A Paradigm for Assessing the Scope and Performance of Predictive Analytics

28 Pages Posted: 12 Jul 2018 Last revised: 15 Oct 2018

See all articles by Jeffrey Prince

Jeffrey Prince

Indiana University - Kelley School of Business - Department of Business Economics & Public Policy

Date Written: June 20, 2018

Abstract

In this paper, I outline possibilities and limitations for the scope and performance of predictive analytics within a simple paradigm. I do this by first bifurcating predictive analytics into two categories, passive and active. I contrast this categorization with current alternatives and highlight its relative merits in terms of clarity in boundaries, as well as appropriate methods for different types of prediction. I then describe the range of suitable applications, as well as the possibilities and limitations with regard to prediction accuracy, for each type of prediction. I conclude with a discussion of key ways in which an understanding of this paradigm can be valuable.

Keywords: Predictive Analytics, Paradigm, Framework, Prediction, Artificial Intelligence, Machine Learning, Econometrics, Data Mining, Diagnostics

JEL Classification: C1

Suggested Citation

Prince, Jeffrey, A Paradigm for Assessing the Scope and Performance of Predictive Analytics (June 20, 2018). Kelley School of Business Research Paper No. 18-56. Available at SSRN: https://ssrn.com/abstract=3199961 or http://dx.doi.org/10.2139/ssrn.3199961

Jeffrey Prince (Contact Author)

Indiana University - Kelley School of Business - Department of Business Economics & Public Policy ( email )

Bloomington, IN 47405
United States

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