The Limits of Optimal Pricing in the Dark
Dec 6, 2021
Speakers
Quinlan Dawkins
Speaker · 0 followers
Minbiao Han
Speaker · 0 followers
Haifeng Xu
Speaker · 0 followers
About
A ubiquitous learning problem in today’s digital market is, during repeated interactions between a seller and a buyer, how a seller can gradually learn optimal pricing decisions based on the buyer’s past purchase responses. A fundamental challenge of learning in such a strategic setup is that the buyer will naturally have incentives to manipulate his responses in order to induce more favorable learning outcomes for him. To understand the limits of the seller’s learning when facing such a strategic and possibly manipulative buyer, we study a natural yet powerful buyer manipulation strategy. That is, before the pricing game starts, the buyer simply commits to “imitate” a different value function by pretending to always react optimally according to this imitative value function. We fully characterize the optimal imitative value function that the buyer should imitate as well as the resultant seller revenue and buyer surplus under this optimal buyer manipulation. Our characterizations reveal many useful insights about what happens at equilibrium. For example, a seller with concave production cost will obtain essentially 0 revenue at equilibrium whereas the revenue for a seller with convex production cost is the Bregman divergence of her cost function between no production and certain production. Finally, and importantly, we show that a more powerful class of pricing schemes does not necessarily increase, in fact, may be harmful to, the seller’s revenue. Our results not only lead to an effective prescriptive way for buyers to manipulate learning algorithms but also shed lights on the limits of what a seller can really achieve when pricing in the dark.A ubiquitous learning problem in today’s digital market is, during repeated interactions between a seller and a buyer, how a seller can gradually learn optimal pricing decisions based on the buyer’s past purchase responses. A fundamental challenge of learning in such a strategic setup is that the buyer will naturally have incentives to manipulate his responses in order to induce more favorable learning outcomes for him. To understand the limits of the seller’s learning when facing such a strateg…
Organizer
NeurIPS 2021
Account · 1.9k followers
About NeurIPS 2021
Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
Like the format? Trust SlidesLive to capture your next event!
Professional recording and live streaming, delivered globally.
Sharing
Recommended Videos
Presentations on similar topic, category or speaker
Mixture of Basis for Interpretable Continual Learning with Distribution Shifts
Watch later
Mengda Xu, …
Total of 1 viewers voted for saving the presentation to eternal vault which is 0.1%
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
Watch later
Total of 2 viewers voted for saving the presentation to eternal vault which is 0.2%
AA3DNet: Attention Augmented Real Time 3D Object Detection
Watch later
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
The Case for Real-World Reinforcement Learning
Watch later
Total of 1 viewers voted for saving the presentation to eternal vault which is 0.1%
Understanding and Improving Early Stopping for Learning with Noisy Labels
Watch later
Yingbin Bai, …
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation
Watch later
Lin Guan, …
Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%