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Hybrid social learning in human-algorithm cultural transmission
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 algo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126184/ https://www.ncbi.nlm.nih.gov/pubmed/35599570 http://dx.doi.org/10.1098/rsta.2020.0426 |
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author | Brinkmann, L. Gezerli, D. Kleist, K. V. Müller, T. F. Rahwan, I. Pescetelli, N. |
author_facet | Brinkmann, L. Gezerli, D. Kleist, K. V. Müller, T. F. Rahwan, I. Pescetelli, N. |
author_sort | Brinkmann, L. |
collection | PubMed |
description | 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’. |
format | Online Article Text |
id | pubmed-9126184 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-91261842022-05-27 Hybrid social learning in human-algorithm cultural transmission Brinkmann, L. Gezerli, D. Kleist, K. V. Müller, T. F. Rahwan, I. Pescetelli, N. Philos Trans A Math Phys Eng Sci Articles 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’. The Royal Society 2022-07-11 2022-05-23 /pmc/articles/PMC9126184/ /pubmed/35599570 http://dx.doi.org/10.1098/rsta.2020.0426 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Articles Brinkmann, L. Gezerli, D. Kleist, K. V. Müller, T. F. Rahwan, I. Pescetelli, N. Hybrid social learning in human-algorithm cultural transmission |
title | Hybrid social learning in human-algorithm cultural transmission |
title_full | Hybrid social learning in human-algorithm cultural transmission |
title_fullStr | Hybrid social learning in human-algorithm cultural transmission |
title_full_unstemmed | Hybrid social learning in human-algorithm cultural transmission |
title_short | Hybrid social learning in human-algorithm cultural transmission |
title_sort | hybrid social learning in human-algorithm cultural transmission |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126184/ https://www.ncbi.nlm.nih.gov/pubmed/35599570 http://dx.doi.org/10.1098/rsta.2020.0426 |
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