Social learning strategies for matters of taste

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.

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Fig. 1: Performance of the social learning strategies as a function of experience.
Fig. 2: The performance of strategies depends on how people’s tastes correlate with those of others in the population.
Fig. 3: The best-performing strategy depends on how people’s tastes correlate with those of others in the population.

Change history

  • 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.

References

  1. 1.

    Resnick, P. & Varian, H. R. Recommender systems. Commun. ACM 40, 56–58 (1997).

    Article  Google Scholar 

  2. 2.

    Adomavicius, G. & Tuzhilin, A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005).

    Article  Google Scholar 

  3. 3.

    Herlocker, J. L., Konstan, J. A., Borchers, A. & Riedl, J. An algorithmic framework for performing collaborative filtering. In Proc. 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 230–237 (ACM, 1999).

  4. 4.

    Brock, T. C. Communicator–recipient similarity and decision change. J. Pers. Soc. Psychol. 1, 650–654 (1965).

    Article  PubMed  CAS  Google Scholar 

  5. 5.

    Simons, H. W., Berkowitz, N. N. & Moyer, R. J. Similarity, credibility, and attitude change: a review and a theory. Psychol. Bull. 73, 1–16 (1970).

    Article  Google Scholar 

  6. 6.

    Cohen, J. B. & Golden, E. Informational social influence and product evaluation. J. Appl. Psychol. 56, 54–59 (1972).

    Article  Google Scholar 

  7. 7.

    Burnkrant, R. E. & Cousineau, A. Informational and normative social influence in buyer behavior. J. Consum. Res. 2, 206–215 (1975).

    Article  Google Scholar 

  8. 8.

    Jussim, L. & Osgood, D. W. Influence and similarity among friends: an integrative model applied to incarcerated adolescents. Soc. Psychol. Q. 52, 98–112 (1989).

    Article  Google Scholar 

  9. 9.

    Fawcett, C. A. & Markson, L. Children reason about shared preferences. Dev. Psychol. 46, 299–309 (2010).

    Article  PubMed  Google Scholar 

  10. 10.

    Eggleston, C. M., Wilson, T. D., Lee, M. & Gilbert, D. T. Predicting what we will like: asking a stranger can be as good as asking a friend. Organ. Behav. Hum. Decis. Process. 128, 1–10 (2015).

    Article  Google Scholar 

  11. 11.

    Yaniv, I., Choshen-Hillel, S. & Milyavsky, M. Receiving advice on matters of taste: similarity, majority influence, and taste discrimination. Organ. Behav. Hum. Decis. Process. 115, 111–120 (2011).

    Article  Google Scholar 

  12. 12.

    Müller-Trede, J., Choshen-Hillel, S., Barneron, M. & Yaniv, I. The wisdom of crowds in matters of taste. Manag. Sci. 64, 1779–1803 (2017).

    Article  Google Scholar 

  13. 13.

    Gershman, S., Hillard, T. P. & Gweon, H. Learning the structure of social influence. Cogn. Sci. 41, 545–575 (2017).

    Article  PubMed  Google Scholar 

  14. 14.

    Clemen, R. T. Combining forecasts: a review and annotated bibliography. Int. J. Forecast. 5, 559–583 (1989).

    Article  Google Scholar 

  15. 15.

    Hastie, R. & Kameda, T. The robust beauty of majority rules in group decisions. Psychol. Rev. 112, 494–508 (2005).

    Article  PubMed  Google Scholar 

  16. 16.

    Goldstone, R. L., Wisdom, T. N., Roberts, M. E. & Frey, S. Learning along with others. Psychol. Learn. Motiv. 58, 1–45 (2013).

    Article  Google Scholar 

  17. 17.

    Rendell, L. et al. Cognitive culture: theoretical and empirical insights into social learning strategies. Trends Cogn. Sci. 15, 68–76 (2011).

    Article  PubMed  Google Scholar 

  18. 18.

    Larrick, R. P., Mannes, A. E. & Soll, J. B. in Frontiers in Social Psychology: Social Judgment and Decision Making (ed. Krueger, J. I.) 227–242 (Psychology Press, New York, NY, 2012).

  19. 19.

    Ellison, G. & Fudenberg, D. Rules of thumb for social learning. J. Polit. Econ. 101, 612–643 (1993).

