  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.
