Mayer Maren, Heck Daniel W
Leibniz-Institut für Wissensmedien (Knowledge Media Research Center), Tübingen, Germany.
Department of Psychology, University of Marburg, Marburg, Germany.
Psychon Bull Rev. 2025 Jun;32(3):1142-1164. doi: 10.3758/s13423-024-02619-9. Epub 2025 Jan 6.
Sequential collaboration describes the incremental process of contributing to online collaborative projects such as Wikipedia and OpenStreetMap. After a first contributor creates an initial entry, subsequent contributors create a sequential chain by deciding whether to adjust or maintain the latest entry which is updated if they decide to make changes. Sequential collaboration has recently been examined as a method for eliciting numerical group judgments. It was shown that in a sequential chain, changes become less frequent and smaller, while judgments become more accurate. Judgments at the end of a sequential chain are similarly accurate and in some cases even more accurate than aggregated independent judgments (wisdom of crowds). This is at least partly due to sequential collaboration allowing contributors to contribute according to their expertise by selectively adjusting judgments. However, there is no formal theory of sequential collaboration. We developed a computational model that formalizes the cognitive processes underlying sequential collaboration. It allows modeling both sequential collaboration and independent judgments, which are used as a benchmark for the performance of sequential collaboration. The model is based on internal distributions of plausible judgments that contributors use to evaluate the plausibility of presented judgments and to provide new judgments. It incorporates individuals' expertise and tendency to adjust presented judgments as well as item difficulty and the effects of the presented judgment on subsequent judgment formation. The model is consistent with previous empirical findings on change probability, change magnitude, and judgment accuracy incorporating expertise as a driving factor of these effects. Moreover, new predictions for long sequential chains were confirmed by an empirical study. Above and beyond sequential collaboration the model establishes an initial theoretical framework for further research on dependent judgments.
顺序协作描述了为维基百科和开放街道地图等在线协作项目做出贡献的渐进过程。在第一个贡献者创建初始条目后，后续贡献者通过决定是调整还是维持最新条目来创建一个顺序链，如果他们决定进行更改，最新条目就会被更新。顺序协作最近被作为一种引出数值群体判断的方法进行了研究。结果表明，在一个顺序链中，更改变得不那么频繁且幅度更小，而判断变得更加准确。顺序链末尾的判断同样准确，在某些情况下甚至比汇总的独立判断（群体智慧）更准确。这至少部分是由于顺序协作允许贡献者根据自己的专业知识通过有选择地调整判断来做出贡献。然而，目前还没有关于顺序协作的形式化理论。我们开发了一个计算模型，该模型将顺序协作背后的认知过程形式化。它既可以对顺序协作进行建模，也可以对独立判断进行建模，而独立判断被用作顺序协作性能的基准。该模型基于贡献者用于评估所呈现判断的合理性并提供新判断的合理判断的内部分布。它纳入了个体的专业知识、调整所呈现判断的倾向以及项目难度和所呈现判断对后续判断形成的影响。该模型与先前关于变化概率、变化幅度和判断准确性的实证研究结果一致，将专业知识作为这些影响的驱动因素。此外，一项实证研究证实了对长顺序链的新预测。除了顺序协作之外，该模型还为依赖判断的进一步研究建立了一个初始理论框架。
