PT  - JOURNAL ARTICLE
AU  - Pantelis P. Analytis
AU  - Daniel Barkoczi
AU  - Stefan M. Herzog
TI  - Social learning strategies for matters of taste
AID  - 10.1101/170191
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 170191
4099  - http://biorxiv.org/content/early/2017/07/30/170191.short
4100  - http://biorxiv.org/content/early/2017/07/30/170191.full
AB  - 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 judgment 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. We found that experienced individuals can benefit from relying mostly on the opinions of seemingly similar people. Inexperienced individuals, in contrast, 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 (i) how mainstream (or alternative) an individual’s tastes are and (ii) how much the similarity of the individual’s taste to that of the other people in the population differs across those people.