Eliciting Human Judgment for Prediction Algorithms

Management Science, Forthcoming

35 Pages Posted: 15 Jun 2020 Last revised: 21 Aug 2020

See all articles by Rouba Ibrahim

Rouba Ibrahim

University College London

Song-Hee Kim

Seoul National University - Business School

Jordan Tong

Wisconsin School of Business

Date Written: August 19, 2020

Abstract

Even when human point forecasts are less accurate than data-based algorithm predictions, they can still help boost performance by being used as algorithm inputs. Assuming one uses human judgment indirectly in this manner, we propose changing the elicitation question from the traditional direct forecast (DF) to what we call the private information adjustment (PIA): how much the human thinks the algorithm should adjust its forecast to account for information the human has that is unused by the algorithm. Using stylized models with and without random error, we theoretically prove that human random error makes eliciting the PIA lead to more accurate predictions than eliciting the DF. However, this DF-PIA gap does not exist for perfectly consistent forecasters. The DF-PIA gap is increasing in the random error that people make while incorporating public information (data that the algorithm uses) but is decreasing in the random error that people make while incorporating private information (data that only the human can use). In controlled experiments with students and Amazon Mechanical Turk workers, we find support for these hypotheses.

Keywords: laboratory experiments, behavioral operations, random error, elicitation, forecasting, prediction, discretion, expert input, private information, judgment, aggregation

Suggested Citation

Ibrahim, Rouba and Kim, Song-Hee and Tong, Jordan, Eliciting Human Judgment for Prediction Algorithms (August 19, 2020). Management Science, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3606633 or http://dx.doi.org/10.2139/ssrn.3606633

Rouba Ibrahim

University College London ( email )

1 Canada Square
London, England E145AB
United Kingdom

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

Song-Hee Kim (Contact Author)

Seoul National University - Business School ( email )

Seoul
Korea, Republic of (South Korea)

Jordan Tong

Wisconsin School of Business ( email )

975 University Avenue
Madison, WI 53706
United States

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