An algorithmic approach for modelling customer expectations
AbstractThe scope of this article is to discuss the dynamics of formatting customer expectations in financial services-under two models for assessing cumulative learning in customer expectations. The first model is a classical Bayesian one, the second model is an entirely new application of the Repetitive Stochastic Guesstimation (RSG) algorithm. The traditional assumption of postulating that empirical data have been generated from an underlying probability has been questioned even by orthodox theorists. Our research strategy is to cast this problem in the form of an optimization problem and show that RSG algorithm will produce a relevant solution for the original economic problem.
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Bibliographic InfoArticle provided by Economic Publishing House in its journal Management & Marketing.
Volume (Year): 4 (2009)
Issue (Month): 1 (Spring)
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Bayesian updating; Computational economics; Customer expectations; Repetitive Stochastic Guesstimation.;
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- Charemza, Wojciech W, 2002.
Journal of Forecasting,
John Wiley & Sons, Ltd., vol. 21(6), pages 417-33, September.
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