'Outlier Blindness': Efficient Coding Generates an Inability to Represent Extreme Values

59 Pages Posted: 29 Mar 2018 Last revised: 13 Jul 2020

See all articles by Elise Payzan-LeNestour

Elise Payzan-LeNestour

University of New South Wales; Financial Research Network (FIRN)

Michael Woodford

Columbia University, Graduate School of Arts and Sciences, Department of Economics; National Bureau of Economic Research (NBER)

Date Written: July 12, 2020

Abstract

How do people record information about the economic outcomes they observe in their environment? Building on a well-established neuroscientific framework, we propose a model in which people are not attuned to making distinctions between realized outcomes that they seldom expect to encounter. We provide evidence for such `outlier blindness' in a series of controlled laboratory experiments and discuss important implications of our model for economic decision-making. In particular, we show how outlier blindness provides a microfoundation for well-established empirical features of risk perception such as tail risk neglect and context-dependent risk attitude.

Keywords: Tail risk, Instability, Imprecise perception, Efficient coding, Adaptation, Decision making under uncertainty, Behavioral finance, Neuroeconomics, Experiments

JEL Classification: C91, D87

Suggested Citation

Payzan-LeNestour, Elise and Woodford, Michael, 'Outlier Blindness': Efficient Coding Generates an Inability to Represent Extreme Values (July 12, 2020). Available at SSRN: https://ssrn.com/abstract=3152166 or http://dx.doi.org/10.2139/ssrn.3152166

Elise Payzan-LeNestour (Contact Author)

University of New South Wales ( email )

Australian School of Business
Sydney, NSW 2052
Australia

HOME PAGE: http://www.elisepayzan.com/

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

Michael Woodford

Columbia University, Graduate School of Arts and Sciences, Department of Economics ( email )

420 W. 118th Street
New York, NY 10027
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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