Sequential Collaboration: The Accuracy of Dependent, Incremental Judgments

Online collaborative projects in which users contribute to extensive knowledge bases such as Wikipedia or OpenStreetMap have become increasingly popular while yielding highly accurate information. Collaboration in such projects is organized sequentially with one contributor creating an entry and the following contributors deciding whether to adjust or to maintain the presented information. We refer to this process as sequential collaboration since individual judgments directly depend on the previous judgment. As sequential collaboration has not yet been examined systematically, we investigate whether dependent, sequential judgments become increasingly more accurate. Moreover, we test whether final sequential judgments are more accurate than the unweighted average of independent judgments from equally large groups. We conducted three studies with groups of four to six contributors who either answered general knowledge questions (Experiments 1 and 2) or located cities on maps (Experiment 3). As expected, individual judgments became more accurate across the course of sequential chains and final estimates were similarly accurate as unweighted averaging of independent judgments. These results show that sequential collaboration profits from dependent, incremental judgments, thereby shedding light on the contribution process underlying large-scale online collaborative projects.