ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2022
Hybrid social learning in human-algorithm cultural transmission.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Propagation and preservation of AI-discovered problem-solving strategies in human culture.Nature communications · 2026Article
- A new sociology of humans and machines.Nature human behaviour · 2024Review
- Simple autonomous agents can enhance creative semantic discovery by human groups.Nature communications · 2024Article
- Article
- Machine culture.Nature human behaviour · 2023Review
- Superhuman artificial intelligence can improve human decision-making by increasing novelty.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Hybrid social learning in human-algorithm cultural transmission.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- From the origin of life to pandemics: emergent phenomena in complex systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Humans are impressive social learners. Researchers of cultural evolution have studied the many biases shaping cultural transmission by selecting who we copy from and what we copy. One hypothesis is that with the advent of superhuman algorithms a hybrid type of cultural transmission, namely from algorithms to humans, may have long-lasting effects on human culture. We suggest that algorithms might show (either by learning or by design) different behaviours, biases and problem-solving abilities than their human counterparts. In turn, algorithmic-human hybrid problem solving could foster better decisions in environments where diversity in problem-solving strategies is beneficial. This study asks whether algorithms with complementary biases to humans can boost performance in a carefully controlled planning task, and whether humans further transmit algorithmic behaviours to other humans. We conducted a large behavioural study and an agent-based simulation to test the performance of transmission chains with human and algorithmic players. We show that the algorithm boosts the performance of immediately following participants but this gain is quickly lost for participants further down the chain. Our findings suggest that algorithms can improve performance, but human bias may hinder algorithmic solutions from being preserved. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
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