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Title: Investigating the "wisdom of crowds" at scale
Authors: Mysore, A.S.
Yaligar, V.S.
Ibarra, I.A.
Simoiu, C.
Goel, S.
Arvind, R.
Sumanth, C.
Srikantan, A.
Bhargav, H.S.
Pahadia, M.
Dobhal, T.
Ahmed, A.
Shankar, M.
Agarwal, H.
Agarwal, R.
Anirudh-Kondaveeti, S.
Arun-Gokhale, S.
Attri, A.
Chandra, A.
Chilukuri, Y.
Dharmaji, S.
Garg, D.
Gupta, N.
Gupta, P.
Jacob, G.M.
Jain, S.
Joshi, S.
Khajuria, T.
Khillan, S.
Konam, S.
Kumar-Kolla, P.
Loomba, S.
Madan, R.
Maharaja, A.
Mathur, V.
Munshi, B.
Nawazish, M.
Neehar-Kurukunda, V.
Nirmal-Gavarraju, V.
Parashar, S.
Parikh, H.
Paritala, A.
Patil, A.
Phatak, R.
Pradhan, M.
Ravichander, A.
Sangeeth, K.
Sankaranarayanan, S.
Sehgal, V.
Sheshan, A.
Shibiraj, S.
Singh, A.
Singh, A.
Sinha, P.
Soni, P.
Thomas, B.
Tuteja, L.
Varma-Dattada, K.
Venkataraman, S.
Verma, P.
Yelurwar, I.
Issue Date: 2015
Citation: UIST 2015 - Adjunct Publication of the 28th Annual ACM Symposium on User Interface Software and Technology, 2015, Vol., , pp.75-76
Abstract: In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon's Mechanical Turk.
Appears in Collections:2. Conference Papers

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