2024/12/11 by Maren Mayer, Daniel W. Heck, Joachim Kimmerle · 1 voice
Computer Science · Decision Sciences · #Expert finding and Q&A systems #Scientific Computing and Data Management #Semantic Web and Ontologies
paper · doi:10.1016/j.chb.2024.108527
openalex publication_date 2024/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/02/03
Many online collaborative projects such as Wikipedia and OpenStreetMap organize collaboration among their contributors sequentially. When engaging in sequential collaboration, one contributor creates an initial entry which is then consecutively adjusted or maintained by the following contributors. Thereby, only the latest version of this entry is presented to subsequent contributors. Sequential collaboration was recently examined as a method for aggregating numerical judgments compared to averaging independently provided judgments (i.e., wisdom of crowds). Sequential collaboration was shown to yield increasingly accurate judgments that result in estimates that are at least as accurate as those obtained from aggregating independent judgments. However, sequential collaboration differs from simply aggregating independent judgments in the sequential nature of the process of providing judgments and in the possibility of contributors to opt out of providing a judgment by maintaining it. How these different features contribute to the accuracy of provided judgments is still unknown. In two experiments, we found that the most accurate judgments were provided by participants who engaged in standard sequential collaboration (with an opt-out option), whereas participants who performed sequential collaboration without opt-out gave less accurate judgments; and independent judgments were least accurate. Thus, both the sequential-collaboration process per se as well as the possibility to opt out and not provide a judgment contribute to the accuracy of contributions. These two features come together in a typical sequential-collaboration paradigm. Allowing contributors to use sequential collaboration in collaborative online projects or at least implement some features of sequential collaboration can be beneficial for the resulting entries and information. • Extend a computational cognitive model of sequential collaboration. • Disentangle the sequential judgment process and opting out of providing a judgment. • Derive new predictions about these influences in sequential collaboration. • Model predictions are confirmed empirically. • The sequential judgment process and opting out both have beneficial effects.