Interpreting and Countering Collusion in Deep-Learning Pricing Algorithms (opens in new tab)

Algorithmic pricing raises a question of interpretation as well as intervention: when autonomous deep-learning pricing systems sustain supracompetitive prices, what strategic pattern have they learned, and how might market institutions alter it? This paper develops an interpretable framework for studying learned collusion in repeated pricing environments. The framework embeds strategic deep learning networks in a differentiated-products Bert...