Prof. Assaf Zeevi
Columbia University
Talk:
Dynamic Pricing and the 'Learning and Earning' Problem
Abstract:
This talk will survey some recent work in the area of dynamic pricing, with particular emphasis on the following problem. A seller (monopolist) offers prices sequentially to a stream of potential customers, observing either success or failure in each sales attempt. Unlike most antecedent literature, the seller is *not* assumed to have a priori knowledge of the parameters of the underlying demand model. In this setting, each price decision involves a trade-off between learning (gaining information) and earning (revenue optimization). The talk will attempt to highlight some of the salient features of this class of dynamic optimization problems under incomplete information. In particular, we will indicate some potential issues with widely used pricing policies, propose modifications thereof, and examine implications on common practices. In doing so we also hope to highlight more broadly some interesting connections between estimation theory (statistics) and on-line decision making (operations).
Biography:
Assaf Zeevi is the Henry Kravis Professor of Business at the Graduate School of Business, Columbia University. He is broadly interested in the formulation and analysis of mathematical models motivated by problems in business settings. Assaf received his Ph.D. from Stanford University in 2001, and has been a faculty at Columbia University ever since, while also holding a visiting position at Stanford University. He is a recipient of a CAREER Award from the National Science Foundation, an IBM Faculty Award, a Google Research Award, and the Dean's Award for Teaching Excellence. Assaf currently holds several editorial positions in the main INFORMS journals in his areas of expertise.
