Using Learning to Predict Average Cooperation

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Abstract/Contents

Abstract: We predict cooperation rates across treatments in the experimental play of the indefinitely repeated prisoner’s dilemma using simulations of a simple learning model. We suppose that learning and the game parameters only influence play in the initial round of each supergame. Using data from 17 papers, we find that our model predicts out-of-sample cooperation at least as well as more complicated models with more parameters and harder-to-interpret machine learning algorithms. Our results let us predict how cooperation rates change with longer experimental sessions, and help explain past findings on the role of strategic uncertainty.

Type of resource	text
Date created	August 13, 2021

Author	Fudenberg, Drew
Author	Karreskog, Gustav
Organizer of meeting	Exley, Christine
Organizer of meeting	Marquina, Alejandro Martínez
Organizer of meeting	Niederle, Muriel
Organizer of meeting	Roth, Alvin
Organizer of meeting	Vesterlund, Lise

DOI	https://doi.org/10.25740/rq985nd5994
Location	https://purl.stanford.edu/rq985nd5994

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License: This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).

Preferred citation: Fudenberg, D. and Karreskog, G. (2022). Using Learning to Predict Average Cooperation. Stanford Digital Repository. Available at https://purl.stanford.edu/rq985nd5994

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