Prediction, Judgment and Complexity: A Theory of Decision Making and Artificial Intelligence

26 Pages Posted: 24 Jan 2018 Last revised: 14 Apr 2018

See all articles by Ajay Agrawal

Ajay Agrawal

University of Toronto - Rotman School of Management; National Bureau of Economic Research (NBER)

Joshua S. Gans

University of Toronto - Rotman School of Management; NBER

Avi Goldfarb

University of Toronto - Rotman School of Management

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Date Written: April 14, 2018

Abstract

We interpret recent developments in the field of artificial intelligence (AI) as improvements in prediction technology. In this paper, we explore the consequences of improved prediction in decision-making. To do so, we adapt existing models of decision-making under uncertainty to account for the process of determining payoffs. We label this process of determining the payoffs ‘judgment.’ There is a risky action, whose payoff depends on the state, and a safe action with the same payoff in every state. Judgment is costly; for each potential state, it requires thought on what the payoff might be. Prediction and judgment are complements as long as judgment is not too difficult. We show that in complex environments with a large number of potential states, the effect of improvements in prediction on the importance of judgment depend a great deal on whether the improvements in prediction enable automated decision-making. We discuss the implications of improved prediction in the face of complexity for automation, contracts, and firm boundaries.

Keywords: artificial intelligence, prediction, judgment, complexity, bounded rationality

JEL Classification: D81

Suggested Citation

Agrawal, Ajay and Gans, Joshua S. and Goldfarb, Avi, Prediction, Judgment and Complexity: A Theory of Decision Making and Artificial Intelligence (April 14, 2018). Rotman School of Management Working Paper No. 3103156. Available at SSRN: https://ssrn.com/abstract=3103156 or http://dx.doi.org/10.2139/ssrn.3103156

Ajay Agrawal

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Joshua S. Gans (Contact Author)

University of Toronto - Rotman School of Management ( email )

Canada

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

NBER ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Avi Goldfarb

University of Toronto - Rotman School of Management ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada
416-946-8604 (Phone)
416-978-5433 (Fax)

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