai Pricing Behavior Under Regulatory Variation” on SSRN. Here is the abstract:
This study experimentally examines how generative AI agents adjust pricing under four regulatory environments: no regulation; fixed detection (constant penalty probability above a threshold); linear detection (penalty probability increases with price); and periodic detection (monitoring at fixed intervals). Without regulation, AI agents choose near-monopoly prices. All regulations reduce prices, but do not induce competitive outcomes. Fixed and linear detection produce lower and more stable supra-competitive prices, while periodic detection leads to strategic evasion and higher prices. These findings suggest that AI agents adapt to enforcement structures, maintaining supra-competitive pricing even under regimes designed to deter monopolistic outcomes.