An Essay on James-Stein Shrinkage and Anchoring

13 Pages Posted: 3 Jan 2018 Last revised: 5 Dec 2018

See all articles by Leon Yang Chu

Leon Yang Chu

University of Southern California - Data Sciences and Operations; Cheung Kong Graduate School of Business

Date Written: December 1, 2018

Abstract

Biases may reduce variability, which increases the decision maker's (concave) expected utility. Hence seeking unbiased estimates can be a strictly dominated decision approach under the expected utility criterion. Moreover, James-Stein shrinkage demonstrates that, by aggregating unrelated tasks and leveraging supposedly irrelevant information, the decision maker may actually improve an unbiased decision by "shrinking" it toward an arbitrarily chosen reference point. This revelation points to the difference between probability theory and statistical inference, and it leads to novel testable hypotheses for estimation problems. We reference the behavioral economics literature to illustrate that, compared with models based on probability theory, those based on statistical inference are better at reconciling the difference between normative and descriptive decision theory.

Keywords: Decision Making under Uncertainty, Decision Theory, James-Stein Shrinkage, Anchoring

JEL Classification: B20, C10, C11, D01, D81

Suggested Citation

Chu, Leon Yang, An Essay on James-Stein Shrinkage and Anchoring (December 1, 2018). Available at SSRN: https://ssrn.com/abstract=3094490 or http://dx.doi.org/10.2139/ssrn.3094490

Leon Yang Chu (Contact Author)

University of Southern California - Data Sciences and Operations ( email )

701 Exposition Blvd
Los Angeles, CA
United States

Cheung Kong Graduate School of Business ( email )

Oriental Plaza, Tower E3
One East Chang An Avenue
Beijing, 100738
China

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