Title :
Exploration in Massively Collaborative Problem Solving
Author :
Greene, Kshanti ; Thomsen, Dan ; Michelucci, Pietro
Author_Institution :
MSI, Inc., Albuquerque, NM, USA
Abstract :
We present key insights from two independent projects attempting to foster massive collaboration to solve complex problems. The teams designed frameworks for Massively Collaborative Problem Solving(MCPS) that encourage deep reasoning to emerge by combining small contributions from many distributed individuals. Instead of a linear approach to problem solving, in which many people are asked to perform the same task, the frameworks encourage problem solvers to decompose a complex problem into parts solved by people with diverse skills and experiences. Social consensus then plays a role in crafting the aggregate solution. Relevant issues such as motivating problem solvers and encouraging innovation are addressed. The paper provides an overview of each project.
Keywords :
groupware; inference mechanisms; problem solving; innovation encouragement; massively collaborative problem solving exploration; problem solver motivation; reasoning; social consensus; Cognition; Communities; Games; Humans; Problem-solving; Social network services; Technological innovation; collaboration; collective reasoning; problem solving; social computing;
Conference_Titel :
Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4577-1931-8
DOI :
10.1109/PASSAT/SocialCom.2011.248