    Article  Google Scholar 

  20. 20.

    Laland, K. N. Social learning strategies. Anim. Learn. Behav. 32, 4–14 (2004).

    Article  Google Scholar 

  21. 21.

    Boyd, R. & Richerson, P. J. Culture and the Evolutionary Process (Univ. Chicago Press, Chicago, IL, 1985).

  22. 22.

    Nosofsky, R. M. Similarity scaling and cognitive process models. Annu. Rev. Psychol. 43, 25–53 (1992).

    Article  Google Scholar 

  23. 23.

    Goldstone, R. L. & Son, J. Y. Similarity (Cambridge Univ. Press, Cambridge, 2005).

  24. 24.

    Desrosiers, C. & Karypis, G. in Recommender Systems Handbook (eds Ricci, F. et al.) 107–144 (Springer, New York, NY, 2011).

  25. 25.

    Mannes, A. E., Soll, J. B. & Larrick, R. P. The wisdom of select crowds. J. Pers. Soc. Psychol. 107, 276–299 (2014).

    Article  PubMed  Google Scholar 

  26. 26.

    Winkler, R. L. & Makridakis, S. The combination of forecasts. J. R. Stat. Soc. Ser. A 146, 150–157 (1983).

    Article  Google Scholar 

  27. 27.

    Gigerenzer, G. & Todd, P. M. Simple Heuristics that Make Us Smart (Oxford Univ. Press, New York, NY, 1999).

    Google Scholar 

  28. 28.

    Davis-Stober, C. P., Dana, J. & Budescu, D. V. Why recognition is rational: optimality results on single-variable decision rules. Judgm. Decis. Mak. 5, 216–229 (2010).

    Google Scholar 

  29. 29.

    Şimşek, Ö. & Buckmann, M. Learning from small samples: an analysis of simple decision heuristics. In Advances in Neural Information Processing Systems 28 (NIPS 2015) (eds Cortes, C. et al.) 3141–3149 (Curran Associates, 2015).

  30. 30.

    Hogarth, R. M. & Karelaia, N. Ignoring information in binary choice with continuous variables: when is less “more”? J. Math. Psychol. 49, 115–124 (2005).

    Article  Google Scholar 

  31. 31.

    Gigerenzer, G. & Brighton, H. Homo heuristicus: why biased minds make better inferences. Top. Cogn. Sci. 1, 107–143 (2009).

    Article  PubMed  Google Scholar 

  32. 32.

    Hastie, T., Tibshirani, R. J. & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd edn.) (Springer, New York, NY, 2009).

  33. 33.

    Şimşek, Ö. Linear decision rule as aspiration for simple decision heuristics. In Advances in Neural Information Processing Systems 26 (NIPS 2013) (eds Burges, C. J. C. et al.) 2904–2912 (Curran Associates, 2013).

  34. 34.

    Einhorn, H. J., Hogarth, R. M. & Klempner, E. Quality of group judgment. Psychol. Bull. 84, 158–172 (1977).

    Article  Google Scholar 

  35. 35.

    Davis-Stober, C. P., Budescu, D. V., Dana, J. & Broomell, S. B. When is a crowd wise? Decision 1, 79–101 (2014).

    Article  Google Scholar 

  36. 36.

    Goldstein, D. G., McAfee, R. P. & Suri, S. The wisdom of smaller, smarter crowds. In Proc. 15th ACM Conference on Economics and Computation 471–488 (ACM, 2014).

  37. 37.

    Mellers, B. et al. Psychological strategies for winning a geopolitical forecasting tournament. Psychol. Sci. 25, 1106–1115 (2014).

    Article  PubMed  Google Scholar 

  38. 38.

    Ekstrand, M. D., Riedl, J. T. & Konstan, J. A. Collaborative filtering recommender systems. Found. Trends Hum. Comput. Interact. 4, 81–173 (2011).

    Article  Google Scholar 

  39. 39.

    Dawes, R. M. The robust beauty of improper linear models in decision making. Am. Psychol. 34, 571–582 (1979).

    Article  Google Scholar 

  40. 40.

    Dana, J. & Dawes, R. M. The superiority of simple alternatives to regression for social science predictions. J. Educ. Behav. Stat. 29, 317–331 (2004).

