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Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

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Evolutionary instability of selfish learning in repeated games.

Alex McAvoy^1,2, Julian Kates-Harbeck³, Krishnendu Chatterjee⁴

¹Department of Mathematics, University of Pennsylvania, Philadelphia, PA, USA.

|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.

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The HoneyComb Paradigm for Research on Collective Human Behavior

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Published on: January 19, 2019

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.