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Social learning strategies for matters of taste

Author

Listed:
  • Pantelis P. Analytis

    (Cornell University)

  • Daniel Barkoczi

    (Linköping University)

  • Stefan M. Herzog

    (Max Planck Institute for Human Development)

Abstract

Most choices people make are about ‘matters of taste’, on which there is no universal, objective truth. Nevertheless, people can learn from the experiences of individuals with similar tastes who have already evaluated the available options—a potential harnessed by recommender systems. We mapped recommender system algorithms to models of human judgement and decision-making about ‘matters of fact’ and recast the latter as social learning strategies for matters of taste. Using computer simulations on a large-scale, empirical dataset, we studied how people could leverage the experiences of others to make better decisions. Our simulations showed that experienced individuals can benefit from relying mostly on the opinions of seemingly similar people; by contrast, inexperienced individuals cannot reliably estimate similarity and are better off picking the mainstream option despite differences in taste. Crucially, the level of experience beyond which people should switch to similarity-heavy strategies varies substantially across individuals and depends on how mainstream (or alternative) an individual’s tastes are and the level of dispersion in taste similarity with the other people in the group.

Suggested Citation

  • Pantelis P. Analytis & Daniel Barkoczi & Stefan M. Herzog, 2018. "Social learning strategies for matters of taste," Nature Human Behaviour, Nature, vol. 2(6), pages 415-424, June.
    • Handle: RePEc:nat:nathum:v:2:y:2018:i:6:d:10.1038_s41562-018-0343-2
    DOI: 10.1038/s41562-018-0343-2
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    Cited by:

    1. Bertrand Jayles & Clément Sire & Ralf H J M Kurvers, 2021. "Crowd control: Reducing individual estimation bias by sharing biased social information," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-28, November.
    2. Niccolo Pescetelli, 2021. "A Brief Taxonomy of Hybrid Intelligence," Forecasting, MDPI, vol. 3(3), pages 1-11, September.
    3. Ni, Xuelian & Xiong, Fei & Pan, Shirui & Chen, Hongshu & Wu, Jia & Wang, Liang, 2023. "How heterogeneous social influence acts on human decision-making in online social networks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    4. Pieter Berg & TuongVan Vu & Lucas Molleman, 2024. "Unpredictable benefits of social information can lead to the evolution of individual differences in social learning," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    5. Lisheng He & Pantelis P. Analytis & Sudeep Bhatia, 2022. "The Wisdom of Model Crowds," Management Science, INFORMS, vol. 68(5), pages 3635-3659, May.
    6. Onishi Hiroshi, 2018. "Consumers’ Social Learning About Videogame Consoles Through Multiple Website Browsing," Journal of Systems Science and Information, De Gruyter, vol. 6(6), pages 495-511, December.
    7. Pablo Bello & David Garcia, 2021. "Cultural Divergence in popular music: the increasing diversity of music consumption on Spotify across countries," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-8, December.

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