Contributors in online collaborative projects, such as Wikipedia, often engage in sequential collaboration to work together. After the initial creation of an entry, contributors form a sequential chain by adjusting and maintaining incremental versions of an entry. Recent research has tested sequential collaboration for the aggregation of numerical judgments showing that judgments become increasingly accurate along the sequential chain. Final estimates even had similar accuracy as aggregated, independent judgments (wisdom of crowds). Sequential collaboration profits from an implicit weighting of judgments by expertise since contributors adjust judgments they can improve but maintain judgments they cannot improve. Since a theory of sequential collaboration is currently lacking, we introduce a computational cognitive model for both sequential collaboration and independent individual judgments. The model considers individuals’ expertise as a predictor for judgment accuracy. Moreover, we consider the presented entry of a previous contributor as both a potential sources of anchoring bias and a potential frame of reference reducing the dispersion of individuals’ plausible judgments. Simulations show that the proposed computational model can describe patterns observed in previous empirical studies. Additionally, a new empirical investigation involving long sequential chains supports for a novel prediction of the model. Overall, the computational model does not only enhance our understanding of the mechanisms underlying sequential collaboration, but also provides an initial theoretical framework for research regarding sequential collaboration.