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Evolutionary instability of selfish learning in repeated games.
Alex McAvoy1,2, Julian Kates-Harbeck3, Krishnendu Chatterjee4
1Department of Mathematics, University of Pennsylvania, Philadelphia, PA, USA.
PNAS Nexus
|January 30, 2023
Summary
Selfish learning in interactions is unstable. Evolution favors learning rules incorporating social preferences for better long-term outcomes, especially when fairness is considered alongside payoffs.
Area of Science:
- Behavioral economics
- Evolutionary game theory
- Computational social science
Background:
- Individuals learn from experience to optimize future actions.
- Selfish learning, focused solely on individual success, can lead to detrimental outcomes in interactions with conflicting interests.
Purpose of the Study:
- To investigate methods for aligning incentives between selfish learners.
- To analyze the evolutionary dynamics of learning rules under selection pressure.
- To determine the stability of selfish learning in repeated games.
Main Methods:
- Extensive computer simulations of two-player repeated games.
- Analytical techniques to model learning dynamics.
- Analysis of experimental data from the repeated prisoner's dilemma.
Main Results:
- Selfish learning is demonstrated to be unstable in most classical two-player repeated games.
- Evolutionary selection favors learning rules that incorporate social (other-regarding) preferences for long-run payoffs.
- Selfish learning alone cannot fully explain human behavior in scenarios balancing payoff maximization and fairness.
Conclusions:
- In interactive scenarios, particularly those with conflicting interests, purely selfish learning is evolutionarily unstable.
- The incorporation of social preferences into learning rules is favored by natural selection acting on long-term success.
- Human behavior in economic games suggests a complex interplay between self-interest and fairness considerations, challenging purely selfish learning models.
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