{"id":166548,"date":"2014-06-08T00:00:00","date_gmt":"2014-06-08T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/the-wisdom-of-smaller-smarter-crowds\/"},"modified":"2018-10-16T20:21:22","modified_gmt":"2018-10-17T03:21:22","slug":"the-wisdom-of-smaller-smarter-crowds","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/the-wisdom-of-smaller-smarter-crowds\/","title":{"rendered":"The Wisdom of Smaller, Smarter Crowds"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The \u201cwisdom of crowds\u201d refers to the phenomenon that aggregated predictions from a large group of people can rival or even beat the accuracy of experts. In domains with substantial stochastic elements, such as stock picking, crowd strategies (e.g. indexing) are difficult to beat. However, in domains in which some crowd members have demonstrably more skill than others, smart sub-crowds could possibly outperform the whole.  The central question this work addresses is whether such smart subsets of a crowd can be identified a priori in a large-scale prediction contest that has substantial skill and luck components. We study this question with data obtained from fantasy soccer, a game in which millions of people choose professional players from the English Premier League to be on their fantasy soccer teams. The better the professional players do in real life games, the more points fantasy teams earn. Fantasy soccer is ideally suited to this investigation because it comprises millions of individual-level, within-subject predictions, past performance indicators, and the ability to test the effectiveness of arbitrary player-selection strategies. We find that smaller, smarter crowds can be identified in advance and that they beat the wisdom of the larger crowd. We also show that many players would do better by simply imitating the strategy of a player who has done well in the past. Finally, we provide a theoretical model that explains the results we see from our empirical analyses.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The \u201cwisdom of crowds\u201d refers to the phenomenon that aggregated predictions from a large group of people can rival or even beat the accuracy of experts. In domains with substantial stochastic elements, such as stock picking, crowd strategies (e.g. indexing) are difficult to beat. However, in domains in which some crowd members have demonstrably more [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13548,13559],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-166548","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-economics","msr-research-area-social-sciences","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"Proceedings of the 15th ACM Conference on Economics and Computation","msr_affiliation":"","msr_published_date":"2014-06-08","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"204833","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"smart_crowds.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/smart_crowds.pdf","id":204833,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":204833,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/smart_crowds.pdf"}],"msr-author-ordering":[{"type":"text","value":"Daniel G. 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