    Article  Google Scholar 

  41. 41.

    Einhorn, H. J. & Hogarth, R. M. Unit weighting schemes for decision making. Organ. Behav. Hum. Perform. 13, 171–192 (1975).

    Article  Google Scholar 

  42. 42.

    Katsikopoulos, K. V. Psychological heuristics for making inferences: definition, performance, and the emerging theory and practice. Decis. Anal. 8, 10–29 (2011).

    Article  Google Scholar 

  43. 43.

    Goldberg, K., Roeder, T., Gupta, D. & Perkins, C. Eigentaste: a constant time collaborative filtering algorithm. Inf. Retr. 4, 133–151 (2001).

    Article  Google Scholar 

  44. 44.

    Goldstone, R. L. & Lupyan, G. Discovering psychological principles by mining naturally occurring data sets. Top. Cogn. Sci. 8, 548–568 (2016).

    Article  PubMed  Google Scholar 

  45. 45.

    Paxton, A. & Griffiths, T. L. Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets. Behav. Res. Methods 49, 1630–1638 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Dunbar, R. How Many Friends Does One Person Need? Dunbar’s Number and Other Evolutionary Quirks (Faber & Faber, London, 2010).

  47. 47.

    Hume, D. Of the standard of taste. In Four Dissertations (A. Millar in the Strand, London, 1757).

  48. 48.

    Geman, S., Bienenstock, E. & Doursat, R. Neural networks and the bias/variance dilemma. Neural Comput. 4, 1–58 (1992).

    Article  Google Scholar 

  49. 49.

    Briscoe, E. & Feldman, J. Conceptual complexity and the bias/variance tradeoff. Cognition 118, 2–16 (2011).

    Article  PubMed  Google Scholar 

  50. 50.

    Geurts, P. in Data Mining and Knowledge Discovery Handbook (eds Maimon, O. & Rokach, L.) 733–746 (Springer, New York, NY, 2010).

  51. 51.

    Arlot, S. & Celisse, A. A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010).

    Article  Google Scholar 

  52. 52.

    Vapnik, V. N. The Nature of Statistical Learning Theory (Springer, New York, NY, 1995).

  53. 53.

    Akaike, H. in Selected Papers of Hirotugu Akaike (eds Parzen, E., Tanabe, K. & Kitagawa, G.) 199–213 (Springer, New York, NY, 1998).

  54. 54.

    Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978).

    Article  Google Scholar 

  55. 55.

    Rissanen, J. Modeling by shortest data description. Automatica 14, 465–471 (1978).

    Article  Google Scholar 

  56. 56.

    Pitt, M. A. & Myung, I. J. When a good fit can be bad. Trends Cogn. Sci. 6, 421–425 (2002).

    Article  PubMed  Google Scholar 

  57. 57.

    Bobadilla, J., Ortega, F., Hernando, A. & Gutiérrez, A. Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013).

    Article  Google Scholar 

  58. 58.

    Pazzani, M. & Billsus, D. in The Adaptive Web (eds Brusilovsky, P., Kobsa, A. & Nejdl, W.) 325–341 (Springer, Berlin, 2007).

  59. 59.

    Nunes, M. A. S. & Hu, R. Personality-based recommender systems: an overview. In Proc. 6th ACM Conference on Recommender Systems 5–6 (ACM, 2012).

  60. 60.

    Fernández-Tobas, I., Braunhofer, M., Elahi, M., Ricci, F. & Cantador, I. Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model. User Adapt. Interact. 26, 221–255 (2016).

    Article  Google Scholar 

  61. 61.

    Gino, F., Shang, J. & Croson, R. The impact of information from similar or different advisors on judgment. Organ. Behav. Hum. Decis. Process. 108, 287–302 (2009).

    Article  Google Scholar 

  62. 62.

    West, P. M. Predicting preferences: an examination of agent learning. J. Consum. Res. 23, 68–80 (1996).

    Article  Google Scholar 

  63. 63.

    Breiman, L. Arcing classifier (with discussion and a rejoinder by the author). Ann. Stat. 26, 801–849 1998).

    Article  Google Scholar 

  64. 64.

    Burke, R. Hybrid recommender systems: survey and experiments. User Model. User Adapt. Interact. 12, 331–370 (2002).

