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Updated: Nov 30, 2025

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
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Learning enables adaptation in cooperation for multi-player stochastic games.

Feng Huang1,2, Ming Cao2, Long Wang1

  • 1Center for Systems and Control, College of Engineering, Peking University, Beijing 100871, People's Republic of China.

Journal of the Royal Society, Interface
|November 17, 2020
PubMed
Summary

Cooperative behaviors can evolve in changing environments through reinforcement learning. Learning aids adaptation in weak social dilemmas but hinders cooperation in strong ones.

Keywords:
adaptive behaviourevolutionary game theoryreinforcement learningsocial dilemmastochastic game

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Area of Science:

  • Ecology
  • Evolutionary Biology
  • Game Theory

Background:

  • Environmental fluctuations are key to population dynamics.
  • How individuals adapt behavior to social dilemmas amid environmental change remains unclear.

Purpose of the Study:

  • Investigate if cooperation evolves in changing environments using evolutionary game theory.
  • Incorporate reinforcement learning into stochastic games to model adaptive behavior.

Main Methods:

  • Developed a stochastic game framework with reinforcement learning.
  • Analyzed conditions favoring cooperation over defection with slight learning influence.
  • Compared learning mechanisms against non-learning decision rules.

Main Results:

  • Derived an analytical condition explaining how environments mediate cooperation.
  • Learning enhances cooperation in weak social dilemmas.
  • Learning hinders cooperation in strong social dilemmas.

Conclusions:

  • Reinforcement learning enables individuals to adapt to environmental variations in social-ecological dilemmas.
  • Environmental mediation is crucial for understanding cooperative behavior evolution.