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BAYESIAN MODELS FOR NEW PRODUCT PRICING
Author(s) -
Thomas Joseph,
Chhabria Prem
Publication year - 1975
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1975.tb00997.x
Subject(s) - computer science , bayesian probability , profit (economics) , mathematical optimization , sensitivity (control systems) , product (mathematics) , fraction (chemistry) , operations research , dynamic programming , econometrics , economics , microeconomics , artificial intelligence , algorithm , mathematics , geometry , chemistry , organic chemistry , electronic engineering , engineering
This paper develops an information decision system for new product pricing based on Bayesian updating of prior estimates of demand distribution parameter values and on optimization by dynamic programming. The model considers the interaction of production and pricing decisions and emphasizes the simultaneous making of both decisions. After presentation of the basic model, approximate techniques are introduced which obtain most of the benefit of the approach while requiring only a fraction of the computer cost and input data. Numerical examples using growing demand and price sensitivity are given to demonstrate the high computer cost of the first model and the relative performance (on a profit basis) of the approximate techniques.