Learning is Revelation in Disguise: Improved Regret and Equivalence Results for Dynamic Pricing
Shiliang Zuo's breakthrough shows learning and revelation are mathematically equivalent in pricing.
Shiliang Zuo's paper tackles dynamic pricing with strategic, non-myopic buyers who discount future utility. The first result shows menu mechanisms—offering multiple allocation-payment contracts—achieve O(T_γ log T_γ) regret, where T_γ is the buyer's effective discounted time horizon. This improves all prior posted-price bounds, which only extract binary accept/reject signals and suffer from higher regret.
The second, more conceptual contribution establishes a fundamental equivalence: indirect learning mechanisms (adaptive, data-driven algorithms from machine learning) and direct revelation mechanisms (type elicitation from economics) achieve identical optimal regret. This proves that learning is revelation in disguise, unifying two paradigms in pricing theory.
- Menu mechanisms achieve O(T_γ log T_γ) regret, improving all prior posted-price bounds.
- Establishes equivalence between adaptive learning algorithms and direct revelation mechanisms.
- Proves learning is revelation in disguise, unifying computer science and economics approaches.
Why It Matters
Unifies machine learning and economics for better dynamic pricing algorithms with provable guarantees.