Models of crowdsourcing and human computation often assume that individuals independently carry out small, modular tasks. However, while these models have successfully shown how crowds can accomplish significant objectives, they can inadvertently advance a less than human view of crowd workers and fail to capture the unique human capacity for complex collaborative work. We present a model centered on interdependencies -- a phenomenon well understood to be at the core of collaboration -- that allows one to formally reason about diverse challenges to complex collaboration. Our model represents tasks as an interdependent collection of subtasks, formalized as a task graph. We use it to explain challenges to scaling complex collaborative work, underscore the importance of expert workers, reveal critical factors for learning on the job, and explore the relationship between coordination intensity and occupational wages. Using data from O*NET and the Bureau of Labor Statistics, we introduce an index of occupational coordination intensity to validate our theoretical predictions. We present preliminary evidence that occupations with greater coordination intensity are less exposed to displacement by AI, and discuss opportunities for models that emphasize the collaborative capacities of human workers, bridge models of crowd work and traditional work, and promote AI in roles augmenting human collaboration.

翻译：众包和人机计算的模型常假设个体独立完成小型、模块化任务。然而，尽管这些模型已成功展示众包如何实现重大目标，却可能无意中推进一种非人性化的众包工人观，且未能捕捉人类进行复杂协作工作的独特能力。我们提出一个以相互依赖为核心的现象模型——这一现象被公认为协作的本质——使得能够从形式化角度推理复杂协作中的多元挑战。该模型将任务表示为相互依赖的子任务集合，形式化为任务图。我们利用它来解释大规模复杂协作工作面临的挑战，强调专家工人的重要性，揭示在职学习的关键因素，并探索协调强度与职业工资之间的关系。基于O*NET和劳工统计局的数据，我们引入职业协调强度指数以验证理论预测。初步证据表明，协调强度更高的职业受人工智能替代的风险更低，并讨论以下机遇：强调人类工人协作能力的模型、连接众包工作与传统工作的桥梁，以及推动人工智能承担增强人类协作的角色。