Noisy Foresight / Anujit Chakraborty, Chad W. Kendall.

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Chakraborty, Anujit

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w30333.Publication details: Cambridge, Mass. National Bureau of Economic Research 2022.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: Rational agents must perform backwards induction by thinking contingently about future states and actions, but failures of backwards induction and contingent reasoning are ubiquitous. How do boundedly-rational agents make decisions when they fail to correctly forecast actions in the future? We construct an individual decision-making experiment to collect a rich dataset in which subjects must reason only about their own future actions. We demonstrate substantial mistakes relative to the rational benchmark, and use the rich dataset to estimate several possible models of boundedly-rational foresight. We find that a model in which subjects expect to make more mistakes when the payoff consequences of their future actions are more similar best explains behavior.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w30333 (Browse shelf(Opens below))	Not For Loan

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August 2022.

Rational agents must perform backwards induction by thinking contingently about future states and actions, but failures of backwards induction and contingent reasoning are ubiquitous. How do boundedly-rational agents make decisions when they fail to correctly forecast actions in the future? We construct an individual decision-making experiment to collect a rich dataset in which subjects must reason only about their own future actions. We demonstrate substantial mistakes relative to the rational benchmark, and use the rich dataset to estimate several possible models of boundedly-rational foresight. We find that a model in which subjects expect to make more mistakes when the payoff consequences of their future actions are more similar best explains behavior.