Predicting what we will like: Asking a stranger can be as good as asking a friend

Eggleston, Casey; Wilson, Timothy D.; Lee, Minha; Gilbert, Daniel T.

doi:10.1016/j.obhdp.2015.03.005

Cited by 21 publications

(32 citation statements)

References 20 publications

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“…Again, our empirical analyses confirmed these predictions. This explains why we were able to observe the wisdom of crowds in symmetric decision settings, going beyond the asymmetric settings studied in the past (e.g., Eggleston et al, 2015;Gilbert et al, 2009;Yaniv et al, 2011).…”

Section: Discussionmentioning

confidence: 70%

“…The cardinality assumption is central to our work and to other efforts to understand judgments in matters of taste. Similar assumptions are made in research programs exploring utility predictions and process interpretations of utility theory (e.g., Eggleston et al, 2015, Gilbert & Wilson, 2007Kahneman & Snell, 1990Kahneman, Wakker, & Sarin, 1997;Loewenstein & Schkade, 1999) and subjective wellbeing measured as happiness or life satisfaction (e.g., Deaton, 2008;Diener & Diener, 1996, Stevenson & Wolfers, 2008.…”

Section: Modeling Assumptionsmentioning

confidence: 92%

“…Second, we report two laboratory studies that tested and confirmed the model's predictions. Third, the existing research on advice-taking and aggregation in matters of taste has focused exclusively on "asymmetric" decision settings in which the decision maker has less information about the stimuli than the individuals in the crowd (who usually have full information in the form of first-hand experience; Eggleston, Wilson, Lee, & Gilbert, 2015;Gilbert, Killingsworth, Eyre, & Wilson, 2009;Yaniv, Choshen-Hillel, & Milyavsky, 2011). Our approach is more general: We show that in predicting tastes (as in predicting facts), crowds can also be wise in "symmetric" settings in which the decision maker and the crowd must rely on equally limited information about a stimulus.…”

Section: Introductionmentioning

confidence: 99%

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The Wisdom of Crowds in Matters of Taste

et al. 2018

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This research was supported by grants from the Israeli Science Foundation (327/10) and the Chicago Wisdom Project (John Templeton Foundation, Subaward FP050019-B). We would like to thank Robin Hogarth and Ed Vul, who both had an important influence on this work. We are also grateful to Shlomi Sher, Mike Yeomans and two anonymous reviewers for their comments, and to Naomi Goldblum for her editorial help.

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Section: Discussionmentioning

confidence: 70%

Section: Modeling Assumptionsmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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The Wisdom of Crowds in Matters of Taste

et al. 2018

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“…But we do see evidence that human recommenders were trying to be accurate. For instance, prior research (Eggleston, Wilson, Lee, & Gilbert, 2015;Gilbert, Killingsworth, Eyre, & Wilson, 2009;Hoch, 1987) suggests that people can make accurate recommendations simply by acting as "surrogates"…”

Section: Study 1a Resultsmentioning

confidence: 99%

“…But we do see evidence that human recommenders were trying to be accurate. For instance, prior research (Eggleston, Wilson, Lee, & Gilbert, ; Gilbert, Killingsworth, Eyre, & Wilson, ; Hoch, ) suggests that people can make accurate recommendations simply by acting as “surrogates” for the target (i.e., the person receiving the recommendation). In this case, surrogation would mean that recommenders predict that the target would give a joke the same rating as themselves.…”

Section: Studymentioning

confidence: 99%

Making sense of recommendations

Yeomans¹,

Shah

Mullainathan

et al. 2019

Behavioral Decision Making

273

130

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Computer algorithms are increasingly being used to predict people's preferences and make recommendations. Although people frequently encounter these algorithms because they are cheap to scale, we do not know how they compare to human judgment. Here, we compare computer recommender systems to human recommenders in a domain that affords humans many advantages: predicting which jokes people will find funny. We find that recommender systems outperform humans, whether strangers, friends, or family. Yet people are averse to relying on these recommender systems. This aversion partly stems from the fact that people believe the human recommendation process is easier to understand. It is not enough for recommender systems to be accurate, they must also be understood.

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Some Hedonic Consequences of Perspective‐Taking in Recommending

Yeomans

2018

J Consum Psychol

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What do people enjoy about making recommendations? Although recommendation recipients can gain useful information, the value of these exchanges for the information provider is less clear in comparison. In this article we test whether a common recommendation heuristic—egocentric projection—also has hedonic consequences, by conducting experiments that compare recommendations (suggestions for another person) to reviews, in which people merely express their own preferences. Over five studies, people preferred reviewing over recommending. Recommenders enjoyed themselves less when they had to take their recipients' perspective, to the extent that the recipients' tastes were different from their own. These results suggest that self‐expression can be intrinsically rewarding for recommendation makers, and that recommendation seekers can elicit more information by asking for reviews instead.

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Predicting what we will like: Asking a stranger can be as good as asking a friend

Cited by 21 publications

References 20 publications

The Wisdom of Crowds in Matters of Taste

The Wisdom of Crowds in Matters of Taste

Making sense of recommendations

Some Hedonic Consequences of Perspective‐Taking in Recommending

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