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Updated: Jul 1, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Cooperation among unequal players with aspiration-driven learning.

Fang Chen¹, Lei Zhou², Long Wang^1,3

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

Journal of the Royal Society, Interface

|March 12, 2024

Summary

Cooperation evolves even with inequality using aspiration-driven learning rules, outperforming payoff-driven methods. High aspirations benefit cooperation when productive players have greater resources.

Keywords:

aspiration cooperation evolutionary dynamics inequality reciprocity

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Area of Science:

Evolutionary Game Theory
Behavioral Economics

Background:

Direct reciprocity typically requires player equality for cooperation.
Ubiquitous inequality challenges the evolution of cooperation.
Existing models often use payoff-driven learning, which requires strategy inference.

Purpose of the Study:

Investigate cooperation evolution among unequal players.
Explore aspiration-driven learning rules in asymmetric games.
Compare aspiration-driven vs. payoff-driven learning for cooperation.

Main Methods:

Modeling asymmetric games with unequal players.
Implementing aspiration-driven learning rules.
Analyzing evolutionary dynamics under inequality.

Main Results:

Aspiration-driven learning promotes higher cooperation than payoff-driven learning across inequalities.
High aspirations enhance cooperation when productive players have higher endowments.
Cooperation is feasible under specific conditions in asymmetric games.

Conclusions:

Aspiration-driven learning effectively promotes cooperation among unequal players.
Aspiration-based decision-making can be more beneficial for collective outcomes.
This study offers a novel perspective on cooperation in unequal societies.