ArticlePNAS nexus2022
Evolutionary instability of selfish learning in repeated games.
Article in PNAS nexus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Evolution of noisy learning in games.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- On the optimal integration of intelligent agents into network systems to steer cooperation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The dynamics of cooperation in asymmetric public goods games.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- From simultaneous to leader-follower play in direct reciprocity.PNAS nexus · 2026Article
- Unilateral incentive alignment in two-agent stochastic games.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Conditional cooperation with longer memory.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Resolving selfish and spiteful interdependent conflict.Proceedings. Biological sciences · 2024Review
- Grouping promotes both partnership and rivalry with long memory in direct reciprocity.PLoS computational biology · 2023Article
- Adaptive dynamics of memory-one strategies in the repeated donation game.PLoS computational biology · 2023Article
- Direct reciprocity between individuals that use different strategy spaces.PLoS computational biology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Across many domains of interaction, both natural and artificial, individuals use past experience to shape future behaviors. The results of such learning processes depend on what individuals wish to maximize. A natural objective is one's own success. However, when two such "selfish" learners interact with each other, the outcome can be detrimental to both, especially when there are conflicts of interest. Here, we explore how a learner can align incentives with a selfish opponent. Moreover, we consider the dynamics that arise when learning rules themselves are subject to evolutionary pressure. By combining extensive simulations and analytical techniques, we demonstrate that selfish learning is unstable in most classical two-player repeated games. If evolution operates on the level of long-run payoffs, selection instead favors learning rules that incorporate social (other-regarding) preferences. To further corroborate these results, we analyze data from a repeated prisoner's dilemma experiment. We find that selfish learning is insufficient to explain human behavior when there is a trade-off between payoff maximization and fairness.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.