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Social learning strategies for matters of taste

Nature Human Behaviourvolume 2pages415424 (2018) | Download Citation


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.

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  • 22 August 2018

    The version of the Supplementary Information file that was originally published with this Article was not the latest version provided by the authors. In the captions of Supplementary Figs. 2 and 8, the median standard error values were reported to be 0.0028 in both cases; instead, in both instances, the values should have been 0.0015. These have now been updated and the Supplementary Information file replaced.


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We thank D. Cosley, T. Joachims, J. Müller-Trede, T. Schnabel, D. Wolpert and C. M. Wu for their insightful comments. We are indebted to the members of the ABC and ARC research groups at the Max Planck Institute for Human Development and the participants in the Microsoft Artificial Intelligence seminar at Cornell University for their constructive feedback during presentations. We are grateful to A. Todd and S. Goss for editing the manuscript. This research was supported in part through NSF Award IIS-1513692. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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  1. Cornell University, Ithaca, NY, USA

    • Pantelis P. Analytis
  2. Linköping University, Linköping, Sweden

    • Daniel Barkoczi
  3. Max Planck Institute for Human Development, Berlin, Germany

    • Stefan M. Herzog


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P.P.A., D.B. and S.M.H. conceived the research, developed the simulation framework for the main results and the bias–variance decomposition, analysed the simulation results and wrote the paper.

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The authors declare no competing interests.

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Correspondence to Pantelis P. Analytis.

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