    Article  Google Scholar 

  65. 65.

    Herzog, S. M. & Hertwig, R. The wisdom of many in one mind: improving individual judgments with dialectical bootstrapping. Psychol. Sci. 20, 231–237 (2009).

    Article  PubMed  Google Scholar 

  66. 66.

    Herzog, S. M. & von Helversen, B. Strategy selection versus strategy blending: a predictive perspective on single- and multi-strategy accounts in multiple-cue estimation. J. Behav. Decis. Mak. 31, 233–249 (2018).

    Article  Google Scholar 

  67. 67.

    Herzog, S. M. & Hertwig, R. Harnessing the wisdom of the inner crowd. Trends Cogn. Sci. 18, 504–506 (2014).

    Article  PubMed  Google Scholar 

  68. 68.

    Bell, R. M. & Koren, Y. Lessons from the Netflix prize challenge. SIGKDD Explor. 9, 75–79 (2007).

    Article  Google Scholar 

  69. 69.

    Repacholi, B. M. & Gopnik, A. Early reasoning about desires: evidence from 14- and 18-month-olds. Dev. Psychol. 33, 12–21 (1997).

    Article  PubMed  CAS  Google Scholar 

  70. 70.

    Mata, J., Scheibehenne, B. & Todd, P. M. Predicting children’s meal preferences: how much do parents know? Appetite 50, 367–375 (2008).

    Article  PubMed  Google Scholar 

  71. 71.

    Davis, H. L., Hoch, S. J. & Ragsdale, E. E. An anchoring and adjustment model of spousal predictions. J. Consum. Res. 13, 25–37 (1986).

    Article  Google Scholar 

  72. 72.

    Sharma, A. & Cosley, D. Studying and modeling the connection between people’s preferences and content sharing. In Proc. 18th ACM Conference on Computer Supported Cooperative Work & Social Computing 1246–1257 (ACM, 2015).

  73. 73.

    Sinha, R. R. & Swearingen, K. Comparing recommendations made by online systems and friends. In Proc. DELOS-NSF Workshop on Personalisation and Recommender Systems in Digital Libraries (ACM, 2001).

  74. 74.

    Krishnan, V., Narayanashetty, P. K., Nathan, M., Davies, R. T. & Konstan, J. A. Who predicts better?—Results from an online study comparing humans and an online recommender system. In Proc. 2008 ACM Conference on Recommender Systems 211–218 (ACM, 2008).

  75. 75.

    Yeomans, M., Shah, A. K., Mullainathan, S. & Kleinberg, J. Making sense of recommendations. Preprint at http://scholar.harvard.edu/files/sendhil/files/recommenders55_01.pdf (2016).

  76. 76.

    Thurstone, L. L. A law of comparative judgment. Psychol. Rev. 34, 273–286 (1927).

    Article  Google Scholar 

  77. 77.

    Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958).

    Article  PubMed  CAS  Google Scholar 

  78. 78.

    Gigerenzer, G. From tools to theories: a heuristic of discovery in cognitive psychology. Psychol. Rev. 98, 254–267 (1991).

    Article  Google Scholar 

  79. 79.

    Analytis, P. P., Kothiyal, A. & Katsikopoulos, K. Multi-attribute utility models as cognitive search engines. Judgm. Decis. Mak. 9, 403–419 (2014).

    Google Scholar 

  80. 80.

    Gordon, K. Group judgments in the field of lifted weights. J. Exp. Psychol. 7, 398–400 (1924).

    Article  Google Scholar 

  81. 81.

    Hogarth, R. M. A note on aggregating opinions. Organ. Behav. Hum. Perform. 21, 40–46 (1978).

    Article  Google Scholar 

  82. 82.

    Treynor, J. L. Market efficiency and the bean jar experiment. Financ. Anal. J. 43, 50–53 (1987).

    Article  Google Scholar 

  83. 83.

    Bollen, D., Knijnenburg, B. P., Willemsen, M. C. & Graus, M. Understanding choice overload in recommender systems. In Proc. 4th ACM Conference on Recommender Systems 63–70 (ACM, 2010).

  84. 84.

