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Related Experiment Video

Updated: Mar 17, 2026

Pavlovian Conditioned Approach Training in Rats

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

Reinforcement Learning Explains Conditional Cooperation and Its Moody Cousin.

Takahiro Ezaki^1,2,3,4, Yutaka Horita^3,4, Masanori Takezawa^5,6

¹Research Center for Advanced Science and Technology, The University of Tokyo, Meguro-ku, Tokyo, Japan.

Plos Computational Biology

|July 21, 2016

Summary

Aspiration learning explains conditional cooperation in social dilemmas. Individuals learn to cooperate by seeking satisfactory outcomes, mimicking conditional cooperation without direct social information.

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Area of Science:

Behavioral Economics
Game Theory
Computational Social Science

Background:

Direct reciprocity sustains cooperation in two-player scenarios.
Larger groups exhibit conditional cooperation, but underlying mechanisms are unclear.

Purpose of the Study:

Provide a proximate account for conditional cooperation in multiplayer social dilemmas.
Explain moody and non-moody conditional cooperation using a novel learning model.

Main Methods:

Modeled individuals using aspiration learning, a reinforcement learning type.
Simulated multiplayer social dilemma games (Prisoner's Dilemma, Public Goods Games).
Assessed behavior in well-mixed groups and networks without explicit social information.

Main Results:

Aspiration learning phenomenologically replicates conditional cooperation.
Learners exhibited behavior consistent with noisy GRIM-like strategies.
The model explains both moody and non-moody conditional cooperation.

Conclusions:

Myopic aspiration learning offers a parsimonious explanation for observed human behavior in social dilemmas.
This mechanism operates without explicit knowledge of others' actions.
Reinforcement learning provides a unified framework for understanding cooperation strategies.