Hybrid social learning in human-Algorithm cultural transmission

L. Brinkmann, D. Gezerli, K. V. Kleist, T. F. Möller, I. Rahwan, N. Pescetelli

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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, algorithmichuman 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 .2022 The Author(s) Published by the Royal Society. All rights reserved.

Original languageEnglish (US)
Article number20200426
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2227
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • General Mathematics
  • General Engineering
  • General Physics and Astronomy


  • cultural evolution
  • human machine collaboration
  • social learning
  • transmission chain


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