Dynamic policies to learn and earn in a customized pricing context

Event Details
  • Date/Time:
    • Thursday February 18, 2010
      10:00 am - 11:00 am
  • Location: IC 109
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Summaries

Summary Sentence: Dynamic policies to learn and earn in a customized pricing context

Full Summary: Dynamic policies to learn and earn in a customized pricing context

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

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School of Industrial and Systems Engineering (ISYE)

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Categories
Seminar/Lecture/Colloquium
Keywords
profit
Status
  • Created By: Anita Race
  • Workflow Status: Published
  • Created On: Feb 1, 2010 - 6:58am
  • Last Updated: Oct 7, 2016 - 9:49pm