High-stakes failures of backward induction

Bouke Klein Teeselink; Dennie van Dolder; Martijn J. van den Assem; Jason D. Dana

doi:10.1016/j.geb.2024.07.001

High-stakes failures of backward induction

Bouke Klein Teeselink^*, Dennie van Dolder, Martijn J. van den Assem, Jason D. Dana

^*Corresponding author for this work

Political Economy

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Abstract

We examine high-stakes strategic choice using more than 40 years of data from the American TV game show The Price Is Right. In every episode, contestants play the Showcase Showdown, a sequential game of perfect information for which the optimal strategy can be found through backward induction. We find that contestants systematically deviate from the subgame perfect Nash equilibrium. These departures from optimality are well explained by a modified agent quantal response model that allows for limited foresight. The results suggest that many contestants simplify the decision problem by adopting a myopic representation, and optimize their chances of beating the next contestant only. In line with learning, contestants' choices improve over the course of our sample period.

Original language	English
Journal	GAMES AND ECONOMIC BEHAVIOR
Early online date	18 Jul 2024
DOIs	https://doi.org/10.1016/j.geb.2024.07.001
Publication status	E-pub ahead of print - 18 Jul 2024

Keywords

Backward induction
Limited foresight
Omission bias
Quantal response equilibrium
Subgame perfect Nash equilibrium

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10.1016/j.geb.2024.07.001

High-stakes failure of_Teeselink_publishedonline18July2024_Gold_VOR (CC BY)Licence: CC BY

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title = "High-stakes failures of backward induction",

abstract = "We examine high-stakes strategic choice using more than 40 years of data from the American TV game show The Price Is Right. In every episode, contestants play the Showcase Showdown, a sequential game of perfect information for which the optimal strategy can be found through backward induction. We find that contestants systematically deviate from the subgame perfect Nash equilibrium. These departures from optimality are well explained by a modified agent quantal response model that allows for limited foresight. The results suggest that many contestants simplify the decision problem by adopting a myopic representation, and optimize their chances of beating the next contestant only. In line with learning, contestants' choices improve over the course of our sample period.",

keywords = "Backward induction, Limited foresight, Omission bias, Quantal response equilibrium, Subgame perfect Nash equilibrium",

author = "{Klein Teeselink}, Bouke and {van Dolder}, Dennie and {van den Assem}, {Martijn J.} and Dana, {Jason D.}",

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AU - Klein Teeselink, Bouke

AU - van Dolder, Dennie

AU - van den Assem, Martijn J.

AU - Dana, Jason D.

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N2 - We examine high-stakes strategic choice using more than 40 years of data from the American TV game show The Price Is Right. In every episode, contestants play the Showcase Showdown, a sequential game of perfect information for which the optimal strategy can be found through backward induction. We find that contestants systematically deviate from the subgame perfect Nash equilibrium. These departures from optimality are well explained by a modified agent quantal response model that allows for limited foresight. The results suggest that many contestants simplify the decision problem by adopting a myopic representation, and optimize their chances of beating the next contestant only. In line with learning, contestants' choices improve over the course of our sample period.

AB - We examine high-stakes strategic choice using more than 40 years of data from the American TV game show The Price Is Right. In every episode, contestants play the Showcase Showdown, a sequential game of perfect information for which the optimal strategy can be found through backward induction. We find that contestants systematically deviate from the subgame perfect Nash equilibrium. These departures from optimality are well explained by a modified agent quantal response model that allows for limited foresight. The results suggest that many contestants simplify the decision problem by adopting a myopic representation, and optimize their chances of beating the next contestant only. In line with learning, contestants' choices improve over the course of our sample period.

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KW - Omission bias

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High-stakes failures of backward induction

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Keywords

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