Modeling and simulating crowdsourcing as a complex biological system: human crowds manifesting collective intelligence on the internet.

Abstract

Crowdsourcing, a real-life instance of human collective intelligence, is a phenomenon that changes the way organizations use the Internet to collect ideas, solve complex cognitive problems, and build high-quality repositories (e.g., Wikipedia) by self-organizing agents around data and knowledge. Many recent studies have highlighted the factors and the small sets of parameters that play a role when a large crowd interacts with an organization. However, no comprehensive simulation has yet been developed to incorporate all these parameters, investigate Artificial Life phenomena such as emergence and self-organization and potentially generate predictive power. Based on a presentation at ALIFE XII, this paper describes the development of a simulator for human crowds performing collective problem solving in a Crowdsourcing scenario. It introduces the mechanics of a multi-agent system (MAS) by building on insights from empirical science in several disciplines. The simulator allows running sensitivity analyses of multiple parameters as well as simulation of intractable interactions of complex networks of irrational agents. In addition, the paper provides a review of Crowdsourcing and human collective intelligence literature structured from an Alife point-of-view.