CHS: Small: Collaborative Research: Understanding and Improving Implicit Coordination in Peer Production Networks

点击翻译按钮获取中文摘要

This research will apply knowledge about coordination mechanisms that was gained in biology to better understand and to improve coordination in peer production using information and communication technologies. Peer production has emerged as an important economic force. It produces knowledge infrastructure that underlies many aspects of educational and research institutions. Thus, ways of furthering peer production may have broad impacts on the infrastructure that supports invention, and in turn on the economy. More generally, collaborative editing is used in most industries that manage knowledge, and so progress on this front may have impacts in corporations, partnerships, non-profits, and government institutions. The research will not only seek an increased understanding of peer production but also will build tools and interfaces that seek to improve the collective creativity of online communities. Models, data sets, and accompanying simulations will be built for two purposes: as explicit forms of knowledge for reuse by other researchers, and as materials for education. The investigation will proceed through a set of observational studies and experiments, to explore how coordination mechanisms that have been discovered through studies in the field of biology may provide useful insights into human behavior. Recent studies have indicated that coordination in open source and peer production networks can be explained at least in part by stigmergy, a process discovered by biologists in which the traces of work become conditions or signals that generate more work. Peer production is described as being stigmergic because coordination often happens not through explicit planning conversations, but through interactions triggered by previous interactions, all centered on the primary technical artifact, some form of text or source code. The work will proceed in three phases: (1) performing studies that examine how interaction rates affect productivity in peer production networks such as Wikipedia; (2) applying dynamic models from mathematical biology to peer production, first through an observational study and then through an experiment, and (3) testing alternative interfaces for peer production and applying the findings to other collaborative editing environments.