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TITLE: Dynamic policies to learn and earn in a customized pricing context
SPEAKER: J. Michael Harrison
ABSTRACT:
Motivated by applications in financial services, we consider the following customized pricing problem. A seller of some good or service (like auto loans or small business loans) confronts a sequence of potential customers numbered 1, 2, … , T. These customers are drawn at random from a population characterized by a price-response function r(p). That is, if the seller offers price p, then the probability of a successful sale is r(p). The profit realized from a successful sale is p(p) = p – c, where c > 0 is known.
If the price-response function r(×) were also known, then the problem of finding a price p* to maximize r(p)p(p) would be simple, and the seller would offer price p* to each of the T customers. We consider the more complicated case where r(×) is fixed but initially unknown: roughly speaking, the seller wants to choose prices sequentially so as to maximize the total profit earned from the T potential customers; each successive choice involves a trade-off between refined estimation of the unknown price-response function (learning) and immediate profit (earning).
* Joint work with Bora Keskin and Assaf Zeevi
