{"id":11352,"date":"2018-11-20T09:20:07","date_gmt":"2018-11-20T17:20:07","guid":{"rendered":"https:\/\/mediax.stanford.edu\/?page_id=11352"},"modified":"2018-11-28T10:00:22","modified_gmt":"2018-11-28T18:00:22","slug":"sow-bernstein","status":"publish","type":"page","link":"https:\/\/mediax.stanford.edu\/research-projects\/sow-bernstein\/","title":{"rendered":"Dream Team: Computational Techniques for Adaptive Teams"},"content":{"rendered":"<p><em>From The Theme<\/em><br \/>\n<a href=\"https:\/\/mediax.stanford.edu\/research\/smart-office-workflows\/\"><strong>SMART OFFICE WORKFLOWS<\/strong><\/a><\/p>\n<p><strong>WHAT IF<\/strong><br \/>\nWhat if we could leverage computation and machine learning to optimize team structure?<\/p>\n<p><a href=\"https:\/\/mediax.stanford.edu\/research-projects\/dream-team-computational-techniques-for-adaptive-teams\/attachment\/dream_team_main\/\" rel=\"attachment wp-att-11411\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-11411\" src=\"https:\/\/mediax.stanford.edu\/wp-content\/uploads\/2018\/11\/Dream_Team_Main.jpg\" alt=\"Dream Team Image\" width=\"995\" height=\"421\" srcset=\"https:\/\/mediax.stanford.edu\/wp-content\/uploads\/2018\/11\/Dream_Team_Main.jpg 995w, https:\/\/mediax.stanford.edu\/wp-content\/uploads\/2018\/11\/Dream_Team_Main-350x148.jpg 350w, https:\/\/mediax.stanford.edu\/wp-content\/uploads\/2018\/11\/Dream_Team_Main-768x325.jpg 768w, https:\/\/mediax.stanford.edu\/wp-content\/uploads\/2018\/11\/Dream_Team_Main-400x169.jpg 400w, https:\/\/mediax.stanford.edu\/wp-content\/uploads\/2018\/11\/Dream_Team_Main-800x338.jpg 800w\" sizes=\"auto, (max-width: 995px) 100vw, 995px\" \/><\/a><\/p>\n<p><strong>WHAT WE SET OUT TO DO<\/strong><br \/>\nWe set out to explore a new computationally empowered management practice for teams. We developed a system called \u201cDreamTeam\u201d that experiments with a set of possible team structures and organizational strategies to identify the most effective structures for each specific team.<\/p>\n<p>The system iterates on organizational designs for teams, adapting based on performance feedback, and leveraging computation to continuously propose alternatives and evaluate outcomes. For this project, 135 workers, in teams of 3, engaged in 10 rounds of a set task, each with a time limit of 15 minutes.<\/p>\n<p><strong>WHAT WE FOUND<\/strong><br \/>\nThe DreamTeam teams had substantially different team structures from one another. Despite their differences, the DreamTeam teams signi\ufb01cantly outperformed all other conditions, by 38%\u201346% on average per round. This research advances computation as a powerful partner in establishing effective teamwork.<\/p>\n<p><strong>LEARN MORE<\/strong><br \/>\n<a href=\"http:\/\/hci.stanford.edu\/publications\/2018\/dreamteam\/zhou_sharon_dreamteam.pdf\">In Search of the Dream Team: Temporally Constrained Multi-Armed Bandits for Identifying Effective Team Structures<\/a>, Sharon Zhou, Melissa Valentine, Michael S. Bernstein, CHI 2018<\/p>\n<p><strong>PEOPLE BEHIND THE PROJECT<\/strong><br \/>\n<strong> <a href=\"https:\/\/mediax.stanford.edu\/research-projects\/dream-team-computational-techniques-for-adaptive-teams\/attachment\/michael_bernstein_small\/\" rel=\"attachment wp-att-11354\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-full wp-image-11354\" src=\"https:\/\/mediax.stanford.edu\/wp-content\/uploads\/2018\/11\/Michael_Bernstein_Small.jpg\" alt=\"Michael Bernstein\" width=\"82\" height=\"100\" \/><\/a><a href=\"https:\/\/hci.stanford.edu\/msb\/\">Michael Bernstein<\/a><\/strong> is an Assistant Professor of Computer Science at Stanford University. His research focuses on the design of crowdsourcing and social computing systems. This work has received five Best Paper awards and eleven honorable mentions at premier venues in human-computer interaction and social computing. Michael has been recognized as a Robert N. Noyce Family Faculty Scholar, and awarded the George M. Sprowls award, NSF CAREER Award, and Sloan Fellowship.<\/p>\n<p><strong><a href=\"https:\/\/mediax.stanford.edu\/research-projects\/dream-team-computational-techniques-for-adaptive-teams\/attachment\/sharon_zhou_small\/\" rel=\"attachment wp-att-11355\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright size-full wp-image-11355\" src=\"https:\/\/mediax.stanford.edu\/wp-content\/uploads\/2018\/11\/Sharon_Zhou_Small.jpg\" alt=\"Sharon Zhou\" width=\"82\" height=\"100\" \/><\/a><a href=\"http:\/\/sharonzhou.me\/\">Sharon Zhou<\/a><\/strong> received her PhD in Computer Science from Stanford University in 2017. Dr. Zhou is the 1st Harvard graduate to have majored in Computer Science and the Classics. She worked as a Research Assistant for the Human Computer Interaction Group, led by Dr. Michael Bernstein, from April 2016 \u2013 September 2017.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>From The Theme SMART OFFICE WORKFLOWS WHAT IF What if we could leverage computation and machine learning to optimize team structure? WHAT WE SET OUT TO DO We set out to explore a new computationally empowered management practice for teams. We developed a system called \u201cDreamTeam\u201d that experiments with a set of possible team structures [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":363,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"tags":[75,23,22,16,11,117],"class_list":["post-11352","page","type-page","status-publish","hentry","tag-algorithms","tag-computation","tag-feedback","tag-hmi","tag-machine-learning","tag-smart-workplace"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Dream Team: Computational Techniques for Adaptive Teams - mediaX at Stanford University<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mediax.stanford.edu\/research-projects\/sow-bernstein\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Dream Team: Computational Techniques for Adaptive Teams - mediaX at Stanford University\" \/>\n<meta property=\"og:description\" content=\"From The Theme SMART OFFICE WORKFLOWS WHAT IF What if we could leverage computation and machine learning to optimize team structure? 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