Hybrid social learning in human-algorithm cultural transmission
Open accessAbstract
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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In the transmission between humans and machines, a misalignment of biases can increase the risk of information loss.
“Conversely, in the transmission between humans and machines, a misalignment of biases can increase the risk of information loss 107 .”
From Machine culture (2023) · cites this paper as [107]
Despite popular media claims about machine-generated art developing its own unique style, we do not yet have a reliable way of verifying these claims, let alone assessing machine-to-human cultural transmission.
“Despite popular media claims about machine-generated art developing its own unique style 177 , we do not yet have a reliable way of verifying these claims, let alone assessing machine-to-human cultural transmission 107 .”
From Machine culture (2023) · cites this paper as [107]
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- Authors
- Brinkmann, L., Gezerli, D., Kleist, K. V., Müller, T. F., Rahwan, I., & Pescetelli, N.
- Affiliations
- Max Planck Institute for Human Development (DE)
- New Jersey Institute of Technology (US)
- Corresponding author
- Brinkmann, L.
- Venue
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Publisher
- The Royal Society
- Published
- 2022-05-23
- Volume
- 380
- Issue
- 2227
- Pages
- 20200426-20200426
- ISSN
- 1364-503X (ISSN-L)
- 1471-2962
- PMC
- PMC9126184
- OpenAlex
- W4281388462
- Cited by
- 24
- References
- 76
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 6 · 2023: 7 · 2022: 4 · 2021: 1
- FWCI
- 26.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- SocArXiv (OSF Preprints) (submittedVersion)
- SocArXiv (OSF Preprints) (submittedVersion, public-domain)
- Topics
- Social Sciences › Cultural Studies › Language and cultural evolution
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Social Sciences › Sociology and Political Science › Evolutionary Game Theory and Cooperation
- Keywords
- cultural transmission, social learning, algorithms, human-algorithm interaction, transmission chains, algorithmic bias, problem-solving strategies, planning tasks, agent-based modeling, cultural evolution, emergence
- MeSH terms
- Social Learning, Algorithms, Cultural Evolution, Humans, Learning, Problem Solving
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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