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Investigating the 'Wisdom of Crowds' at Scale

Authors :
Rajat Kumar Agarwal
Avinash Paritala
Sonali Parashar
Pulkit Verma
Himani Agarwal
Shashank Arun-Gokhale
Suprajha Shibiraj
Vibhor Sehgal
Sukanya Venkataraman
Atif Ahmed
Harsh Parikh
Sharad Goel
Ashrith Sheshan
Rahul Phatak
Chiraag Sumanth
Aditya Singh
Bhargav Hs
Mandar Pradhan
Shashank Joshi
Vidit Mathur
Krishna Sangeeth
Akshansh Maharaja
Aayush Attri
Mayank Pahadia
Deepak Garg
Ishan Yelurwar
Alok Mysore
Imanol Arrieta Ibarra
Sandeep Konam
Siddharth Jain
Sreecharan Sankaranarayanan
Arvind Srikantan
Mani Shankar
Sameeksha Khillan
Paras Gupta
Venkata Neehar-Kurukunda
Naman Gupta
Sahil Loomba
Pushkin Soni
Ramesh Arvind
Tarun Khajuria
Abhilasha Ravichander
Prashant Sinha
Glincy Mary Jacob
Bipin Thomas
Vikas S. Yaligar
Mohammed Nawazish
Sai Anirudh-Kondaveeti
Camelia Simoiu
Anjali Singh
Rachit Madan
Sharath Dharmaji
Arpita Chandra
Tushar Dobha
Venkat Nirmal-Gavarraju
Kasyap Varma-Dattada
Praveen Kumar-Kolla
Amit Patil
Bharat Munshi
Yogitha Chilukur
Source :
UIST (Adjunct Volume)
Publication Year :
2015
Publisher :
ACM, 2015.

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.

Details

Database :
OpenAIRE
Journal :
Adjunct Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology
Accession number :
edsair.doi...........c0d0332a78c00a33b0c94470889f92e1
Full Text :
https://doi.org/10.1145/2815585.2815725