AI + Markets: Interactive Learning, Smart Pricing, and Optimal Allocations

We study the joint effect of pricing and recommendations on total customer satisfaction and market stability in markets such as AirBnB, Uber, Doordash, Amazon, eBay, etc. We develop an efficient algorithm to minimize customer dissatisfaction and market instability in a repeated learning setting by integrating techniqes such as collaborative filtering, explore and exploit, and bidding protocols for optimal resource allocation.

How to design assistive AI agents that learn individual user preferences, find top recommendations out of several options for their user, bid opportunistically on behalf of the user in competing markets, be aware of the changing market conditions, and learn efficiently from the vast data available from past interactions? In this project, we develop a principled approach for this task by integrating techniques such as collaborative filtering, explore and exploit, and bidding protocols for optimal resource allocation. Example applications: AirBnB, Uber, Doordash, Amazon, eBay, smart-grids.