Sequential Collaboration: The Accuracy of Dependent, Incremental Judgments

Abstract

On the internet, people often collaborate to generate extensive knowledge bases such as Wikipedia for semantic information or OpenStreetMap for geographic information. When contributing to such online projects, individual judgments follow a sequential process in which one contributor creates an entry and other contributors have the possibility to modify, extend, and correct the entry by making incremental changes. We refer to this way of working together as sequential collaboration because it is characterized by dependent judgments that are based on the latest judgment available. Since the process of correcting each other in sequential collaboration has not yet been studied systematically, we compare the accuracy of sequential collaboration and wisdom of crowds, the aggregation of a set of independent judgments. In three experiments with groups of four or six individuals, accuracy for answering general knowledge questions increased within sequences of judgments in which participants had the possibility to correct the judgment of the previous contributor. Moreover, the final individual judgments in sequential collaboration were slightly more accurate than the averaged judgments in wisdom of crowds. This shows that collaboration can benefit from the dependency of individual judgments, thus explaining why large collaborative online projects often provide data of high quality.

Using OpenStreetMap as a Data Source in Psychology and the Social Sciences

“…The change history including the modifications of all OpenStreetMap elements can be traced back to its creation by using the contribution view for data extraction. This comprehensive information on the processes of adding and modifying elements can help to gain insights on how collaboration in online communities works and how information on OpenStreetMap develops (Mayer & Heck, 2021).…”