Strategic Information Disclosure in Algorithmic Pricing
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摘要
Strategic Information Disclosure in Algorithmic Pricing examines how third-party information disclosure shapes Q-learning-powered pricing under stochastic demand. We analyze three rules: no disclosure, full disclosure, and upper censorship. Upper censorship, which truthfully reveals low-demand states while pooling high-demand ones, delivers higher profits than full disclosure, consistent with theoretical predictions. However, we uncover a robust profit reversal: when the discount factor is high, no disclosure yields higher profits than full disclosure, whereas when the discount factor is low, full disclosure performs better. This pattern is exactly the opposite of classical collusion theory. These findings indicate that Q-learning agents respond systematically to information structure and that restricting information sharing may backfire when algorithms are patient, underscoring the need to reassess regulatory approaches in AI-mediated markets [page::1][page::6][page::14][page::14][page::17].