    Ekstrand, M. D., Harper, F. M., Willemsen, M. C. & Konstan, J. A. User perception of differences in recommender algorithms. In Proc. 8th ACM Conference on Recommender Systems 161–168 (ACM, 2014).

  85. 85.

    Rich, E. User modeling via stereotypes. Cogn. Sci. 3, 329–354 (1979).

    Article  Google Scholar 

  86. 86.

    Sarwar, B., Karypis, G., Konstan, J. & Riedl, J. Application of Dimensionality Reduction in Recommender System—A Case Study Technical Report (Minnesota Univ. Department of Computer Science, 2000).

  87. 87.

    Schnabel, T., Swaminathan, A., Singh, A., Chandak, N. & Joachims, T. Recommendations as treatments: debiasing learning and evaluation. In ICML'16 Proc. 33rd International Conference on International Conference on Machine Learning (eds Balcan, M. F. & Weinberger, K. Q.) 1670–1679 (JMLR, 2016).

  88. 88.

    Herlocker, J. L., Konstan, J. A., Terveen, L. G. & Riedl, J. T. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 5–53 (2004).

    Article  Google Scholar 

  89. 89.

    Gigerenzer, G. & Goldstein, D. G. Reasoning the fast and frugal way: models of bounded rationality. Psychol. Rev. 103, 650–669 (1996).

    Article  PubMed  CAS  Google Scholar 

  90. 90.

    Richerson, P. J. & Boyd, R. Not by Genes Alone: How Culture Transformed Human Evolution (Univ. Chicago Press, Chicago, IL, 2008).

  91. 91.

    Yetton, P. W. & Bottger, P. C. Individual versus group problem solving: an empirical test of a best-member strategy. Organ. Behav. Hum. Perform. 29, 307–321 (1982).

    Article  Google Scholar 

  92. 92.

    Shardanand, U. & Maes, P. Social information filtering: algorithms for automating “word of mouth”. In Proc. SIGCHI Conference on Human Factors in Computing Systems (eds Katz, I. R. et al.) 210–217 (ACM/Addison-Wesley, 1995).

  93. 93.

    Hammond, K. R., Hursch, C. J. & Todd, F. J. Analyzing the components of clinical inference. Psychol. Rev. 71, 438–456 (1964).

    Article  PubMed  CAS  Google Scholar 

  94. 94.

    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. & Riedl, J. GroupLens: an open architecture for collaborative filtering of netnews. In Proc. 1994 ACM Conference on Computer Supported Cooperative Work 175–186 (ACM, 1994).

  95. 95.

    Kruschke, J. K. ALCOVE: an exemplar-based connectionist model of category learning. Psychol. Rev. 99, 22–44 (1992).

    Article  PubMed  CAS  Google Scholar 

  96. 96.

    Juslin, P. & Persson, M. PROBabilities from EXemplars (PROBEX): a “lazy” algorithm for probabilistic inference from generic knowledge. Cogn. Sci. 26, 563–607 (2002).

    Article  Google Scholar 

  97. 97.

    Nosofsky, R. M. Attention, similarity, and the identification–categorization relationship. J. Exp. Psychol. Gen. 115, 39–57 (1986).

    Article  PubMed  CAS  Google Scholar 

  98. 98.

    Altman, N. S. An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46, 175–185 (1992).

    Google Scholar 

  99. 99.

    Sarwar, B., Karypis, G., Konstan, J. & Riedl, J. Item-based collaborative filtering recommendation algorithms. In Proc. 10th International Conference on World Wide Web 285–295 (ACM, 2001).

  100. 100.

    Gilbert, D. T., Killingsworth, M. A., Eyre, R. N. & Wilson, T. D. The surprising power of neighborly advice. Science 323, 1617–1619 (2009).

    Article  PubMed  CAS  Google Scholar 

  101. 101.

    Cavalli-Sforza, L. L. & Feldman, M. W. Cultural Transmission and Evolution: a Quantitative Approach (Princeton Univ. Press, Princeton, NJ, 1981).

    Google Scholar 

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Acknowledgements

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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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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Correspondence to Pantelis P. Analytis.

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Analytis, P.P., Barkoczi, D. & Herzog, S.M. Social learning strategies for matters of taste. Nat Hum Behav 2, 415–424 (2018). https://doi.org/10.1038/s41562-018-0343-2

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