Expertise Determines the Frequency and Accuracy of Contributions in Sequential Collaboration

Many collaborative online projects such as Wikipedia and OpenStreetMap organize collaboration among their contributors sequentially. In sequential collaboration, one contributor creates an entry which is then consecutively encountered by other contributors who decide whether to adjust or maintain the presented entry. For numeric and geographical judgments, sequential collaboration yields improved judgments over the course of a sequential chain and results in accurate final estimates. We hypothesize that these benefits emerge since contributors adjust entries according to their expertise, implying that judgments of experts have a larger impact compared to those of novices. In three preregistered studies, we measured and manipulated expertise to investigate whether expertise leads to higher change probabilities and larger improvements in judgment accuracy. Moreover, we tested whether expertise results in an increase in accuracy over the course of a sequential chain. As expected, experts adjusted entries more frequently, made larger improvements, and contributed more to the final estimates of sequential chains. Overall, our findings suggest that the high accuracy of sequential collaboration is due to an implict weighting of judgments by expertise.