Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumulative discounted surplus. We propose a novel algorithm that never decreases offered prices and has a tight strategic regret bound of $\Theta(\log\log T)$. This result closes the open research question on the existence of a no-regret horizon-independent weakly consistent pricing. We also show that the property of non-decreasing prices is nearly necessary for a weakly consistent algorithm to be a no-regret one.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
On consistency of optimal pricing algorithms in repeated posted-price auctions with strategic buyer
We study revenue optimization learning algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation for a good and seeks to maximize his cumula…
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer
We study revenue optimization pricing algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation. When the participants non-equally discount …
Understanding algorithmic collusion with experience replay
In an infinitely repeated pricing game, pricing algorithms based on artificial intelligence (Q-learning) may consistently learn to charge supra-competitive prices even without communication. Although concerns on algorith…
Q-LearningRobust Pricing in Dynamic Mechanism Design
Motivated by the repeated sale of online ads via auctions, optimal pricing in repeated auctions has attracted a large body of research. While dynamic mechanisms offer powerful techniques to improve on both revenue and ef…
Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders
We study revenue optimization learning algorithms for repeated second-price auctions with reserve where a seller interacts with multiple strategic bidders each of which holds a fixed private valuation for a good and seek…