NDLI: Learning in nonlinear pricing with unknown utility functions

Content Provider	Springer Nature Link
Author	Berg, Kimmo Ehtamo, Harri
Copyright Year	2009
Abstract	Optimal screening is one of the basic models of contracting under incomplete information, and we study the problem in a quality pricing application. We present a simple numerical method for solving the pricing problem when the firm has limited information about the buyers’ utility functions. In the method, the firm learns the optimal price schedule as the demand data is collected. We examine what the firm can learn about the preferences by observing the sales, and how the revealed information can be used in adjusting the quality-price bundles to increase the profit. We analyze the properties of the solution and derive the first-order optimality conditions under different assumptions. We show that the problem can be solved by making use of these optimality conditions together with the buyers’ marginal valuations. The firm can estimate the marginal valuations either by offering linear tariffs or by selling test bundles near the current solution.
Starting Page	375
Ending Page	392
Page Count	18
File Format	PDF
ISSN	02545330
Journal	Annals of Operations Research
Volume Number	172
Issue Number	1
e-ISSN	15729338
Language	English
Publisher	Springer US
Publisher Date	2009-10-06
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Nonlinear pricing Optimal screening Limited information Learning Computation Theory of Computation Combinatorics Operations Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Management Science and Operations Research

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