Crowdsourcing, Open Innovation and Collective Intelligence in the scientific method: a research agenda and operational framework

Abstract

The lonely researcher trying to crack a problem in her office still plays an important role in fundamental research. However, a vast exchange, often with participants from different fields is taking place in modern research activities and projects. In the "Research Value Chain" (a simplified depiction of the Scientific Method as a process used for the analyses in this paper), interactions between researchers and other individuals (intentional or not) within or outside their respective institutions can be regarded as occurrences of Collective Intelligence. "Crowdsourcing" (Howe 2006) is a special case of such Collective Intelligence. It leverages the wisdom of crowds (Surowiecki 2004) and is already changing the way groups of people produce knowledge, generate ideas and make them actionable. A very famous example of a Crowdsourcing outcome is the distributed encyclopedia "Wikipedia". Published research agendas are asking how techniques addressing "the crowd" can be applied to non-profit environments, namely universities, and fundamental research in general. This paper discusses how the non-profit "Research Value Chain" can potentially benefit from Crowdsourcing. Further, a research agenda is proposed that investigates a) the applicability of Crowdsourcing to fundamental science and b) the impact of distributed agent principles from Artificial Intelligence research on the robustness of Crowdsourcing. Insights and methods from different research fields will be combined, such as complex networks, spatially embedded interacting agents or swarms and dynamic networks. Although the ideas in this paper essentially outline a research agenda, preliminary data from two pilot studies show that nonscientists can support scientific projects with high quality contributions. Intrinsic motivators (such as "fun") are present, which suggests individuals are not (only) contributing to such projects with a view to large monetary rewards